Time-to-Solution and Energy-to-Solution: A Comparison between ARM and Xeon

Padoin, Edson Luiz; Oliveira, Daniel; Velho, Pedro Isaacsson; Navaux, Philippe O. A.

doi:10.1109/wamca.2012.10

Cited by 21 publications

(17 citation statements)

References 9 publications

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“…Padoin et al [32] compared an ARM Cortex-A9 1.0 GHz dual-core processor from Texas Instruments to two multiprocessors: one composed of two quad-core 2.4 GHz Intel Xeon E5 processors and the other composed of four 2.0 GHz 8-core Xeon X7 processors. They analyzed different metrics such as time-to-solution, peak power and energy-to-solution using 6 benchmarks from the NAS Parallel Benchmarks (NPB).…”

Section: Related Workmentioning

confidence: 99%

On the energy efficiency and performance of irregular application executions on multicore, NUMA and manycore platforms

Francesquini

Castro

Penna

et al. 2015

Journal of Parallel and Distributed Computing

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h i g h l i g h t s• Programming for a manycore is challenging.• Limited memory and NoC are among the most important constraints of manycores. • For CPU-bound and mixed workloads, MPPA-256 achieves better performance than Xeon. • MPPA-256 consumes up to 13× less energy than embedded and general-purpose multicores. a b s t r a c t Until the last decade, performance of HPC architectures has been almost exclusively quantified by their processing power. However, energy efficiency is being recently considered as important as raw performance and has become a critical aspect to the development of scalable systems. These strict energy constraints guided the development of a new class of so-called light-weight manycore processors. This study evaluates the computing and energy performance of two well-known irregular NP-hard problems -the Traveling-Salesman Problem (TSP) and K-Means clustering -and a numerical seismic wave propagation simulation kernel -Ondes3D -on multicore, NUMA, and manycore platforms. First, we concentrate on the nontrivial task of adapting these applications to a manycore, specifically the novel MPPA-256 manycore processor. Then, we analyze their performance and energy consumption on those different machines. Our results show that applications able to fully use the resources of a manycore can have better performance and may consume from 3.8× to 13× less energy when compared to low-power and general-purpose multicore processors, respectively.

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Section: Related Workmentioning

confidence: 99%

On the energy efficiency and performance of irregular application executions on multicore, NUMA and manycore platforms

Francesquini

Castro

Penna

et al. 2015

Journal of Parallel and Distributed Computing

Self Cite

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“…They modeled the degree of CPU core, memory, and I/O and estimated the clock frequency to achieve energy-efficient performance without compromising execution time. Edson et al analyzed the time-to-solution and energy-to-solution comparison of ARM and Intel Xeon and found that Xeon has still a better tradeoff from user's point-of-view [22]. They analyzed measurements on ARM Cortex-A8, Cortex-A9, and Intel Atom and SandyBridge using mobile, desktop, and server workloads.…”

Section: Related Studiesmentioning

confidence: 99%

Evaluating ARM HPC clusters for scientific workloads

Maqbool

Fox

2015

Concurrency and Computation

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The power consumption of modern high-performance computing (HPC) systems that are built using power hungry commodity servers is one of the major hurdles for achieving Exascale computation. Several efforts have been made by the HPC community to encourage the use of low-powered system-on-chip (SoC) embedded processors in large-scale HPC systems. These initiatives have successfully demonstrated the use of ARM SoCs in HPC systems, but there is still a need to analyze the viability of these systems for HPC platforms before a case can be made for Exascale computation. The major shortcomings of current ARM-HPC evaluations include a lack of detailed insights about performance levels on distributed multicore systems and performance levels for benchmarking in large-scale applications running on HPC. In this paper, we present a comprehensive evaluation of results that covers major aspects of server and HPC benchmarking for ARMbased SoCs. For the experiments, we built an unconventional cluster of ARM Cortex-A9s that is referred to as Weiser and ran single-node benchmarks (STREAM, Sysbench, and PARSEC) and multi-node scientific benchmarks (High-performance Linpack (HPL), NASA Advanced Supercomputing (NAS) Parallel Benchmark, and Gadget-2) in order to provide a baseline for performance limitations of the system. Based on the experimental results, we claim that the performance of ARM SoCs depends heavily on the memory bandwidth, network latency, application class, workload type, and support for compiler optimizations. During server-based benchmarking, we observed that when performing memory intensive benchmarks for database transactions, x86 performed 12% better for multithreaded query processing. However, ARM performed four times better for performance to power ratios for a single core and 2.6 times better on four cores. We noticed that emulated double precision floating point in Java resulted in three to four times slower performance as compared with the performance in C for CPU-bound benchmarks. Even though Intel x86 performed slightly better in computation-oriented applications, ARM showed better scalability in I/O bound applications for shared memory benchmarks. We incorporated the support for ARM in the MPJ-Express runtime and performed comparative analysis of two widely used message passing libraries. We obtained similar results for network bandwidth, large-scale application scaling, floating-point performance, and energy-efficiency for clusters in message passing evaluations (NBP and Gadget 2 with MPJ-Express and MPICH). Our findings can be used to evaluate the energy efficiency of ARM-based clusters for server workloads and scientific workloads and to provide a guideline for building energy-efficient HPC clusters.

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“…Padoin et al [12] compared an ARM Cortex-A9 1.0GHz dual-core processor from Texas Instruments to two multiprocessors: one composed of two quad-core 2.4GHz Intel Xeon E5 processors and the other composed of four 2.0GHz 8-core Xeon X7 processors. They analyzed different metrics such as time-to-solution, peak power and energy-to-solution using 6 benchmarks from the NAS Parallel Benchmarks (NPB).…”

Section: Related Workmentioning

confidence: 99%

“…Research on the power efficiency and numerical kernel performances of both general purpose and low-power processors are quite common [12,14]. However, in this work we are interested in the analysis of the code adaptations needed to go from multicores to manycores.…”

Section: Introductionmentioning

confidence: 99%

Analysis of computing and energy performance of multicore, NUMA, and manycore platforms for an irregular application

Castro

Francesquini

Nguélé

et al. 2013

Proceedings of the 3rd Workshop on Irregular Applications: Architectures and Algorithms

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The exponential growth in processor performance seems to have reached a turning point. Nowadays, energy efficiency is as important as performance and has become a critical aspect to the development of scalable systems. These strict energy constraints paved the way for the development of multi and manycore processors. Research on the performance and the energy efficiency of numerical kernels on multicores are common but studies in the context of manycores are sparse. Unlike these works, in this paper we analyze a well-known irregular NP-complete problem, the Traveling-Salesman Problem (TSP). This study investigates two aspects of the TSP on multicore, NUMA, and manycore processors. First, we concentrate on the nontrivial task of adapting this application to a manycore, specifically the novel MPPA-256 manycore processor. Then, we analyze its performance and energy consumption on different platforms that comprise general-purpose and low-power multicores, a NUMA machine, and the MPPA-256 manycore. Our results show that applications able to fully use the resources of a manycore can have better performance and may consume 9.8 and 13 times less energy when compared to low-power and general-purpose multicore processors, respectively.

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Time-to-Solution and Energy-to-Solution: A Comparison between ARM and Xeon

Cited by 21 publications

References 9 publications

On the energy efficiency and performance of irregular application executions on multicore, NUMA and manycore platforms

On the energy efficiency and performance of irregular application executions on multicore, NUMA and manycore platforms

Evaluating ARM HPC clusters for scientific workloads

Analysis of computing and energy performance of multicore, NUMA, and manycore platforms for an irregular application

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