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 .
There are many computation-centric metrics. FLOPS (FLoating-point Operations Per Second) is a compu-tation-centric metric, measuring the computer system, especially in fields of scientific computing that make heavy use of floating-point calculations @cite_27 . The wide recognition of FLOPS indicates of the maturation of high performance computing. MIPS (Million Instructions Per Second) @cite_24 is another famous computation-centric metric, which is defined as the number of million instructions the processor can execute per second. The main defect of MIPS is that it is architecture-dependent. There are many derivatives of the MIPS, including MWIPS and DMIPS @cite_9 , which use synthetic workloads to evaluate the floating point operations and integer3v operations, respectively. The metric of WSMeter @cite_23 , which is defined as quota-weighted sum of per-job's MIPS ( ), is also a derivative from MIPS, and hence it is architecture-dependent. Unfortunately, modern datacenters are heterogeneous, consisting of different generations of hardware.
@cite_24 @cite_9 @cite_27 @cite_23
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Energy efficiency is one of the most important design requirements in wireless sensor networks (WSN). In this paper, three performed benchmarks compared code size and execution speed of ARM Cortex-M series processors with processors which are commonly used in WSN. In addition, power consumption is taken into account in order to determine energy efficiency of tested processors. Results show that recently introduced ARM Cortex-M series processors are best choice for energy efficient WSN nodes according to all design criteria: power consumption, execution speed and code size. SUMMARY This paper describes the LINPACK Benchmark and some of its variations commonly used to assess the performance of computer systems. Aside from the LINPACK Benchmark suite, the TOP500 and the HPL codes are presented. The latter is frequently used to obtained results for TOP500 submissions. Information is also given on how to interpret the results of the benchmark and how the results fit into the performance evaluation process. Copyright c � 2003 John Wiley & Sons, Ltd. Evaluating the comprehensive performance of a warehouse-scale computer (WSC) has been a long-standing challenge. Traditional load-testing benchmarks become ineffective because they cannot accurately reproduce the behavior of thousands of distinct jobs co-located on a WSC. We therefore evaluate WSCs using actual job behaviors in live production environments. From our experience of developing multiple generations of WSCs, we identify two major challenges of this approach: 1) the lack of a holistic metric that incorporates thousands of jobs and summarizes the performance, and 2) the high costs and risks of conducting an evaluation in a live environment. To address these challenges, we propose WSMeter, a cost-effective methodology to accurately evaluate a WSC's performance using a live production environment. We first define a new metric which accurately represents a WSC's overall performance, taking a wide variety of unevenly distributed jobs into account. We then propose a model to statistically embrace the performance variance inherent in WSCs, to conduct an evaluation with minimal costs and risks. We present three real-world use cases to prove the effectiveness of WSMeter. In the first two cases, WSMeter accurately discerns 7 and 1 performance improvements from WSC upgrades using only 0.9 and 6.6 of the machines in the WSCs, respectively. We emphasize that naive statistical comparisons incur much higher evaluation costs (> 4 times) and sometimes even fail to distinguish subtle differences. The third case shows that a cloud customer hosting two services on our WSC quantifies the performance benefits of software optimization (+9.3 ) with minimal overheads (2.3 of the service capacity).