The past decades witness FLOPS (Floating-point Operations per Second) , as an important computation-centric performance metric , guides computer architecture evolution, bridges hardware and software co-design, and provides quantitative performance number for system optimization . However, for emerging datacenter computing (in short, DC)  workloads, such as internet services or big data analytics, previous work reports on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\% and the average FLOPS efficiency is only 0.1\%, while the average CPU utilization is high as 63\%. These contradicting performance numbers imply that FLOPS is inappropriate for evaluating DC computer systems. To address the above issue, we propose a new computation-centric metric BOPS (Basic OPerations per Second) . In our definition, Basic Operations include all of arithmetic, logical, comparing  and array addressing operations for integer and floating point. BOPS is the average number of BOPs (Basic OPerations) completed each second. To that end, we present a dwarf-based measuring tool to evaluate DC computer systems in terms of our new metrics . On the basis of BOPS, also we propose a new roofline performance model for DC computing . Through the experiments, we demonstrate that our new metrics--BOPS, measuring tool, and new performance modelindeed facilitate DC computer system design and optimization .
The past decades witness FLOPS (Floating-point Operations per Second) as an important computation-centric performance metric . However, for datacenter (in short, DC) computing workloads, such as Internet services or big data analytics, previous work reports that they have extremely low floating point operation intensity, and the average FLOPS efficiency is only 0.1\%, while the average IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform). Furthermore, we reveal that the traditional FLOPS based Roofline performance model is not suitable for modern DC workloads, and gives misleading information for system optimization. These observations imply that FLOPS is inappropriate for evaluating DC computer systems. To address the above issue, we propose a new computation-centric metric BOPs (Basic OPerations ) that measures the efficient work defined by the source code, includes floating-point operations and the arithmetic, logical, comparing , and array addressing parts of integer operations. We define BOPS as the average number of BOPs per second, and propose replacing FLOPS with BOPS to measure DC computer systems . On the basis of BOPS, we propose a new Roofline performance model for DC computing , which we call DC-Roofline model, with which we optimize DC workloads with the improvement varying from 119\% to 325\% .
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, guides computer architecture evolution, bridges hardware and software co-design, and provides quantitative performance number for system optimization
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emerging datacenter computing
datacenter
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computing
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internet
Internet
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on the modern CPU architecture that the average proportion of floating-point instructions only takes 1\%
that they have extremely low floating point operation intensity,
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CPU utilization is high as 63\%. These contradicting performance numbers
IPC is 1.3 (the theoretic IPC is 4 on the Intel Xeon E5600 platform). Furthermore, we reveal that the traditional FLOPS based Roofline performance model is not suitable for modern DC workloads, and gives misleading information for system optimization. These observations
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BOPS
BOPs
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per Second) . In our definition, Basic Operations include all of
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,
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operations for integer and floating point. BOPS is
parts of integer operations. We define BOPS as
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(Basic OPerations) completed each second. To that end, we present a dwarf-based measuring tool to evaluate
per second, and propose replacing FLOPS with BOPS to measure
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in terms of our new metrics
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also
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roofline
Roofline
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. Through the experiments, we demonstrate that our new metrics--BOPS, measuring tool, and new performance modelindeed facilitate DC computer system design and optimization
, which we call DC-Roofline model, with which we optimize DC workloads with the improvement varying from 119\% to 325\%