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• Содержание выпуска • • Mathematical Foundations of Programming • • Software and Hardware for Distributed Systems and Supercomputers • • Artificial Intelligence, Intelligence Systems, Neural Networks •
Software and Hardware for Distributed Systems and Supercomputers
Responsible for the Section: Sergei Abramov, Dr. Phys.-Math.Sci.,
corresponding member of RAS
On the left: assigned number of the paper, submission date, the number
of A5 pages contained in the paper,
and the reference to the full-text PDF
.
Article #
3_2022
67
p.
PDF |
submitted on 17th
Dec 2021 displayed on
website on
22th Feb 2022
Mikhail Borisovich Kuzminsky
Modern server ARM processors for supercomputers:
A64FX and others Initial data of
benchmarks
A comparative analysis of the performance of ARM
server processors used on supercomputers or also aimed at
high-performance computing (HPC) is given. Fujitsu A64FX, Marvell
ThunderX2 and Huawei Kunpeng 920 were selected for the initial
performance analysis. The HPC performance review focuses primarily
on benchmarks and applications for the A64FX, which supports longer
vectors than other ARM processors and has higher peak performance.
The performance of the A64FX is compared against corresponding data
for Intel Xeon Skylake and Cascade Lake, and AMD EPYC with Zen 2 and
3 (Roma and Milan), as well as Nvidia V100 and A100 GPUs.
A short set of potential pros and cons of the A64FX
microarchitecture has been formulated. Comparison of performance
data obtained using different compilers for A64FX. Features have
been formed when A64FX usually gives advantages in performance over
x86-64, and when it concedes to x86-64.
It is clear that the use of A64FX in supercomputers can grow
further. There is an assumption that x86-64 hegemony in HPC will
decrease, in particular, due to the increased use of server ARM
processors. But the analysis of A64FX and new AArch64 processors
expected in the near future showed that A64FX will not necessarily
lead in this process. (In Russian).
Key words: ARM, A64FX, x86-64, high performance computing,
supercomputers, benchmarks. |
article citation |
http://psta.psiras.ru/read/psta2022_1_63-129.pdf |
DOI |
https://doi.org/10.25209/2079-3316-2022-13-1-63-129 |
Article #
4_2022
64
p.
PDF |
ssubmitted on 17th
Dec 2021 displayed on
website on
22th Feb 2022
Mikhail Borisovich Kuzminsky
Modern server ARM processors for supercomputers:
A64FX and others Initial data of
benchmarks
A comparative analysis of the performance of ARM
server processors used on supercomputers or also aimed at
high-performance computing (HPC) is given. Fujitsu A64FX, Marvell
ThunderX2 and Huawei Kunpeng 920 were selected for the initial
performance analysis. The HPC performance review focuses primarily
on benchmarks and applications for the A64FX, which supports longer
vectors than other ARM processors and has higher peak performance.
The performance of the A64FX is compared against corresponding data
for Intel Xeon Skylake and Cascade Lake, and AMD EPYC with Zen 2 and
3 (Roma and Milan), as well as Nvidia V100 and A100 GPUs.
A short set of potential pros and cons of the A64FX
microarchitecture has been formulated. Comparison of performance
data obtained using different compilers for A64FX. Features have
been formed when A64FX usually gives advantages in performance over
x86-64, and when it concedes to x86-64.
It is clear that the use of A64FX in supercomputers can grow
further. There is an assumption that x86-64 hegemony in HPC will
decrease, in particular, due to the increased use of server ARM
processors. But the analysis of A64FX and new AArch64 processors
expected in the near future showed that A64FX will not necessarily
lead in this process. (In Russian).
Key words: ARM, A64FX, x86-64, high performance computing,
supercomputers, benchmarks. |
article citation |
http://psta.psiras.ru/read/psta2022_1_131-194.pdf |
DOI |
https://doi.org/10.25209/2079-3316-2022-13-1-131-194 |
• Содержание выпуска • • Mathematical Foundations of Programming • • Software and Hardware for Distributed Systems and Supercomputers • • Artificial Intelligence, Intelligence Systems, Neural Networks •
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Electronic Scientific Journal "Program Systems: Theory and
Applications" 2010-2017
© Ailamazyan Program System Institute of RAS 2010-2018
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