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'Serial': Bergdahl Says Toxic Battalion Commander Prompted Walk-Off
Stars and Stripes | By Steven Beardsley
A corrosive commander. An unclear mission. An inflated sense of his own abilities.
The factors that pushed Army Sgt. Bowe Bergdahl to walk off his post in eastern Afghanistan in 2009 were the focus of the sixth and latest installment of the podcast "Serial," which is examining Bergdahl's case.
In a previous episode, the Army infantryman said that his disappearance was meant to draw attention to his troubled unit and that his plan was to run more than 20 miles to the nearest American base, Forward Operating Base Sharana. He says in the new episode that a toxic battalion commander was the driving force behind his decision.
Then-Lt. Col. Clint Baker, commander of the 1st Battalion, 501st Infantry Regiment, demoted one soldier in Bergdahl's platoon and punished two others after a journalist published photographs showing them without their uniforms digging a foxhole on a hot day, according to soldiers interviewed in the podcast.
Baker also criticized his men's unshaven appearance after they returned from a five-day mission to guard a crippled military truck on a steep mountainside, those interviewed said.
Bergdahl, in conversations with screenwriter Mark Boal, said Baker was a danger to his platoon. "Serial" draws on more than 25 hours of those conversations, recorded months after the soldier's release in a controversial prisoner exchange in 2014.
"He was unfit for what he was doing," Bergdahl told Boal. "And I wouldn't put it past him to be the type of guy to purposefully put me and my platoon mates in harm's way just because he has a personal grudge against us."
Baker declined to be interviewed on air for "Serial" because of Bergdahl's pending court-martial on charges of desertion and misbehavior before the enemy. If convicted, Bergdahl could face life in prison.
Others interviewed for the podcast suggested Bergdahl's own feelings about the war and his sense of self-importance likely factored into his decision to walk away.
A model soldier with ambitions of joining Special Forces, Bergdahl was also an introvert who seemed to make a point of sticking out from his platoon mates, they said. He refrained from typical soldier banter, smoked a pipe instead of cigarettes and studied Pashto in his spare time.
Disappointed by his unit's nicer-than-expected accommodations at its operating base in Paktika province, Bergdahl removed the mattress from his bunk and slept instead on steel bedsprings, one platoon mate said.
Like others in his unit, Bergdahl was frustrated by the lack of fight in their counterinsurgency mission, which emphasized being friendly to the same people they believed were targeting them.
His frustrations boiled over after the crippled-truck mission.
Sent to a remote district center to pull back a truck that had hit a roadside bomb, his unit ended up losing two of its own trucks to similar bombs. After holding tight for five days while the battalion debated how to get the trucks back, his platoon was ambushed on its way back.
"'We should've gone out there and we should've killed every single one of those guys,'" Josh Korder remembered Bergdahl saying. "He thought we were being cowards because we hid in our trucks and we didn't do that."
Bergdahl told Boal he was most angry about Baker's shaving comment after the firefight and what it suggested about his priorities. The episode with the photographs sent him over the edge, he said.
"I saw that somebody had to do something about it," he said. "Somebody had to do something that called the situation in check."
Global Hot Spots Military Legal Army Afghanistan Bowe Bergdahl Taliban
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Movies |
Molly Ringwald returns in 'The Kissing…
Molly Ringwald returns in 'The Kissing Booth 3' and adds a new role as advocate for meningitis vaccines
Molly Ringwald, seen here in New York City in 2019, returns as the mother in "The Kissing Booth" series and takes on a new role as an advocate for teens to get their second meningitis vaccination at 16. (Photo by Dia Dipasupil, Getty Images)
By Peter Larsen | plarsen@scng.com | Orange County Register
PUBLISHED: August 4, 2021 at 8:39 a.m. | UPDATED: August 4, 2021 at 8:40 a.m.
Molly Ringwald says she was 18 or 19, a few years after her breakout role in "Sixteen Candles," when out of nowhere she started to suffer severe headaches that did not stop.
"I was in Los Angeles, I wasn't in the middle of making a movie at the time, just hanging out with friends," says Ringwald, whose new Netflix movie "Kissing Booth 3" arrives on Wednesday, Aug. 11. "And I'm not a headachy kind of person. I knew something was wrong.
"I just knew in my body it was something that I needed to be concerned about," she says. "So I was rushed to the hospital and I was given a spinal tap, which is not a pleasant experience. But that's how it was diagnosed."
Molly Ringwald is shown as Samantha Baker in the John Hughes film "Sixteen Candles." (Courtesy of Universal Pictures)
From left, Molly Ringwald, Anthony Michael Hall and Emilio Estevez are shown in the 1985 John Hughes film "The Breakfast Club." (Courtesy of Universal Pictures)
Paul Gleason as Richard Vernon appears in a scene from "The Breakfast Club" with Molly Ringwald and Emilio Estevez. (Courtesy of Universal Pictures)
Molly Ringwald as Andie and Jon Cryer as Duckie in the 1986 teen romantic comedy "Pretty In Pink." (Courtesy of Paramount Pictures)
Molly Ringwald as Andie and James Spader as Steff in the 1986 teen romantic comedy "Pretty In Pink." (Courtesy of Paramount Pictures)
Ringwald had viral meningitis, an acute inflammation of the membranes in the brain and spinal cord, and with medical treatment, she soon recovered.
At the time, in the '80s, there were no vaccines for viral meningitis or the even more dangerous and potentially deadly bacterial meningitis.
But today there is, and that's led to Ringwald's off-screen role as a spokeswoman for National Meningitis Association and The 16 Vaccine campaign, urging teens and their parents to make sure they get the second of two bacterial meningitis vaccine shots around the time they make birthday wishes on their own 16 candles.
"As many people know, I am the parent of three children," says Ringwald, 53. "One's a teenager and two preteens. And I very much believe in protecting my kids. They're all vaccinated for everything they can be vaccinated for."
When she was asked to lend her celebrity to the push to make sure teens complete their bacterial vaccinations, her real-life role as a mother, her past portraying teens, and her own brush with the viral form of the illness made her an immediate yes.
The COVID-19 pandemic and the lockdowns that followed caused a large drop off in routine childhood or adolescent vaccinations, leaving many unprotected from vaccine-preventable diseases.
"You're supposed to get the first one as an 11-year-old and then the second one is recommended around 16," she says of the MenACWY vaccine for meningitis. "And it's just fallen way off. For whatever reason, parents are not bringing their teens in to get their second vaccinations.
"So I decided to join up."
A devastating disease
Ringwald's costars in a recent telephone interview included Dr. Oliver Brooks, a Los Angeles pediatrician, and Jonathan DeGuzman, a meningitis survivor
According to one study, 40 percent of parents surveyed said they missed vaccine appointments for their children due to the pandemic, Brooks said, adding that the study is now a few months old, and the number likely higher.
The threat that meningococcal disease is a kind of sepsis caused by what Brooks described as "the worst bacterial infection you can have," describing how a patient can progress from first diagnosis to hospitalization more swiftly than most other diseases.
"Even if you diagnosis it, you can potentially die from it," he said. "So what I say is it's an infection that you want to prevent as opposed to diagnose and treat."
DeGuzman, who lives in National City, was a college student 15 years ago when he suddenly fell severely ill. At the time, vaccines for bacterial meningitis were still new, and he'd not received one.
"My mom actually found me, and she told me I wasn't responding," he says. "She was checking me, thinking what I had looked like chickenpox, but it was all over my body and they were turning purple. And after that, she said my legs, my feet were turning purple and black."
DeGuzman was rushed to the hospital where he spent the next 12 days mostly unconscious. When he awoke, his mother told them she and his father had started to plan his funeral, so gravely ill he'd become. To save his life, all 10 fingers and his feet were amputated.
"I want parents to know that the vaccine is available," he says. "It's really important to have the first dose at 11 or 12, and the second dose, which is a really important one, when you're 16, so they can be protected once they go out into the world."
Teens & mothers
Ringwald's third appearance in the "Kissing Booth" franchise fits neatly into her current advocacy given the ways in which teens often spread meningococcal disease.
"It's a sweet movie, very feel good movie, but yes, meningococcal disease, one of the reasons why it hits young people is because of kissing," she says. "And because of kids being in tight spaces and teen sports and sharing beverages and just all hanging out together as teens want to do.
"So yeah, you could say that there's a connection there between that and what I'm doing now (with The 16 Vaccine)."
As for playing Sara Flynn, mother of teenage boys Noah and Lee in the "Kissing Booth" movies, that bit of fantasy is a breeze compared to the realities of parenthood, Ringwald says with a laugh.
"Being a mother on screen is much easier," she says. "In the 'Kissing Booth' movies, I like to call myself the Teen Buddha. I'm the one that all the kids go to, and I have very wise, sage advice, and they're all really happy to hear what I have to say.
"That's not the case with my own kids at all," Ringwald says. "I have absolutely zero cred with my own kids, which I think is pretty neurotypical of teenagers.
"They just see me as their mom. I feel like that's the way that it should be, right?"
That extends to her kids' interest in their mom's work, Ringwald says.
"They love to watch other movies," she says. "I mean, I can't tell you how many times my (older) daughter's watched 'Mean Girls.' I just watched 'Eighth Grade,' which I loved, with my son yesterday.
"But my movies are a little different. It's always weird, I think, to see your mom in a movie. I mean, I think they're super-proud of me, but they prefer just to think of me as their mom."
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As for her trilogy of teen-movie classics — "Sixteen Candles," "The Breakfast Club," and "Pretty In Pink" — her 18-year-old daughter as seen those, Ringwald says, and the 12-year-old twins will soon.
"That's actually something that we're planning to do any day now," Ringwald says. "I'm getting myself up for it.
"She liked it," she says of her oldest child. "I feel like it was a little bit too early because there were certain aspects of it that she didn't really understand, that weren't age-appropriate.
"But, having said that, she really did get a lot out of it the first time she saw it," Ringwald says. "It was a very emotional evening for both of us."
Peter Larsen | Reporter
Peter Larsen has been the Pop Culture Reporter for the Orange County Register since 2004, finally achieving the neat trick of getting paid to report and write about the stuff he's obsessed about pretty much all his life. He regularly covers the Oscars and the Emmys, goes to Comic-Con and Coachella, reviews pop music, and conducts interviews with authors and actors, musicians and directors, a little of this and a whole lot of that. He grew up, in order, in California, Arkansas, Kentucky and Oregon. Graduated from Lewis and Clark College in Portland, Ore. with degrees in English and Communications. Earned a master's degree at the Medill School of Journalism at Northwestern University. Earned his first newspaper paycheck at the Belleville (Ill.) News-Democrat, fled the Midwest for Los Angeles Daily News and finally ended up at the Orange County Register. He's taught one or two classes a semester in the journalism and mass communications department at Cal State Long Beach since 2006. Somehow managed to get a lovely lady to marry him, and with her have two daughters. And a dog named Buddy. Never forget the dog.
plarsen@scng.com
Follow Peter Larsen @PeterLarsenBSF
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Why Brew Dog, M&S and Lush are leading the way for brands in the pandemic
Cat Turner
By John Regan, CEO, Mymyne
Just before the Covid-19 pandemic spread across the Western World, adtech and marketing professionals were facing their own existential crisis in the wake of Google's cookie killing announcement.
A data-fuelled mass marketing culture, enabled by the internet and geared only towards short-term profits, was coming to an end and the entire industry was left asking itself 'what next?'
Now, despite the pandemic giving us ample opportunity to offer real digital value and engage consumers, many brands have run for the hills. Sadly, it's not the first time we've seen this. When the financial crisis hit, many marketers opted to keep their heads down, daring not to rock the boat or do anything that might attract attention, be it positive or negative.
In times of crisis, the default for many is inertia. We've all heard reports of how the brand safety police have been trying to avoid coronavirus related content, but more notably what we are seeing is……nothing. A recent survey showed that, as of 31st March, 86% of UK marketers are delaying or reviewing campaigns.
Today, as in right now, we're seeing that bored audiences are looking for things to engage with. A Global Web Index report released earlier this month showed:
38% of people are spending more time on social media
39% are spending more time on messaging services
31% are watching more online videos
We're also seeing consumers have an expectation that brands, which have grown wealthy from the money we spent on them in the good times, will support us in our hour of need.
According to Edelman's Covid-19 Brand Trust Report, consumers want to see brands putting people before profit by taking action to help address the societal challenges posed by the pandemic. For 71%, brands that fail to demonstrate this, will lose their trust forever.
And, with 86% of campaigns delayed, brands which do take the risk of doing something different will be heard and remembered. Brands like Brew Dog, M&S, Lush and Joe Wicks (or his personal brand) have won the attention and affinity of the public by taking positive measures to support their customers and employees.
What should brands be doing?
The one safe bet is that more people are currently transacting digitally than ever before and, with the end of social distancing possibly as long as 18 months away, this will continue for the foreseeable future.
Aside from Coronavirus, we also know that the way we identify online audiences is also being disrupted by changing sentiment towards data, restrictive privacy measures from the likes of Google and Facebook and tougher privacy legislation in Europe and the US.
These two factors suggest that the priority for brands must be to adapt quickly to the current situation, and redefine the way they communicate with their audiences going forward, because it will be very different to what's gone before. Right now, the most risky thing a brand can do is to hunker down and hope to return to 'business as usual' in the near future.
One thing the brands mentioned above have in common is their ability to capture the zeitgeist of the pandemic and taken action that acknowledges the strange times we're living in and are adding real value in their own way. People are bored, isolated, stressed.
The healthcare system is stretched to breaking point and parents are reacquainting themselves with Pythagoras theorem and Jane Eyre.
Brands need to explore new ways of identifying their audience. They need to understand what makes them tick and what they have to offer, going beyond profit-driven metrics to deliver on those less tangible but equally important KPIs – engagement, trust and loyalty.
As was the case in 2008, now is not the time to be dovish.
I've long held the belief that clichés become clichés because they're true.
So here's a true cliché – brands that experiment now and adapt to this new and evolving landscape, will emerge from the crisis with an audience which is more engaged, more loyal and more willing to buy as the global economy resets itself.
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Girls! Girls! Girls!
Girls! Girls! Girls! is a 1962 musical comedy film starring Elvis Presley as a penniless fisherman who loves his life on the sea and dreams of owning his own boat.
The film was shot on location in Hawaii.
Elvis plays Ross Carpenter, a fishing guide/sailor who loves his life out on the sea. When he finds out his boss is retiring to Arizona, he has to find a way to buy the Westwind, a boat that he and his father built. He is also caught between two women: insensitive club singer Robin and sweet Laurel.
With the huge success of Blue Hawaii, producer Hal Wallis had decided to promote Elvis as an entertainer rather than the rebel actor, reminiscent of James Dean, as Elvis had been seen in King Creole. Thus another script was set in scenic Hawaii. The working titles for 'Girls! Girls! Girls!' had been 'A Girl In Every Port', 'Welcome Aboard', 'Jambalaya', and 'Gumbo Ya-Ya',which is said to be a Creole expression for 'everybody talks at once'.
Ross Carpenter, Elvis Presley's character in Girls! Girls! Girls! epitomizes the type of role Elvis is most associated with – the handsome, carefree bachelor with a colorful occupation. Ross works as a charter boat pilot who moonlights as a nightclub singer to buy a sailboat that once belonged to his father. Wealthy Laurel Dodge, played by Laurel Goodwin, falls hard for Ross and secretly buys the sailboat for him.
When Ross discovers that Laurel has purchased the boat, his pride is damaged, and he sails off by himself. Laurel quickly follows in a boat piloted by wealthy Wesley Johnson, portrayed by Jeremy Slate, who turns out to be a wolf in tailored clothing. Ross rescues Laurel from Wesley's clutches, realizing that he loves her. Ross asks Laurel to sell the sailboat so that he can feel free to marry her and build a new boat.
G.I. Blues
Elvis #1 Hit Performances
Buy "Girls! Girls! Girls!" on DVD
1960's 1962 Benson Fong Girls! Girls! Girls! Hawaii Jeremy Slate Kin Yung Laurel Laurel Dodge Laurel Goodwin Robert Strauss Robin Robin Gantner Ross Carpenter Sam Stella Stevens Wesley Johnson


Halo and Buster celebrated their 4th birthday this past weekend at Cosmo Dog Park, and I can honestly say they had a blast. While the dogs played, my sister and I set up a table just for their friends. I love the colorful tennis balls from PetSmart, so I decided to go with a vibrant polka dot theme. For a dog party, I think the table setting was fabulous.
Each guest received a goodie bag full of dog treats, a yummy rawhide bone and their very own homemade banana carob pupcake garnished with a wheat bone to enjoy. Just because it's a party doesn't mean we can't think a little healthy right? The pupcakes were a hit! I can see why…they look absolutely delish.
I had such a wonderful time entertaining the birthday pups and our guests. Luckily, the weather held up long enough for us all to enjoy the terrific activities at the park. I did find out that Buster is not much of a swimmer; he enjoys taking in the sites more than jumping into the water…my little wallflower. 🙂 Halo, on the other hand, jumped right in and swam four laps! I never even knew she could swim that far and long. Way to go Halo!
At the end of the party, we had just enough time to capture some family pictures. Overall, probably the best dog party yet.
It looks like it was the perfect pup party!! Love the decorations and the goodies. Shawna is going to be so bummed she missed out on it all!


'Reading the fiction of J.G. Ballard can be an unnerving experience for a modern day, city-dwelling millennial such as myself. The accuracy which so many of Ballard's fictions achieved in hindsight – predicting the architectural, cultural and political trends that dominate today with a sage-like precision – means that it sometimes feels like having been being born into one of his novels. Tower blocks, business parks, retail centres, celebrity obsessions and a constant bombardment of violent imagery have all been normal for quite some time. With novels such as The Atrocity Exhibition (1970), Crash (1973), Concrete Island (1974) and The Unlimited Dream Company (1979), Ballard fed the raw material of 1970s Britain back to itself, merely accelerating its tendencies and obsessions to the fever pitch that is now recognisably the everyday norm of twenty-first century life. Walking around London today, it's not difficult to be consistently reminded of his most prescient novel, High-Rise (1975); a complex and violent prediction of what such structures were capable of doing to the human psyches that resided within them. With Ballard's warning unheeded, London took on the high-rise model of living which still continues today, the skyline being more and more littered with protruding blocks and shards vying for the clouds.
Yet, with Ballard's most prolific period of writing being the 1970s, it's sometimes easy to forget that he was channelling themes and ideas ubiquitous in a certain type of British culture, especially that found on the decade's television. The strangeness, the brutality and the surrealism of Ballard's writing can be found reflected in even the most popular of 1970s programming; for example, catching the right episode of the police series, The Sweeney (1975-1978), can sometimes be a far more Ballardian experience than actually watching genuine adaptations of his work. It was with a recent and constant engagement with such television that I decided to put together a fake trailer for what High-Rise itself could have looked like if the BBC had adapted it then, perhaps for one of their many (much missed) single drama slots such as Play for Today or The Wednesday Play. Ballard had had a brief affair with the BBC in terms of adaptations, including a dramatisation of his short story, Thirteen To Centaurus (1965), for the series Out Of The Unknown, and a short adaptation of his Crash! chapter from The Atrocity Exhibition in 1971, starring Ballard himself opposite actress, Gabrielle Drake. With so many potential slots for Ballard to have worked in, it's a shame to find so little else. But other series were carrying many Ballardian torches even before the typical themes for the writer himself were really lit.
"A new social type was being created by the apartment building, a cool, unemotional personality impervious to the psychological pressures of high-rise life, with minimal needs for privacy, who thrived like an advanced species of machine in the neutral atmosphere. This was the sort of resident who was content to do nothing but sit in his over-priced apartment, watch television with the sound turned down, and wait for his neighbours to make a mistake." (1975).
J.G. Ballard's High-Rise is out now in paperback.


Business School applicants typically have type-A personalities, so managing the application process with zest and precision comes with the territory. One area they can't directly control, however, is the recommendation. Everything works great with someone who is responsive and excited for your b-school journey, but how do you handle someone who is not so eager to help?
When it comes to recommendations, the first thing that any applicant needs to understand is how they work and, therefore, how they should handle them as part of the process. We sum up this analysis with something we call "The Rule of 10%": they count for about 10% of a decision, they should be about 10% of your focus during application season, and you should contribute about 10% of the work that goes into their outcome.
I tell my clients up front that they have to waive their right and that it is not really an option to not do so. Not waiving your right could tell the adcom that you don't trust your recommenders. It could tell them that you are paranoid or overly anxious.
A significant portion of my MBA admissions consulting applicants come to be with little to no extracurricular experience since their undergraduate days. While this is a problem that can be addressed, it can show a lack of proper planning over the long term. A lot of applicants don't think about the impact of their actions on their applicant competitiveness when the graduate from undergrad. To a certain extent, even I was the same way. What I like most about some of my clients is the the way some of them are way ahead of the game we call the MBA application process. Those that have been planning since day 1 to go back to b-school tend to be distinguishing yourself from your peers out of the gate and these habits show when constructing the business school application.
As an MBA admissions consultant, I have worked with a number of clients over the past 5 years. Invariably, the most common question I receive is "What are my chances of getting in?" Answering this question qualitatively requires years of admissions consulting experience with dozens and dozens of clients. Doing the question justice and providing the client with meaning insight requires several hours of examination. Even then, as the applicant matures and the application process begins, the applicant's "chances" can change remarkably.
With this in mind, I when out and created a model that analyzes an applicant's prospects of entering the top 25 U.S. business school programs from an admissions committee view. I developed this tool for Veritas Prep as part of my MBA admissions consulting responsibilities.
"This tool was created to generate an optimal range of business schools for you. By "optimal," we mean that these are the highest-ranked schools to which you have a reasonable chance of getting accepted. While no model is perfect, especially in beta, you can use the range of schools at the bottom of this page as a way to start narrowing your choice of MBA programs."
"Please don't take the model too seriously. Since it only utilizes a subset of data, it cannot be considered a comprehensive tool for all situations and applicant profiles. The admissions process is highly subjective, and no one model can serve as an "oracle" or "magic eight ball.""
Tagged: Business School Selector, Veritas Prep, What are my chances?
What do I consider to be the critical b-school admission factors?


We took the over on the Bucks and Nets game. The Nets really needed this win to stay in the playoffs while the Bucks had clinched their position at the top of the East. I was a bit worried heading in as the Bucks went without Giannis Antetokounmpo for this one, but that turn out to be for not. This team bombs threes and they did exactly that here. The Nets played their hearts out in this one and grabbed the win. D'Angelo Russell led the way with 25 points and 10 assists, followed closely by Caris LeVert's 24.
We've entered the final stretch of the season as next week at this time we'll be watching playoff basketball. A lot of teams are just biding their time until then, but others are still fighting for their spots. While other teams are trying to get the best draft positioning. It's an interesting time, but I really just wish it was over already and the playoffs were here.
A full slate on the schedule tonight after just two games last night. There isn't a lot that I see value in tonight, but I do have two tips for you below.
The Nuggets will sit Nikola Jokic for this one and that's a huge blow to them. He is a huge part of their offense as he works as the main facilitator and makes everyone around him look good. Him being in this game would be a big hit to the Blazers without Jusuf Nurkic, but the lack of Jokic really evens the playing field here.
Portland should have C.J. McCollum back for this one which helps a lot as well. I like the Nuggets guards a lot, but they are really no match for McCollum and Damian Lillard. Portland has done a great job of taking care of business at home this season and I expect them to do that once again here.
The Clippers are in that 6-7-8 fight in the West right now. They hold on to the sixth seed currently, but one false step and they could soon by the 8th seed. They've lost the last two games and really need to get it right before they draw the Warriors in the first round. I am expecting them to show up strong here.
Meanwhile, the Warriors basically have the West locked up. They aren't going to risk too much in this one as they have too much on the line come playoff time. This team has been terrible against the spread this season with a 30-41 record as a favorite and 15-23 as a home favorite. The line on this one is big and considering the circumstances here, the Clippers feel like a great bet.
Our first tip was the San Antonio Spurs -7 over the Washington Wizards on the road. This one was mostly decided in the first half as at the break it was Spurs 77-Wizards 58. This was a true team effort as eight Spurs scored 10 or more points in this one. LaMarcus Aldridge led the way with 24 points in 28 minutes.
We also had the Orlando Magic -8.5 over the Atlanta Hawks and this one was a complete blowout. The score after one was 42-18 and that was about all she wrote. This was just a dominating performance from the Magic and they look like they are going to lock up on of the final playoff spots in the Eastern Conference. This game saw Terrence Ross, Evan Fournier, and Nikola Vucevic all finish the game with 25 points.
Last night also saw the Heat drop a game to the Timberwolves. This was Miami's third straight loss and they are no in real danger of missing the playoffs as they are one game back of the Nets and Pistons. Of course, Detroit and Brooklyn are both 3-7 over their past ten. So maybe no one wants those last couple of playoff spots. They certainly aren't playing like it.
The Milwaukee Bucks have locked up the best record in the NBA and home court advantage throughout the playoffs. Their main goal here should be to stay healthy and get this team to the playoffs. I am not expecting an outstanding defensive performance here. They are going to shoot their threes and play their best, but they aren't going to put the ultimate goal at risk.
Brooklyn, on the other hand, should be playing hard here. Their very playoff lives are at stake and I think this team really wants to make the playoffs. They are going to come out firing in this one.
That said, I'm just not sure I can see them winning this game. The Bucks at 75% are better than the Nets at 100% and the way Brooklyn has played of late I just can't trust them here.
I do, however, really like the over here. Milwaukee has been hitting overs in their last four games as their playoff lives have been decided. The last time these two teams played it was a 131-121 result and I can see the same type of game playing out here.
As long as neither team goes cold from three-point range, I can definitely see them beating this over. It's a little higher than I typically like, but I think it hits here.
The Bucks and Sixers not only went over the posted total, but absolutely blew it away. The total was set at 230.5 and they ended up scoring a combined for 250. The Bucks especially exploded with a 46 point (!) fourth quarter. Giannis Antetokounmpo led the way with 45 points, 13 rebounds, and six assists. Joel Embiid was just as impressive with a 34-13-13 game. I really hope we see these two teams in the playoffs.
The win allowed the Bucks to clinch the best record in the NBA and home court advantage throughout the playoffs. That is huge for them and it's great to see them do it without Eric Bledsoe who was ejected just minutes into the game. Reminder: anyone who thinks the Bucks could lose in the first round is an idiot. The Bucks, Raptors and Sixers are all winning those series easily.
The season ends on the 10th. A lot has been decided, but there are still spots up for grabs as well as draft seeding stuff that can't be ignored. We have to be careful this next week of games when picking. You never really know who cares down the stretch. Sad thing to say, but it's true.
The Spurs are in the eighth seed in the Western Conference right now, but they could still move up. They are just a half game behind the Thunder for the 7th seed and the difference between the Warriors and the Nuggets in the first round is huge. They have to want that 7th seed.
There's no reason to think they can't beat the Wizards here. Bradley Beal should be seeing less minutes and the team doesn't exactly play great defense as it is. The Spurs should be able to score at will here and win this one easily. Honestly, I'm surprised this line isn't double digits.
The Hawks need to stop. They are 5-5 over their past ten and have been playing well. Trae Young is really balling and this team looks to have a bright future, but they need to stop winning.
Why? They can't lose that Mavericks pick. If the Mavericks finish within the top five or hit the lottery jackpot, they keep this year's pick and would then convey their 2020 first-round pick to the Hawks.
The Mavericks are expected to be much better next year with money to spend this offseason so they definitely don't want that to happen. Atlanta still has a two-game lead on the Mavs for the fifth worst record, but they can't keep winning. They need to lock this thing up, they need to lose.
For Orlando, this game is huge. They have a half game lead over the Heat for the 8th seed and they really don't want to lose that. This team hasn't made the playoffs in forever and they want that. I expect them to show and I expect the Hawks to conveniently rest some people at key times.
An unbelievable game in Denver last night. I honestly can't believe our bad luck in this one and this is one of the true head scratching games of the season. The score at the half was 62-53 Nuggets. Almost exactly on pace to cover the over bet and well within reason to think that the Nuggets would cover the spread. Then the third quarter happened and I am honestly baffled.
The Nuggets scored just 10 points. TEN. POINTS. Jamal Murray got injured here and had to leave the game which definitely shook this team, but it still seemed like they could regroup and win this game. Then in the fourth Nikola Jokic, flustered by not getting any foul calls, got involved in a ref show and got hit with a double technical to leave the game and torpedo his team's chances. There are so many factors in these games, a human element, more than any other sport and I think that definitely blew up in our face here.
This type of game is why people think the Nuggets are ripe for the picking in the playoffs and it's hard to argue against that after a game like this. Jokic was shook from a lot of the physical play and without him, his team struggled. There will be a lot more of that in the playoffs and depending on their opponent an upset might be in the cards for round one.
This whole league is dying for the playoffs to start. The regular season ends on the 10th with most teams having around five games left. The Bucks basically have this thing locked up in the East, Toronto is locked in at two, and Philly is locked in at three. The bottom of the East is a four team race for three spots. Just a game and a half separates the Nets, Pistons, Magic and Heat.
The West is basically the same. You have the Warriors on top and the Nuggets at two. Then the Rockets and Blazers jockeying for the 3/4 spots with Houston having a slight edge. Followed by 1.5 game separating the Jazz, Clippers, Spurs and Thunder for seeding.
So there is stuff to play for, but not much and not a whole lot that matters. We're going to have to dig deep this last week plus.
The Miami Heat are fighting for their playoff lives and you know they really want to make it for Dwyane Wade's last season. Miami in the Erik Spoelstra era always seemingly gives the Celtics problems. They win when they shouldn't and they make games far more competitive when that's unexpected. This line is just a bit high to me as I don't think I can see the Heat laying an egg here. This game is important to them.
Boston is a team that I just can't figure out. They are 20-19 as a home favorite against the spread, and 28-31 as a favorite ATS overall. I feel like they are going to let down here once again, even if they win outright.
We took the Blazers +6.5 yesterday and this one was just a three-pointer short of covering. This was just an ugly game. It was 14-11 after the first quarter, gross. This one was really disappointing as Blake Griffin ended up being a late scratch so it definitely felt like this one would go our way. This was just an ugly game, almost 90s style bully basketball, and that type of game is going to favor a team like the Pistons. The Pistons went wild in the third quarter, but I think it was the first quarter that ultimately cost Portland here.
Our win was the Orlando Magic over the Indiana Pacers as the Magic continue their run towards the playoffs. This was a true team effort as nine Magic players scored nine points or more. Aaron Gordon led the way with 23 points and 10 assists.
Last night also featured one of the craziest games of the season as the refs really seemed to pull some shady stuff in the Warriors/Wolves game. There was a game tying three that was waived off for questionable reasons then Stephen Curry hit a shot to tie and taunted the refs a bit. That turned out to be a bad move as a really strange call decided this game on the next play. Check out this Deadspin article for a full recap. This is definitely one of the strangest ends of a game that I've seen this season.
A typical Sunday slate with a bunch of teams without much to play for. That New Orleans/Lakers game without LeBron looks particularly ugly. Just one game that really jumped out at me on the schedule. Let's check that out now.
Denver is rolling right now. They are 8-2 over their past ten games and are currently in a tie with the Warriors for the top seed in the West. That weird game with the Warriors yesterday definitely helped their chances and I expect them to jump on it with a win here today. They are a great home team as Denver is one of the toughest places to play in the league due to the elevation and the distance from the airport to the arena. I love Denver here. They are 21-13 against the spread as a home favorite on the season.
A big reason I love them is I am expecting less than nothing from the Wizards defensively here. Trevor Ariza is their best defender and he's out for this one. That leaves big minutes for Jabari Parker who has no interest in that. Really, this whole team doesn't have much interest in it as they rank 27th in Defensive Rating.
I'm going with the double pick here. The Nuggets to cover the spread and for this one to go over. Washington is allowing an average of 117 per game this year, but I can see Denver hitting 120+ here. Nikola Jokic should have a big game too as the lack of big men for Washington has been a problem all season since Dwight Howard went down.


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text:
\section{Introduction}
With a deluge in the volume and variety of data collected, large-scale web enterprises (such as Yahoo, Facebook, and Google) run big data analytics applications using clusters of commodity servers.
However, it has been recently reported that using clusters is a case of over-provisioning since a majority of analytics jobs do not process huge data sets and that modern scale-up servers are adequate to run analytics jobs~\cite{Scale_up_vs_Scale_out_for_Hadoop}. Additionally, commonly used predictive analytics such as  machine learning algorithms work on filtered datasets that easily fit into memory of modern scale-up servers. Moreover the today's scale-up servers can have CPU, memory and persistent  storage resources in abundance at affordable prices. Thus we envision small cluster of scale-up servers to be the preferable choice of enterprises in near future.

While Phoenix~\cite{Phoenix_Rebirth}, Ostrich~\cite{Tiled_mr} and Polymer~\cite{Polymer} are specifically designed to exploit the potential of a single scale-up server, they don't scale-out to multiple scale-up servers. Apache Spark~\cite{Spark} is getting popular in industry because it enables in-memory processing, scales out to large number of commodity machines and provides a unified framework for batch and stream processing of big data workloads. However it's performance on modern scale-up servers is not fully  understood. A recent study~\cite{Awan} characterizes the performance of Spark based data analytics on a scale-up server but it does not quantify the impact of data volume. Knowing the limitations of modern scale-up servers for Spark based data analytics will help in achieving the future goal of improving the performance of Spark based data analytics on small clusters of scale-up servers. In this paper, we answer the following questions concerning Spark based data analytics running on modern scale-up servers:

\begin{itemize}
\item Do Spark based data analytics benefit from using larger scale-up servers?
\item How severe is the impact of garbage collection on performance of Spark based data analytics?
\item Is file I/O detrimental to Spark based data analytics performance?
\item How does data size affect the micro-architecture performance of Spark based data analytics?
\end{itemize}

To answer the above questions, we use empirical evaluation of Apache Spark based benchmark applications on a modern scale-up server. Our contributions are:

\begin{itemize}

\item We evaluate the impact of data volume on the performance of Spark based data analytics running on a scale-up server.
\item We find the limitations of using Spark on a scale-up server with large volumes of data.
\item We quantify the variations in micro-architectural performance of applications across different data volumes.
\end{itemize}

\section{\textbf{Background}}

Spark is a cluster computing framework that uses Resilient Distributed Datasets (RDDs)~\cite{Spark} which are immutable collections of objects spread across a cluster. Spark programming model is based on higher-order functions that execute user-defined functions in parallel. These higher-order functions are of two types: Transformations and Actions. Transformations are lazy operators that create new RDDs. Actions launch a computation on RDDs and generate an output. When a user runs an action on an RDD, Spark first builds a DAG of stages from the RDD lineage graph. Next, it splits the DAG into stages that contain pipelined transformations with narrow dependencies. Further, it divides each stage into tasks. A task is a combination of data and computation. Tasks are assigned to executor pool threads. Spark executes all tasks within a stage before moving on to the next stage.

Spark runs as a Java process on a Java Virtual Machine(JVM). The JVM has a heap space which is divided into young and old generations.
The young generation keeps short-lived objects while the old generation holds objects with longer lifetimes. The young generation is further divided into eden, survivor1 and survivor2 spaces. When the eden space is full, a minor garbage collection (GC) is run on the eden space and objects that are alive from eden and survivor1 are copied to survivor2. The survivor regions are then swapped. If an object is old enough or survivor2 is full, it is moved to the old space. Finally when the old space is close to full, a full GC operation is invoked.

\section{\textbf{Methodology}}
\subsection{\textbf{Benchmarks}}

Table \ref{Benchmarks} shows the list of benchmarks along with transformations and actions involved. We used Spark versions of the following benchmarks from BigDataBench~\cite{BigDataBench}. Big Data Generator Suite (BDGS), an open source tool was used to generate synthetic datasets based on raw data sets~\cite{BDGS}.

\begin{itemize}
\item \textbf{Word Count (Wc)} counts the number of occurrences of each word in a text file. The input is unstructured Wikipedia Entries.
\item \textbf{Grep (Gp)} searches for the keyword ``The'' in a text file and filters out the lines with matching strings to the output file.  It works on unstructured Wikipedia Entries.
\item \textbf{Sort (So)} ranks records by their key. Its input is a set of samples. Each sample is represented as a numerical d-dimensional vector.

\item \textbf{Naive Bayes (Nb)} uses semi-structured Amazon Movie Reviews data-sets for sentiment classification. We use only the classification part of the benchmark in our experiments.

\textbf{K-Means (Km)} clusters data points into a predefined number of clusters. We run the benchmark for 4 iterations with 8 desired clusters. Its input is structured records, each represented as a numerical d-dimensional vector.

\end{itemize}

\begin{table}[!ht]
\renewcommand{\arraystretch}{1.3}
\caption{Benchmarks.}
\label{Benchmarks}
\centering
\begin{tabular}{p{2cm}p{2cm}|p{3cm}|p{3cm}}
\hline
\multicolumn{2}{l|}{\textbf{Benchmarks}} & \textbf{Transformations} & \textbf{Actions} \\ \hline
\multicolumn{1}{l|}{Micro-benchmarks} & Word count & map, reduceByKey & saveAsTextFile \\\cline{2-4}
\multicolumn{1}{l|}{} & Grep & filter & saveAsTextFile \\ \cline{2-4}
\multicolumn{1}{l|}{} & Sort & map, sortByKey & saveAsTextFile \\ \hline
\multicolumn{1}{l|}{Classification} & Naive Bayes & map & collect \\
\multicolumn{1}{l|}{} &  &  & saveAsTextFile \\ \hline
\multicolumn{1}{l|}{Clustering} & K-Means & map, filter & takeSample \\
\multicolumn{1}{l|}{} &  & mapPartitions & collectAsMap \\
\multicolumn{1}{l|}{} &  & reduceByKey & collect \\ \hline
\end{tabular}
\end{table}

\subsection{\textbf{System Configuration}}

Table~\ref{hardware} shows details about our test machine. Hyper-Threading and Turbo-boost are disabled through BIOS because it is difficult to interpret the micro-architectural data with these features enabled~\cite{HT_disabled}. With Hyper-Threading and Turbo-boost disabled, there are 24 cores in the system operating at the frequency of 2.7 GHz.

\begin{table}[!ht]
\renewcommand{\arraystretch}{1.3}
\caption{Machine Details.}
\label{hardware}
\centering
\begin{tabular}{l|l|p{6.5cm}}
\hline
\textbf{Component} & \multicolumn{2}{c}{\textbf{Details}} \\ \hline
Processor & \multicolumn{2}{l}{Intel Xeon E5-2697 V2, Ivy Bridge micro-architecture} \\ \hline
\multirow{6}{*}{} & Cores & 12 @ 2.7 GHz (Turbo upto 3.5 GHz) \\ \cline{2-3}
 & Threads & 2 per core \\ \cline{2-3}
 & Sockets & 2 \\ \cline{2-3}
 & L1 Cache & 32 KB for instructions and 32 KB for data per core \\ \cline{2-3}
 & L2 Cache & 256 KB per core \\ \cline{2-3}
 & L3 Cache (LLC) & 30 MB per socket \\ \hline
Memory & \multicolumn{2}{l}{2 x 32 GB, 4 DDR3 channels, Max BW 60 GB/s} \\ \hline
OS & \multicolumn{2}{l}{Linux kernel version 2.6.32} \\ \hline
JVM & \multicolumn{2}{l}{Oracle Hotspot JDK version 7u71} \\ \hline
Spark & \multicolumn{2}{l}{Version 1.3.0} \\ \hline
\end{tabular}
\end{table}

Table~\ref{parameters} also lists the parameters of JVM and Spark. For our experiments, we use HotSpot JDK version 7u71 configured in server mode (64 bit). The Hotspot JDK provides several parallel/concurrent GCs out of which we use three combinations: (1) Parallel Scavenge (PS) and Parallel Mark Sweep; (2) Parallel New and Concurrent Mark Sweep; and  (3) G1 young and G1 mixed for young and old generations respectively. The details on each algorithm are available~\cite{HotSpot, G1GC}. The heap size is chosen to avoid getting ``Out of memory'' errors while running the benchmarks. The open file limit in Linux is increased to avoid getting ``Too many files open in the system'' error. The values of Spark internal parameters after tuning are given in Table~\ref{parameters}. Further details on the parameters are available~\cite{spark_config}.

\begin{table}[!ht]
\renewcommand{\arraystretch}{1.3}
\caption{JVM and Spark Parameters for Different Workloads.}
\label{parameters}
\centering
\begin{tabular}{p{1cm}|l|ccccc}
\multicolumn{2}{c|}{} & \multicolumn{1}{c|}{{\bf Wc}} & \multicolumn{1}{c|}{{\bf Gp}} & \multicolumn{1}{c|}{{\bf So}} & \multicolumn{1}{c|}{{\bf Km}} & {\bf Nb} \\ \hline
JVM & Heap Size (GB) & \multicolumn{5}{c}{50} \\ \cline{2-7}
 & Old Generation Garbage Collector & \multicolumn{5}{c}{PS MarkSweep} \\ \cline{2-7}
 & Young Generation Garbage Collector & \multicolumn{5}{c}{PS Scavange} \\ \hline
Spark & spark.storage.memoryFraction & \multicolumn{1}{c|}{0.1} & \multicolumn{1}{c|}{0.1} & \multicolumn{1}{c|}{0.1} & \multicolumn{1}{c|}{0.6} & 0.1 \\ \cline{2-7}
 & spark.shuffle.memoryFraction & \multicolumn{1}{l|}{0.7} & \multicolumn{1}{l|}{0.7} & \multicolumn{1}{l|}{0.7} & \multicolumn{1}{l|}{0.4} & \multicolumn{1}{l}{0.7} \\ \cline{2-7}
 & spark.shuffle.consolidateFiles & \multicolumn{5}{c}{true} \\ \cline{2-7}
 & spark.shuffle.compress & \multicolumn{5}{c}{true} \\ \cline{2-7}
 & spark.shuffle.spill & \multicolumn{5}{c}{true} \\ \cline{2-7}
 & spark.shuffle.spill.compress & \multicolumn{5}{c}{true} \\ \cline{2-7}
 & spark.rdd.compress & \multicolumn{5}{c}{true} \\ \cline{2-7}
 & spark.broadcast.compress & \multicolumn{5}{c}{true} \\ \hline
\end{tabular}
\end{table}

\subsection{\textbf{Measurement Tools and Techniques}}

We configure Spark to collect GC logs which are then parsed to measure time (called real time in GC logs) spent in garbage collection. We rely on the log files generated by Spark to calculate the execution time of the benchmarks. We use Intel Vtune~\cite{Vtune} to perform concurrency analysis and general micro-architecture exploration. For scalability study, each benchmark is run 5 times within a single JVM invocation and the mean values are reported. For concurrency analysis, each benchmark is run 3 times within a single JVM invocation and Vtune measurements are recorded for the last iteration. This experiment is repeated 3 times and the best case in terms of execution time of the application is chosen. The same measurement technique is also applied in general architectural exploration, however the difference is that mean values are reported. Additionally, executor pool threads are bound to the cores before collecting hardware performance counter values.

We use the top-down analysis method proposed by Yasin \cite{Top_Down_Method_for_Counters} to study the micro-architectural performance of the workloads. Super-scalar processors can be conceptually divided into the "front-end" where instructions are fetched and decoded into constituent operations, and the "back-end" where the required computation is performed. A pipeline slot represents the hardware resources needed to process one micro-operation. The top-down method assumes that for each CPU core, there are four pipeline slots available per clock cycle. At issue point each pipeline slot is classified into one of four base categories: Front-end Bound, Back-end Bound, Bad Speculation and Retiring. If a micro-operation is issued in a given cycle, it would eventually either get retired or cancelled. Thus it can be attributed to either Retiring or Bad Speculation respectively. Pipeline slots that could not be filled with micro-operations due to problems in the front-end are attributed to Front-end Bound category whereas pipeline slot where no micro-operations are delivered due to a lack of required resources for accepting more micro-operations in the back-end of the pipeline are identified as Back-end Bound.

\section{Scalability Analysis}

\subsection{Do Spark based data analytics benefit from using scale-up servers?}
We configure spark to run in local-mode and used system configuration parameters of Table~\ref{parameters}. Each benchmark is run with 1, 6, 12, 18 and 24 executor pool threads. The size of input data-set is 6 GB. For each run, we set the CPU affinity of the Spark process to emulate hardware with same number of cores as the worker threads. The cores are allocated from one socket first before switching to the second socket. Figure~\ref{speed-up} plots speed-up as a function of the number of cores. It shows that benchmarks scale linearly up to 4 cores within a socket. Beyond 4 cores, the workloads exhibit sub-linear speed-up, e.g., at 12 cores within a socket, average speed-up across workloads is 7.45. This average speed-up increases up to 8.74, when the Spark process is configured to use all 24 cores in the system. The performance gain of  mere 17.3\% over the 12 cores case suggest that Spark applications do not benefit significantly by using more than 12-core executors.

\subsection{Does performance remain consistent as we enlarge the data size?}

The benchmarks are configured to use 24 executor pool threads in the experiment. Each workload is run with 6 GB, 12 GB and 24 GB of input data and the amount of data processed per second (DPS) is calculated by dividing the input data size by the total execution time. The data sizes are chosen to stress the whole system and evaluate the system's data processing capability. In this regard, DPS is a relevant metric as suggested in by Luo et al.~\cite{DPS}. We also evaluate the sensitivity of DPS to garbage collection schemes but explain it in the next section. Here we only analyse the numbers for Parallel Scavenge garbage collection scheme. By comparing 6 GB and 24 GB cases in Figure~\ref{dps}, we see that K-Means performs the worst as its DPS decreases by 92.94\% and Grep performs the best with a DPS decrease of 11.66\%. Furthermore, we observe that DPS decreases by 49.12\% on average across the workloads, when the data size is increased from 6 GB to 12 GB. However DPS decreases further by only 8.51\% as the data size is increased to 24GB. In the next section, we will explain the reason for poor data scaling behaviour.

\begin{figure*}[!t]
\centering
\subfloat[Benchmarks do not benefit by adding more than 12 cores.]{\includegraphics[scale=0.29]{speedup}
\label{speed-up}}
\subfloat[Data processed per second decreases with increase in data size.]{\includegraphics[scale=0.29]{dps}
\label{dps}}
\caption{Scale-up performance of applications: (a) when the number of cores increases and (b) when input data size increases.}
\label{scale-up performance}
\end{figure*}

\section{Limitations to Scale-up}

\subsection{How severe is the impact of garbage collection?}

Because of the in-memory nature of most Spark computations, garbage collection can become a bottleneck for Spark programs. To test this hypothesis, we analysed garbage collection time of scalability experiments from the previous section. Figure~\ref{gc_6G} plots total execution time and GC time across the number of cores. The proportion of GC time in the execution time increases with the number of cores. At 24 cores, it can be as high as 48\% in K-Means. Word Count and Naive Bayes also show a similar trend. This shows that if the GC time had at least not been increasing, the applications would have scaled better. Therefore we conclude that GC acts as a bottleneck.

To answer the question, ``How does GC affect data processing capability of the system?'', we examine the GC time of benchmarks running at 24 cores. The input data size is increased from 6 GB to 12 GB and then to 24 GB. By comparing 6 GB and 24 GB cases in Figure~\ref{gc_datasize}, we see that GC time does not increase linearly, e.g., when input data is increased by 4x, GC time in K-Means increases by 39.8x. A similar trend is also seen for Word Count and Naive Bayes. This also shows that if GC time had been increasing at most linearly, DPS would not have decreased significantly. For K-Means, DPS decreases by 14x when data size increases by 4x. For similar scenario in Naive Bayes, DPS decreases by 3x and GC time increases by 3x. Hence we can conclude that performance of Spark applications degrades significantly because GC time does not scale linearly with data size.

Finally we answer the question, ``Does the choice of Garbage Collector impact the data processing capability of the system?''. We look at impact of three garbage collectors on DPS of benchmarks at 6 GB, 12 GB and 24 GB of input data size. We study out-of-box (without tuning) behaviour of Concurrent Mark Sweep, G1 and Parallel Scavenge garbage collectors. Figure~\ref{gc_datasize} shows that across all the applications, GC time of Concurrent Mark Sweep is the highest and GC time of Parallel Scavenge is the lowest among the three choices. By comparing the DPS of benchmarks across different garbage collectors, we see that Parallel Scavenge results in 3.69x better performance than Concurrent Mark Sweep and 2.65x better than G1 on average across the workloads at 6 GB. At 24 GB, Parallel Scavenge performs 1.36x better compared to Concurrent Mark Sweep and 1.69x better compared to G1 on average across the workloads.

\begin{figure*}[!t]
\centering
\subfloat[GC overhead is a scalability bottleneck.]{\includegraphics[scale=0.29]{gc_6G}
\label{gc_6G}}
\subfloat[GC time increases at a higher rate with data size.]{\includegraphics[scale=0.29]{gc_datasize}
\label{gc_datasize}}
\caption{Impact of garbage collection on application performance: (a) when the number of cores increases and (b) when input data size increases.}
\label{performance_gc}
\end{figure*}

\subsection{Does file I/O become a bottleneck under large data volumes?}

In order to find the reasons for poor performance of Spark applications under larger data volumes, we studied the thread-level view of benchmarks by performing concurrency analysis in Intel Vtune. We analyse only executor pool threads as they contribute to 95\% of total CPU time during the entire run of the workloads. Figure~\ref{CPU_wait_time} shows that CPU time and wait time of all executor pool threads. CPU time is the time during which the CPU is actively executing the application on all cores. Wait time occurs when software threads are waiting on I/O operations or due to synchronization. The wait time is further divided into idle time and wait on file I/O operations. Both idle time and file I/O time are approximated from the top 5 waiting functions of executor pool threads. The remaining wait time comes under the category of ``other wait time''.

It can be seen that the fraction of wait time increases with increase in input data size, except in Grep where it decreases. By comparing 6 GB and 24 GB case, the data shows that the fraction of CPU time decreases by 54.15\%, 74.98\% and 82.45\% in Word Count, Naive Bayes and Sort respectively; however it increases by 21.73\% in Grep. The breakdown of wait time reveals that contribution of file I/O increases by 5.8x, 17.5x and 25.4x for Word Count, Naive Bayes and Sort respectively but for Grep, it increases only 1.2x.
The CPU time in Figure~\ref{CPU_wait_time} also correlates with CPU utilization numbers in Figure~\ref{CPU_utilization}. On average across the workloads, CPU utilization decreases from 72.34\% to 39.59\% as the data size is increased from 6 GB to 12 GB which decreases further by 5\% in 24 GB case.

\begin{figure*}[!t]
\centering
\subfloat[CPU utilization decreases with data size.]{\includegraphics[scale=0.29]{cpu_utilization}
\label{CPU_utilization}}
\subfloat[Wait time becomes dominant at larger datasets due to significant increase in file I/O operations.]{\includegraphics[scale=0.29]{cpu_wait_time}
\label{CPU_wait_time}}
\caption{Time breakdown under executor pool threads.}
\label{performance_thread_level}
\end{figure*}

\subsection{Is micro-architecture performance invariant to input data size?}

We study the top-down breakdown of pipeline slots in the micro-architecture using the general exploration analysis in Vtune. The benchmarks are configured to use 24 executor pool threads. Each workload is run with 6 GB, 12 GB and 24 GB of input data. Figure~\ref{top_level} shows that benchmarks are back-end bound. On average across the workloads, retiring category accounts for 28.9\% of pipeline slots in 6 GB case and it increases to 31.64\% in the 24 GB case. Back-end bound fraction decreases from 54.2\% to 50.4\% on average across the workloads. K-Means sees the highest increase of 10\% in retiring fraction in 24 GB case in comparison to 6 GB case.

Next, we show the breakdown of memory bound stalls in Figure~\ref{memory_level}. The term DRAM Bound refers to how often the CPU was stalled waiting for data from main memory. L1 Bound shows how often the CPU was stalled without missing in the L1 data cache. L3 Bound shows how often the CPU was stalled waiting for the L3 cache, or contended with a sibling core. Store Bound shows how often the CPU was stalled on store operations. We see that DRAM bound stalls are the primary bottleneck which account for 55.7\% of memory bound stalls on average across the workloads in the 6 GB case. This fraction however decreases to 49.7\% in the 24 GB case. In contrast, the L1 bound fraction increase from 22.5\% in 6 GB case to 30.71\% in 24 GB case on average across the workloads. It means that due to better utilization of L1 cache, the number of simultaneous data read requests to the main memory controller decreases at larger volume of data. Figure~\ref{memory_bandwidth} shows that average memory bandwidth consumption decreases from 20.7 GB/s in the 6 GB case to 13.7 GB/s in the 24 GB case on average across the workloads.

Figure~\ref{core_level} shows the fraction of cycles during execution ports are used. Ports provide the interface between instruction issue stage and the various functional units. By comparing 6 GB and 24 GB cases, we observe that cycles during which no port is used decrease from 51.9\% to 45.8\% on average across the benchmarks and cycles during which 1 or 2 ports are utilized increase from 22.2\% to 28.7\% on average across the workloads.

\begin{figure*}[!ht]
\centering
\subfloat[Retiring rate increases at larger datasets.]{\includegraphics[scale=0.29]{backend_frontend}
\label{top_level}}
\subfloat[L1 Bound stalls increase with data size.]{\includegraphics[scale=0.29]{memorybound_distribution}
\label{memory_level}}
\hfill
\subfloat[Port utilization increases at larger datasets.]{\includegraphics[scale=0.29]{cores_distribution}
\label{core_level}}
\subfloat[Memory traffic decreases with data size.]{\includegraphics[scale=0.29]{memory_bandwidth}
\label{memory_bandwidth}}
\caption{Micro-architecture performance is inconsistent across different data sizes.}
\label{top_down_analysis}
\end{figure*}

\section{Related Work}

Several studies characterize the behaviour of big data workloads and identify the mismatch between the processor and the big data applications~\cite{Clearing_the_clouds,DCBench,BigDataBench,
understanding_in_memory_workloads, Characterising_and_subsetting_big_data_workloads, deep_dive_data_analytics,   MMU_Performance_Scale_out_workloads}. However these studies lack in identifying the limitations of modern scale-up servers for Spark based data analytics. Ousterhout et al.~\cite{Making_sense} have developed blocked time analysis to quantify performance bottlenecks in the Spark framework and have found out that CPU and not I/O operations are often the bottleneck. Our thread level analysis of executor pool threads shows that the conclusion made by Ousterhout et al. is only valid when the the input data-set fits in each node's memory in a scale-out setup. When the size of data set on each node is scaled-up, file I/O becomes the bottleneck again. Wang et al.~\cite{BigDataBench} have shown that the volume of input data has considerable affect on the micro-architecture behaviour of Hadoop based workloads. We make  similar observation about Spark based data analysis workloads.

\section{Conclusions}

We have reported a deep dive analysis of Spark based data analytics on a large scale-up server.
The key insights we have found are as follows:

\begin{itemize}
\item Spark workloads do not benefit significantly from executors with more than 12 cores.
\item The performance of Spark workloads degrades with large volumes of data due to substantial increase in garbage collection and file I/O time.
\item With out any tuning, Parallel Scavenge garbage collection scheme outperforms Concurrent Mark Sweep and G1 garbage collectors for Spark workloads.
\item Spark workloads exhibit improved instruction retirement due to lower L1 cache misses and better utilization of functional units inside cores at large volumes of data.
\item Memory bandwidth utilization of Spark benchmarks decreases with large volumes of data and is 3x lower than the available off-chip bandwidth on our test machine.
\end{itemize}

We conclude that Spark run-time needs node-level optimizations to maximize its potential on modern servers. Garbage collection is detrimental to performance of in-memory big data systems and its impact could be reduced by careful matching of garbage collection scheme to workload. Inconsistencies in micro-architecture performance across the data sizes pose additional challenges for computer architects. Off-chip memory buses should be optimized for in-memory data analytics workloads by scaling back unnecessary bandwidth.

\bibliographystyle{splncs03}

redpajama_set_name: RedPajamaArXiv


text:
Azerbaijan vs Republic of Ireland: Predictions, odds & betting tips
class="article_teaser__2t5do">The Boys in Green have kept fans waiting for a breakthrough in recent matches, and Al Hain-Cole expects more late action against De Biasi's men
Republic of Ireland will be desperate for a morale-boosting victory when they travel to Baku to take on Azerbaijan for Saturday's World Cup qualifier.
The Boys in Green are currently second from bottom in Group A, having lost three and won none of their opening five matches.
Azerbaijan vs Republic of Ireland latest odds
Without a victory in 13 competitive matches in all competitions, Ireland can be backed at 23/20 (2.15) with bet365 to finally have something to cheer about after coming out on top at the Olympic Stadium.
Gianni De Biasi's men are bottom of the group with just one point to their name but Azerbaijan are on offer at 13/5 (3.60) to leapfrog their opponents by emerging victorious here.
The sides shared a 1-1 draw in the reverse encounter in Dublin, and you can get odds of 21/10 (3.10) on them cancelling each other out once again in this one.
Azerbaijan vs Republic of Ireland team news
Anton Krivotsyuk should return for the hosts at left-back after missing the 3-0 home defeat against Portugal due to suspension.
Alan Browne and Jayson Molumby are both suspended after an accumulation of yellow cards, while Ryan Manning has withdrawn from the squad due to injury and been replaced by Liam Scales.
Azerbaijan vs Republic of Ireland preview
Although they have secured only two points from the last three group games, Stephen Kenny's team have provided plenty of drama during that spell.
Having led Portugal until the 89th minute before succumbing to two late Cristiano Ronaldo goals, they subsequently rescued a point against both Azerbaijan and Serbia with goals scored during the last five minutes.
Click Here: Thomas Muller Jersey Sale
In fact, each of their last five competitive fixtures has seen goals scored within the final five minutes of regulation time, meaning anyone tuning in on Saturday should be prepared to watch right up until the end.
Azerbaijan vs Republic of Ireland tips & predictions
In fact odds of 5/6 (1.83) offer decent value on a goal being scored after the 67th minute for Ireland's sixth consecutive competitive game, as well as a third successive home match for Azerbaijan.
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text:
Home > Net Worth > Eckhart Tolle Net Worth (Updated 2022)
Eckhart Tolle Net Worth (Updated 2022)
What is Eckhart Tolle Net Worth?
How Was Eckhart Tolle's Early Life & Education?
How Did Eckhart Tolle Start His Career?
Eckhart Tolle's Personal Life
Eckhart Tolle Books
Eckhart Tolle Social Profile
5 Eckhart Tolle Famous Quotes
4 Lessons from Eckhart Tolle
Ask the Right Question
Find Your Inner Purpose
Take Small Steps
Born February 16, 1948
Birthplace Lünen Germany
Source of Wealth Writer Public Speaker
Last Update 2022
Eckhart Tolle Canadian resident Spiritual Teacher and Author. As of 2022, he has an estimated net worth of $80 million.
He is best known for his book "The Power of Now" and " A New Earth: Awakening to your life's purpose". He is also one of the best-selling authors in this genre.
Tolle is also being called "The most famous spiritual author in the United State" by The New York Times.
He is recognized as the most influential spiritual teacher worldwide. He doesn't associate with any religion. It's his work and love that is appreciated around the world.
His book The Power of Now has sold more than 3 million copies worldwide. Whereas A New Earth: Awakening to Your Life's Purpose has sold more than 5 million.
Would you like to know?
How did he make $80 million?
What is the main source of his wealth?
How much is Eckhart Tolle Worth?
… Let's check out the inspiring success story of Eckhart Tolle
Eckhart's real name is Ulrich Leonard Tölle. He was born in Lünen, a small village in Dortmund in Ruhr Valley, German, on February 16, 1948.
Tolle's childhood was miserable. At a very early age, his parents got separated and he lived in a very painful and alienated manner.
The grown-up with fear and anxiety has after the post war he used to play in the bombarded building.
At the age of 13, he went to Spain to live with his father. His father did not insist him to go to study high school. He decided to study literature, astronomy, and many other languages at home.
He has read many books at the age of 15. He responded very deeply with those books. Few among them were written by German Mystic and Joseph Anton Schneierfranken.
He moved to London at the age of 19 and taught german and Spanish for three years at the London school of language. As he was having trouble with fear and anxiety. He started searching for an answer in his life.
He then decided to pursue literature, philosophy, and psychology by enrolling at the University of London. He then joined for post-graduation after graduating. But later he dropped out.
At the age of 29, he first felt his inner transformation. After a long period of depression one night, he awakens in the middle of sleep suffering from unbearable depression. But that was an enlightening experience, a life-changing situation.
He had a dream that in one of his piles of bookshelf he has a book with the name Eckhart that he has written and one day a random friend called him Eckhart. That is why he changed his name from Ulrich to Eckhart.
After this inner transformation period. He began to work as a counselor and spiritual teacher. For the next five years, students continued to visit him. Later he moved and settled in Vancouver, British Columbia. And that was the time where he encountered his future wife, Kim Eng.
Tolle Published his first book in the year 1997 and printed 3000 copies which were distributed in the local bookstores in Vancouver. Later it was published in 1999 by New World Library publication.
In 2000, Oprah Winfrey recommended this book in his magazine. Later that year it reached The New York Times Best Seller list. Later this book became a success and was published in more than 52 different languages. Which was sold all around the world.
Later he came up with another book "New Earth" that also became a huge success all around the world.
He also formed a company to sell his products named Eckhart teaching. Then he came up with his website that provides various platforms for spreading and teaching spirituality.
In 2009 he appeared with the Dalai Lama and other speakers in the peace meeting in Vancouver.
In 2017 he was seen as a Landscape and nature photographer.
Eckhart Tolle got married to Kim Eng. As for now, there is not enough information about his children and other personal life.
Here is the complete list of Eckhart Tolle Books.
A New Earth: Awakening to Life's Purpose
Stillness Speaks
Oneness With All
"The only place where you can find yourself now. You cannot find yourself in the past or future."
"Seeing Beauty is everything"
"Many times surrender is trying to understand, and become comfortable with not knowing."
"Some changes look negative in starting, but you will soon realize that space is created in your life for something new to happen."
"The More you have the less you own."
Know that you know about Tolle. There are many lessons to take away from him but here are the 4 best Lessons you can learn right now.
It's all about the journey, not the destination. Your life and happiness are in the journey itself and not in the destination. People often get stuck on the future outcome rather than focusing on the current moment.
Information is abundant all around you. But if you want to get the right answer and want to make a correct decision. You have to ask the right questions with yourself and within the details of the information available.
That can be achieved by stillness. Being present in the movement.
Inner purpose is more important than the other purpose. If you can find your inner purpose you can achieve any outer purpose. You need to align yourself with the present movement to achieve your inner purpose.
Small steps lead to greatness. Anything great happened was because of the tiny steps taken in that present moment. If you want to achieve anything in your life. You have to be present at the moment and just take the first small steps that become huge successes in the future.
Now you know about Eckhart Tolle. As you know that he is one of the most influential spiritual teachers around the world. And his major source of income is as an Author, spiritual teaching and as a Speaker.
To know more about his thoughts and teachings you can read his books. Where you can start with "The power on Now". That will guide you to be present in yourself.
This book has been a life-changing book for many peoples and it can change your life too.
You can watch his videos on youtube and follow him on social media for more insights.
Now it's your turn to do something big … Let's Start Now…!


A former USC song girl died at sea when the tall ship she was aboard sank off the North Carolina coast under Sandy's ferocious winds and high waves.
Claudene Christian, 42, was a crew member of the HMS Bounty, a three-masted ship featured in the 1962 film "Mutiny on the Bounty" and two of the "Pirates of the Caribbean" movies. She was one of 16 crew members, 14 of whom were plucked from lifeboats and rescued by the U.S. Coast Guard Monday. Coast Guard officials were still searching for the ship's captain, 63-year-old Robin Walbridge.
Christian, a USC song girl in the 1990s, was not in a lifeboat and was unresponsive when rescue crews found her at 4:30 a.m. Monday, according to Petty Officer 1st Class Brandyn Hill. She was taken by helicopter to a North Carolina hospital, where she was pronounced dead.
By 4:30 a.m. Monday, the post said, Walbridge ordered all hands to abandon the sinking ship.
The Coast Guard said two rescue helicopters arrived about 6:30 a.m. as the ship was sinking in 18-foot seas accompanied by 40-mph winds. Air crews found and rescued the 14 crew members who were in lifeboats in cold weather survival suits and life jackets.
The Facebook page featured sharp debate over whether the crew had responsibly sailed through the storm, with one reader castigating an effort to solicit donations through a relief fund that has been set up.
On her Facebook page, Christian said she had joined the Bounty crew to share the history of the ship, a replica of the 18th century British vessel that was the center of events on which the "Mutiny on the Bounty" was based. She said she was a descendent of Fletcher Christian, the master's mate on the ship who led the mutiny and seized command of the ship from William Bligh during the voyage to Tahiti in 1789.
Christian was the owner of Cheerleader Doll Co.
"The USC Song Girl Family is deeply saddened by the loss of one of our former Song Girls, Claudene Christian," the coaches of the USC Song Girls said in a statement. "As a USC Song Girl, Claudene will always be remembered for her energetic and bubbly personality on an off the field."


text:
The _router_ middleware provides a routing API similar to that of Sinatra and Express.

	function user(app) {
		app.get('/users/(all.:format?)?', function(req, res, next){
			// populates req.format
	        // ex:
	        //   GET /users
	        //   GET /users/all
	        //   GET /users/all.json
	        //   GET /users/all.xml
		});

		app.get('/users/:id.:format?', function(req, res, next){
			// populates req.format
		    // populates req.params.id
		    // ex:
		    //   GET /user/5
		    //   GET /user/5.json
		    //   GET /user/5.xml
		});

		app.put('/user/:id', function(req, res, next){
			// populates req.params.id
			// ex:
			//   PUT /user/2
		});

		app.del('/user/:id/file/*', function(req, res, next){
			// populates req.params.id
			// populates req.params[0]
			// ex:
			//   PUT /user/4/file/javascripts/jquery.js
			//   PUT /user/4/file/stylecss
		});
	}

	function commits(app) {
		// RegExps too!
		app.get(/\/commit\/(\w+)\.\.(\w+)\/?/i, function(req, res, next){
			// populates req.params[0] with first capture group
			// populates req.params[1] with second capture group
			// ex:
			//   GET /commit/kj4k..d3sdf
        });
	}

    connect.createServer(
		connect.router(user),
		connect.router(commits),
	);

Those of you looking for _PUT_ and _DELETE_ support may want to take a look at the _methodOverride_ middleware.

### See Also

  * methodOverride

redpajama_set_name: RedPajamaGithub


Ref rule -> lower ref sends first.
I sell but I do not buy atm. I do not use Paypal.
Please tell me what you have from my wants.
Cards that are *decked* will be very hard to get off of me.
Put "Chibi" in your post, so I know you've read the rules.
1xMission of Capturing the Missing "Pet Tora"
I have all of them except Ino Yamanaka[Usf].
Starter Decks Rares and ST.
for older foils, rares, uncommons, or commons, ask!
Where have i seen this before. lolz.


Life with Lesley: Want some freebies?
I love free stuff. Whether it's samples in the mail or something online. So I thought I'd share some of the great freebies I've ran across lately!
Pop-Tarts Sprinklings -- Join now to get a bunch of fun for you and the kids, including a smattering of samples and a variety of values that'll keep your family happy throughout the year.
FREE booklet, "Renew the Romance in Your Marriage"
Way more ideas to save money at the grocery store? Check out Hillbilly Housewife. They have two awesome weekly plans - $45 emergency menu and $70 low-cost menu.
Have you ever thought about homeschooling? Afraid the cost may be too much for your budget? There are so many different publishers and curriculum to chose from it can become overwhelming. Now is the time to stop worrying! Start with the basics and check out An Old-Fashioned Education. This is a directory of free homeschool curricula, literature and text books catagorized for the use of homeschooling families.
Homeschool Memoirs #25: How Many Loads?


I have always had passion for making things look better, whether it's organizing a room or shooting a short film. Over the years I have found that my favorite area is cinematography. After seeing BBC's Sherlock, an excellently produced TV series, I was greatly inspired and motivated to create something so clever, unique, and beautiful.
I strive to create media with a strong visual appeal but my passion flows from my mission to produce media that uplifts, encourages, and inspires others.


text:
Q: trying to group by generic_m_ai Our current application allows to filter data by NLSSORT=generic_m_ai, I'd like to be able to group by it as well.
Currently testing it with a small table, but keeping the 'case' statement similar to real query
This is how my table is created:
create table alex_collate_test
(aaa varchar(30),
bbb varchar(30), primary key(aaa, bbb))
insert into alex_collate_test values ('hi', 'bye')
insert into alex_collate_test values ('HI', 'bye')
insert into alex_collate_test values ('hi', 'BYE')
insert into alex_collate_test values ('HI', 'BYE')
insert into alex_collate_test values ('next', 'howdy')

Here's the regular group by:
select case when aaa is null or length(aaa) = 0 then '(blank) - '|| case when bbb is null then '' else bbb end  else aaa||' - '|| case when bbb is null then '' else bbb end  end mycolumn
from alex_collate_test
group by case when aaa is null or length(aaa) = 0 then '(blank) - '|| case when bbb is null then '' else bbb end  else aaa||' - '|| case when bbb is null then '' else bbb end  end

and get:
hi - BYE
HI - BYE
hi - bye
next - howdy
HI - bye

I'm following MSSQL's 'Collate' code, and wrapping the columns in 'NLSSORT':
select NLSSORT(case when aaa is null or length(aaa) = 0 then '(blank) - '|| case when bbb is null then '' else bbb end  else aaa||' - '|| case when bbb is null then '' else bbb end  end, 'NLS_SORT=generic_m_ai') mycolumn
from alex_collate_test
group by NLSSORT(case when aaa is null or length(aaa) = 0 then '(blank) - '|| case when bbb is null then '' else bbb end  else aaa||' - '|| case when bbb is null then '' else bbb end  end , 'NLS_SORT=generic_m_ai')

And get this output:
0213021B01EF026701FE
023201FE0266025502130238026401F70267

At first it looks like hex, that I just have to convert to varchar, but if you look closer, it's not hex.  It has '02' - which isn't any normal character like 'hi' or 'bye'.
My expected output, or rather what I want to get is this:
HI - BYE
next - howdy

A: Maybe using MIN() can solve this problem
with qry as (
  select
    case when aaa is null or length(aaa) = 0 then '(blank) - '||
    case when bbb is null then '' else bbb end  else aaa||' - '||
    case when bbb is null then '' else bbb end
    end mycolumn
  from alex_collate_test
)
select min( mycolumn ) mycolumn
from qry
group by NLSSORT( mycolumn, 'NLS_SORT=generic_m_ai')

Here is a link to sql fiddle: http://sqlfiddle.com/#!4/6934c/2

A: NLSSORT gives you the bytes used to perform the sort using that value, and is a RAW value. So it's not directly useful.
But you can use that indirectly to achieve this. There may well be simpler methods, but using an analytic function that gives you a pseudo-column based on the sort order seems to work:
select mygroupcolumn
from (
  select mycolumn, first_value(mycolumn)
    over (partition by NLSSORT(mycolumn, 'NLS_SORT=generic_m_ai')
      order by NLSSORT(mycolumn, 'NLS_SORT=generic_m_ai')) as mygroupcolumn
  from (
    select case when aaa is null or length(aaa) = 0 then '(blank)' else aaa end
      || ' - ' || case when bbb is not null then bbb end as mycolumn
    from alex_collate_test
  )
)
group by mygroupcolumn
order by mygroupcolumn;

MYGROUPCOLUMN
---------------------------------------------------------------
HI - BYE
next - howdy

I've also simplified (IMO) the case structure, but maybe that isn't so straightforward with your real one.
Adding a number column to check the result further, on the assumption you're grouping for a reason, your query gets:
MYCOLUMN                                                          SUM(CCC)
--------------------------------------------------------------- ----------
HI - BYE                                                                 8
HI - bye                                                                 2
hi - BYE                                                                 4
hi - bye                                                                 1
next - howdy                                                            16

And mine, with a tweak to get that ccc value out from the inner queries:
select mygroupcolumn, sum(ccc)
from (
  select mycolumn, ccc, first_value(mycolumn)
    over (partition by NLSSORT(mycolumn, 'NLS_SORT=generic_m_ai')
      order by NLSSORT(mycolumn, 'NLS_SORT=generic_m_ai')) as mygroupcolumn
  from (
    select case when aaa is null or length(aaa) = 0 then '(blank)' else aaa end
      || ' - ' || case when bbb is not null then bbb end as mycolumn, ccc
    from alex_collate_test
  )
)
group by mygroupcolumn
order by mygroupcolumn;

... gets:
MYGROUPCOLUMN                                                     SUM(CCC)
--------------------------------------------------------------- ----------
HI - BYE                                                                15
next - howdy                                                            16

I do seem to have reimplemented MIN(), kind of. @kordirko's answer is simpler; with the same data that also gives:
select min(mycolumn), sum(ccc)
from (
  select case when aaa is null or length(aaa) = 0 then '(blank)' else aaa end
    || ' - ' || case when bbb is not null then bbb end as mycolumn, ccc
  from alex_collate_test
)
group by NLSSORT(mycolumn, 'NLS_SORT=generic_m_ai')
order by NLSSORT(mycolumn, 'NLS_SORT=generic_m_ai');

MIN(MYCOLUMN)                                                     SUM(CCC)
--------------------------------------------------------------- ----------
HI - BYE                                                                15
next - howdy                                                            16

... so I'd probably go with that unless you need other analytic results anyway.

redpajama_set_name: RedPajamaStackExchange


text:
#include "Test.h"
#include "SkMetaData.h"

static void test_ptrs(skiatest::Reporter* reporter) {
    SkRefCnt ref;
    REPORTER_ASSERT(reporter, 1 == ref.getRefCnt());

    {
        SkMetaData md0, md1;
        const char name[] = "refcnt";

        md0.setRefCnt(name, &ref);
        REPORTER_ASSERT(reporter, md0.findRefCnt(name));
        REPORTER_ASSERT(reporter, md0.hasRefCnt(name, &ref));
        REPORTER_ASSERT(reporter, 2 == ref.getRefCnt());

        md1 = md0;
        REPORTER_ASSERT(reporter, md1.findRefCnt(name));
        REPORTER_ASSERT(reporter, md1.hasRefCnt(name, &ref));
        REPORTER_ASSERT(reporter, 3 == ref.getRefCnt());

        REPORTER_ASSERT(reporter, md0.removeRefCnt(name));
        REPORTER_ASSERT(reporter, !md0.findRefCnt(name));
        REPORTER_ASSERT(reporter, !md0.hasRefCnt(name, &ref));
        REPORTER_ASSERT(reporter, 2 == ref.getRefCnt());
    }
    REPORTER_ASSERT(reporter, 1 == ref.getRefCnt());
}

static void TestMetaData(skiatest::Reporter* reporter) {
    SkMetaData  m1;

    REPORTER_ASSERT(reporter, !m1.findS32("int"));
    REPORTER_ASSERT(reporter, !m1.findScalar("scalar"));
    REPORTER_ASSERT(reporter, !m1.findString("hello"));
    REPORTER_ASSERT(reporter, !m1.removeS32("int"));
    REPORTER_ASSERT(reporter, !m1.removeScalar("scalar"));
    REPORTER_ASSERT(reporter, !m1.removeString("hello"));
    REPORTER_ASSERT(reporter, !m1.removeString("true"));
    REPORTER_ASSERT(reporter, !m1.removeString("false"));

    m1.setS32("int", 12345);
    m1.setScalar("scalar", SK_Scalar1 * 42);
    m1.setString("hello", "world");
    m1.setPtr("ptr", &m1);
    m1.setBool("true", true);
    m1.setBool("false", false);

    int32_t     n;
    SkScalar    s;

    m1.setScalar("scalar", SK_Scalar1/2);

    REPORTER_ASSERT(reporter, m1.findS32("int", &n) && n == 12345);
    REPORTER_ASSERT(reporter, m1.findScalar("scalar", &s) && s == SK_Scalar1/2);
    REPORTER_ASSERT(reporter, !strcmp(m1.findString("hello"), "world"));
    REPORTER_ASSERT(reporter, m1.hasBool("true", true));
    REPORTER_ASSERT(reporter, m1.hasBool("false", false));

    SkMetaData::Iter iter(m1);
    const char* name;

    static const struct {
        const char*         fName;
        SkMetaData::Type    fType;
        int                 fCount;
    } gElems[] = {
        { "int",    SkMetaData::kS32_Type,      1 },
        { "scalar", SkMetaData::kScalar_Type,   1 },
        { "ptr",    SkMetaData::kPtr_Type,      1 },
        { "hello",  SkMetaData::kString_Type,   sizeof("world") },
        { "true",   SkMetaData::kBool_Type,     1 },
        { "false",  SkMetaData::kBool_Type,     1 }
    };

    int                 loop = 0;
    int count;
    SkMetaData::Type    t;
    while ((name = iter.next(&t, &count)) != NULL)
    {
        int match = 0;
        for (unsigned i = 0; i < SK_ARRAY_COUNT(gElems); i++)
        {
            if (!strcmp(name, gElems[i].fName))
            {
                match += 1;
                REPORTER_ASSERT(reporter, gElems[i].fType == t);
                REPORTER_ASSERT(reporter, gElems[i].fCount == count);
            }
        }
        REPORTER_ASSERT(reporter, match == 1);
        loop += 1;
    }
    REPORTER_ASSERT(reporter, loop == SK_ARRAY_COUNT(gElems));

    REPORTER_ASSERT(reporter, m1.removeS32("int"));
    REPORTER_ASSERT(reporter, m1.removeScalar("scalar"));
    REPORTER_ASSERT(reporter, m1.removeString("hello"));
    REPORTER_ASSERT(reporter, m1.removeBool("true"));
    REPORTER_ASSERT(reporter, m1.removeBool("false"));

    REPORTER_ASSERT(reporter, !m1.findS32("int"));
    REPORTER_ASSERT(reporter, !m1.findScalar("scalar"));
    REPORTER_ASSERT(reporter, !m1.findString("hello"));
    REPORTER_ASSERT(reporter, !m1.findBool("true"));
    REPORTER_ASSERT(reporter, !m1.findBool("false"));

    test_ptrs(reporter);
}

#include "TestClassDef.h"
DEFINE_TESTCLASS("MetaData", TestMetaDataClass, TestMetaData)


text:
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posted: February 26, 2009 • Advertisements, Ontario
Source: Preserve Grey Highlands
Is wind power really green? Preserve Grey Highlands ad
Source: Preserve Grey Highlands | Advertisements, Ontario
Is wind power really green?
Examine the facts, then decide for yourself.
Denmark is the world's most wind-intensive nation with more than 6,000 turbines generating 19% of its electricity. But consider this:
Not one fossil fuel power plant has been closed
50% more coal-generated electricity is needed to cover wind's failings
Pollution and carbon dioxide emissions rose 36% in 2006 alone
Danish electricity costs are the highest in Europe
So what are the Danes saying about wind power now?
"Windmills are a mistake and economically make no sense." – Neils Gram, Danish Federation of Industries
"Wind turbines do not reduce carbon dioxide emissions." – Fleming Nissen, Head of Denmark's largest energy utility
"For our industry, it has been a terribly expensive disaster." – Aase Madsen, MP, Danish Parliament and Chair of Energy Policy
But don't take our word for it. Check it out for yourself –
http://windfarms.wordpress.com/denmark ("Blowing our Tax Dollars on Wind Farms").
The "Friends" of Wind Power, a group supported by the industry, tell us "every time the wind blows, Ontario burns less coal, oil or gas." The evidence clearly contradicts yet another false claim.
The Wall Street Journal (12/29/08): "Wind generation is the prime example of what can go wrong when the government decides to pick winners. The idea that it can replace coal or natural gas in electrical generation is a fantasy."
Der Spiegel: "Germany's CO emissions haven't been reduced by even a single gram" despite all their wind turbines. In fact, Germany has had to build many more coal and gas-fired plants.
The Fraser Institute: "The government of Ontario is either ignorant of the latest clean-coal technology or, worse, has opted to ignore it in favour of courting 'green' voters."
Wind turbines generate power on average less than 25% of the time, with varying voltages that can lead to brownouts. Energy experts say that, under these circumstances, "wind is more a nuisance than a source of power."
Wind power is expensive, unreliable and inefficient – worldwide, it contributes less than 1% to the reduction of greenhouse gases.
Who can you trust? Those who profit financially from wind turbines at your expense? Or your neighbours who are simply suggesting you examine the evidence for yourself?
Start asking some hard questions about the impact of wind turbines on your family, your business and your community.
A public service message paid for by concerned citizens of Grey Highlands.
For more information, please go to www.gwag.ca
[ Short URL: https://wind-watch.org/alerts/?p=778 ]