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###### Clockwork eliminates the need for specialized hardware using a breakthrough software-based clock synchronization technology developed by the founders at Stanford. This enables sub-microsecond congestion measurement on each data path at scale, without specialized hardware or network support. Now hosts can accurately detect and resolve congestion and contention, transforming GPU cluster with greater reliability, monitoring and fabric acceleration.
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* Maximize GPU efficiency with intelligent traffic balancing.
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 >  >  > Maximize GPU Efficiency: Smarter Fixes for Checkpointing Challenges
# Maximize GPU Efficiency: Smarter Fixes for Checkpointing Challenges
January 17, 2025
In the race to build bigger and more powerful AI systems, organizations are discovering that simply adding more GPUs isn’t the golden ticket to faster results. While GPU clusters with thousands—or even hundreds of thousands—of chips offer unparalleled computational power, they also introduce a formidable challenge: synchronization and checkpointing.
This blog explores why checkpointing is critical for AI training, why it becomes exponentially more challenging as GPU clusters grow, and how Clockwork’s innovative solution transforms this bottleneck into an opportunity for efficiency and cost savings.
### **Why is Checkpointing a Requirement for AI Training?**
Training a modern AI model relies on a process called **backpropagation**, where the model learns by making predictions, calculating errors, and adjusting its parameters. In distributed GPU clusters, where each GPU works on a portion of the data, checkpointing ensures these updates are synchronized across the entire system. Without synchronization, GPUs would train isolated models rather than contribute to a single, unified model.
Checkpointing involves two critical steps:
* **Sharing Gradients**: Each GPU must communicate the changes it has made (gradients) to the rest of the cluster.
* **Synchronizing Updates**: This ensures all GPUs operate with the same version of the model, preventing divergence.
While vital, this process is not without its costs, especially as the scale of GPU clusters grows.
### **The Exponential Growth of the Checkpointing Problem**
The logistical burden of synchronization grows exponentially as GPU clusters scale. Here’s why:
1. **Networking Complexity**
   * With two GPUs, there’s a single communication link.
   * For 20 GPUs, the number of links grows to 190.
   * For 200,000 GPUs, there are almost 20 billion links.
2. **Communication Overhead**
   * Synchronization time increases commensurately with the number of connections, often consuming up to 50% of training time for large clusters.
   * According to AMD, 40% of AI training and inference time is spent on network communications rather than computation.
3. **Impact on Time-to-Failure**
   * In a newly deployed 10,000-GPU cluster, failures in synchronization can lead to a time-to-first-job-failure as low as 157 minutes.
### **Why This Matters**
These inefficiencies mean that simply scaling up hardware leads to diminishing returns. Instead of more computational throughput, organizations face spiraling communication costs and underutilized GPUs.
### **How Clockwork Dramatically Reduces the Checkpointing Bottleneck**
Clockwork addresses the checkpointing challenge with a groundbreaking approach that minimizes communication delays, ensures operational reliability, and optimizes throughput in GPU clusters. Here’s how:
1. **Fine-grained Visibility** Clockwork provides comprehensive, real-time monitoring of GPU fleets and workloads. This unparalleled visibility ensures that communication bottlenecks are identified and mitigated before they escalate, enabling organizations to maintain high GPU utilization throughout training cycles.
2. **Continuous Reliability** Unlike traditional systems prone to failures during synchronization, Clockwork is built for resilience. Even in the face of link flapping or NIC failures—common challenges in GPU clusters—Clockwork ensures continuous operations by rerouting traffic intelligently and maintaining synchronization integrity. This reliability minimizes downtime and costly job re-runs, providing a seamless training experience.
3. **Fabric Acceleration** Clockwork’s advanced communication framework eliminates contention and congestion across the network. By optimizing data flow and improving throughput, it reduces synchronization delays, allowing GPUs to spend more time on computation and less on waiting for updates.
These innovations not only reduce the time spent on checkpointing but also scale seamlessly across workloads, ensuring consistent performance even in the largest GPU clusters.
### **Results: Faster AI Training and Lower Costs**
By addressing the network communication bottlenecks, Clockwork enables organizations to:
* Achieve faster training times.
* Reduce AI capital expenditure by fully utilizing existing GPU capacity with less time wasted on network communication.
* Ensure better reliability, minimizing job failures and costly re-runs.
* Supercharge AI jobs 24/7 in 100% SOFTWARE—use any AI accelerator and commodity NICs and switches at scale.
### **Conclusion: Scaling AI Without Breaking the Bank**
The race to train ever-larger AI models doesn’t have to mean building ever-larger GPU clusters. The smarter approach is to optimize how those GPUs communicate and synchronize. With Clockwork’s cutting-edge solutions, businesses can break free from the checkpointing bottleneck, accelerating their AI initiatives while keeping costs under control.
As *The Economist* aptly noted:
“The contest to build ever-bigger computing clusters cannot continue indefinitely. Computer scientists are looking for cleverer, less resource-intensive ways to train future AI models.”
Clockwork provides exactly that clever solution.
**Ready to transform your AI training efficiency? Visit Clockwork.io to learn more.**
# All posts by Clockwork Team
 > Articles by: Clockwork Team
## 
In the race to build bigger and more powerful AI systems, organizations are discovering that simply adding more GPUs isn’t the golden ticket to faster results. While GPU clusters with thousands—or even hundreds of thousands—of chips offer unparalleled computational power, they also introduce a formidable challenge: synchronization and checkpointing. This blog explores why checkpointing is critical for AI training, why it becomes exponentially more challenging as GPU clusters grow, and how Clockwork’s innovative solution transforms this bottleneck into an opportunity for efficiency and cost savings. Why is Checkpointing a Requirement for AI Training? Training a modern AI model relies on […]
AI at velocity. Made simple in 100% software. Artificial intelligence (AI) is reshaping industries at an unprecedented pace. But with this rapid growth comes immense pressure on the networks that support it. From CPU clouds to sprawling GPU clusters, the infrastructure powering AI workloads is under constant strain. Network bottlenecks, interruptions, and inefficiencies are more than annoyances—they’re costly barriers to innovation How do we move beyond these challenges to build networks that are ready for the AI workloads of today and tomorrow? Let’s dive into what makes a network truly “AI-ready” and how innovations like Clockwork’s software-based solutions are rewriting […]
According to a study by Insight Partners and the MIT Center for Information Systems Research (CISR), businesses in the top quartile of “real-time-ness” significantly outperform their peers: 62% higher revenue growth 97% higher profit margins These organizations also demonstrate improvements in operations: Innovation: 20% better Operational efficiency: 22% better Risk management: 17% better These findings emphasize the importance of becoming a real-time business. Operating in real time isn’t just about speed—it’s about aligning agility with scale, accuracy, and innovation. Notably, these profit margins weren’t achieved by being agile at any cost. Building a real-time business must also account for cost […]
Introduction In the ultra-competitive worlds of crypto arbitrage and high-frequency trading (HFT), every microsecond counts. Your algorithms are in a constant race against competitors to capitalize on fleeting opportunities. A mere microsecond delay can mean the difference between a profitable trade and a missed opportunity—or worse, a loss due to being outpaced by faster traders. The Challenge As a crypto arbitrageur, your strategies involve exploiting price discrepancies across multiple exchanges. Your algorithms gather information from two or more exchanges to execute contracts that ideally settle for a net gain. However, you’re not alone in this endeavor; competing algorithms are simultaneously […]
In today’s digital world, we all expect apps to work quickly and seamlessly, no lag, no waiting—just a smooth, efficient experience. For site reliability engineers (SREs) and data platform engineers, this is more than just a goal; it’s essential. These teams are tasked with making sure applications, databases, and systems perform well, meeting strict Service Level Objectives (SLOs) for latency and uptime. But keeping applications responsive and reliable can be a challenge, especially when those applications run in Kubernetes clusters. Kubernetes has become a go-to solution for containerizing and managing applications, and for good reason—it’s flexible and can handle scaling […]
Precision Timing—A Game Changer for Real-Time Applications In a digital ecosystem where milliseconds matter, precise timestamping isn’t just a nice-to-have; it’s a necessity. Whether you’re managing a high-frequency trading platform, handling critical telecom infrastructure, responding to cybersecurity incidents, or operating latency-sensitive applications, the accuracy of your event timestamps can make or break your performance or limit your ability to consistently meet legal mandates requiring high precision time synchronization. Yet, synchronizing clocks across geographically dispersed locations has long been a costly, complex, and infrastructure-heavy challenge. Let’s dive into why exact time synchronization matters, the limitations of traditional methods, and how Clockwork […]
Let’s face it—cloud services are fantastic. They empower you to build scalable, resilient applications leveraging virtualized compute instances, Kubernetes, and cloud databases. But what happens when those apps start to feel sluggish, unresponsive, or underperforming for no obvious reason? You’ve checked CPU, memory, and disk I/O, and everything seems fine. So what’s the deal?
The hidden culprit could be network throttling by your cloud provider, impacting latency, errors, traffic, and saturation—the four golden signals of system reliability.
In today’s fast-paced world, a seamless digital experience can make or break your business. When customers interact with your applications, they expect instantaneous responses consistently. Any lag or delay can lead to frustration, diminished satisfaction, and ultimately, a loss of business. Accurate, real-time data on network performance isn’t a “nice-to-have”—it’s essential. Let’s dive into why this matters and can now be realized cost-effectively for any real-world scenario utilizing distributed workloads.
Recently, the Clockwork team sponsored both Google Cloud Next ‘24 and Cisco Live in Las Vegas. We had the pleasure of talking to hundreds of people who stopped by our booth to chat about their challenges managing hybrid, multi-cloud networks, and discuss Cloud Deluxe, Clockwork’s congestion control solution. One challenge we heard repeatedly was the need for Quality of Service (QoS) to address performance degradation when primary workloads are disrupted by resource contention from concurrent tasks like backups, observability, and security processes. If this resonates with you, we’d love to learn more in this quick survey. Clockwork’s Bandwidth Slicer addresses […]
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As we wrap up 2023 and reflect on the year’s successes, the Clockwork team would like to thank you for following our progress and take a moment to spotlight our latest solutions.
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