Fast Fourier transform benchmark on X86 Xeon system for multimedia data processing

Park, Young-Soo; Park, Koo-Rack; Kim, Jin-Mook; Jeong, Hwa-Young

doi:10.1007/s11042-015-2843-7

Cited by 6 publications

(1 citation statement)

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“…To our surprise, comprehensive and peer-reviewed benchmarks of FFT implementations across different hardware platforms have not been published extensively. Either only specific hardware is chosen for the benchmark [2,12,26] or only specific FFT implementation variants are tested [9,10]. In addition, many performance benchmarks are tied to domain-specific implementations [14] that either lack comprehensiveness or the ability to map the results obtained to other implementation requirements.…”

Section: Introductionmentioning

confidence: 99%

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

Steinbach

Werner

2017

Lecture Notes in Computer Science

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Fast Fourier Transforms (FFTs) are exploited in a wide variety of fields ranging from computer science to natural sciences and engineering. With the rising data production bandwidths of modern FFT applications, judging best which algorithmic tool to apply, can be vital to any scientific endeavor. As tailored FFT implementations exist for an ever increasing variety of high performance computer hardware, choosing the best performing FFT implementation has strong implications for future hardware purchase decisions, for resources FFTs consume and for possibly decisive financial and time savings ahead of the competition. This paper therefor presents gearshifft, which is an open-source and vendor agnostic benchmark suite to process a wide variety of problem sizes and types with state-of-the-art FFT implementations (fftw, clFFT and cuFFT). gearshifft provides a reproducible, unbiased and fair comparison on a wide variety of hardware to explore which FFT variant is best for a given problem size.

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Section: Introductionmentioning

confidence: 99%

gearshifft – The FFT Benchmark Suite for Heterogeneous Platforms

Steinbach

Werner

2017

Lecture Notes in Computer Science

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High Performance Computing

Steinbach

Werner

2017

Lecture Notes in Computer Science

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In this paper we analyze the major trends and changes in the High-Performance Computing (HPC) market place since the beginning of the journal`Parallel Computing'. The initial success of vector computers in the 1970s was driven by raw performance. The introduction of this type of computer systems started the area of`Supercomputing'. In the 1980s the availability of standard development environments and of application software packages became more important. Next to performance these factors determined the success of MP vector systems, especially at industrial customers. MPPs became successful in the early 1990s due to their better price/performance ratios, which was made possible by the attack of the`killer-micros'. In the lower and medium market segments the MPPs were replaced by microprocessor based symmetrical multiprocessor (SMP) systems in the middle of the 1990s. There success formed the basis for the use of new cluster concepts for very high-end systems. In the last few years only the companies which have entered the emerging markets for massive parallel database servers and ®nancial applications attract enough business volume to be able to support the hardware development for the numerical high-end computing market as well. Success in the traditional¯oating point intensive engineering applications seems to be no longer sucient for survival in the market. Ó 1999 Elsevier Science B.V. All rights reserved. 0167-8191/99/$ -see front matter Ó 1999 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 -8 1 9 1 ( 9 9 ) 0 0 0 6 7 -8 1. Introductioǹ`T

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