Nothing Special   »   [go: up one dir, main page]
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:2304.11921 (cs)
[Submitted on 24 Apr 2023]

Title:Performance Evaluation of a Next-Generation SX-Aurora TSUBASA Vector Supercomputer

Authors:Keichi Takahashi, Soya Fujimoto, Satoru Nagase, Yoko Isobe, Yoichi Shimomura, Ryusuke Egawa, Hiroyuki Takizawa
View PDF
Abstract:Data movement is a key bottleneck in terms of both performance and energy efficiency in modern HPC systems. The NEC SX-series supercomputers have a long history of accelerating memory-intensive HPC applications by providing sufficient memory bandwidth to applications. In this paper, we analyze the performance of a prototype SX-Aurora TSUBASA supercomputer equipped with the brand-new Vector Engine (VE30) processor. VE30 is the first major update to the Vector Engine processor series, and offers significantly improved memory access performance due to its renewed memory subsystem. Moreover, it introduces new instructions and incorporates architectural advancements tailored for accelerating memory-intensive applications. Using standard benchmarks, we demonstrate that VE30 considerably outperforms other processors in both performance and efficiency of memory-intensive applications. We also evaluate VE30 using applications including SPEChpc, and show that VE30 can run real-world applications with high performance. Finally, we discuss performance tuning techniques to obtain maximum performance from VE30.
Comments: This paper has been accepted at ISC 2023
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF)
Cite as: arXiv:2304.11921 [cs.DC]
  (or arXiv:2304.11921v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.2304.11921
Related DOI: https://doi.org/10.1007/978-3-031-32041-5_19

Submission history

From: Keichi Takahashi [view email]
[v1] Mon, 24 Apr 2023 09:01:07 UTC (392 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
view license
Current browse context:
cs.DC
< prev   |   next >
new | recent | 2023-04
Change to browse by:
cs
cs.PF

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)