skip to main content
10.1145/3437801.3441591acmconferencesArticle/Chapter ViewAbstractPublication PagesppoppConference Proceedingsconference-collections
poster

Simplifying low-level GPU programming with GAS

Published: 17 February 2021 Publication History

Abstract

Many low-level optimizations for NVIDIA GPU can only be implemented in native hardware assembly (SASS). However, programming in SASS is unproductive and not portable.
To simplify low-level GPU programming, we present GAS (Gpu ASsembly), a PTX-like language that provides a stable instruction set across hardware architectures while giving programmers a low-level control of code execution. We demonstrate that GAS can be used with ease for low-level benchmarking and performance tuning in the context of Tensor Core HGEMM.

References

[1]
NervanaSystems. 2016. Neon. Retrieved Jan 12, 2020 from https://github.com/NervanaSystems/neon
[2]
NVIDIA. 2020. cuBLAS. Retrieved Aug 12, 2020 from https://docs.nvidia.com/cuda/cublas/index.html
[3]
Prashant Singh Rawat, Fabrice Rastello, Aravind Sukumaran-Rajam, Louis-Noël Pouchet, Atanas Rountev, and P Sadayappan. 2018. Register optimizations for stencils on GPUs. In Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP'18). ACM, Vienna, Austria, 168--182.
[4]
Da Yan, Wei Wang, and Xiaowen Chu. 2020. Demystifying Tensor Cores to Optimize Half-Precision Matrix Multiply. In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS'2020). IEEE, New Orleans, LA, USA, 634--643.
[5]
Xiuxia Zhang, Guangming Tan, Shuangbai Xue, Jiajia Li, Keren Zhou, and Mingyu Chen. 2017. Understanding the GPU Microarchitecture to Achieve Bare-Metal Performance Tuning. In Proceedings of the 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP'17). ACM, Austin, TX, USA, 31--43.

Cited By

View all
  • (2024)STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep LearningIEEE Access10.1109/ACCESS.2024.340232612(70581-70599)Online publication date: 2024

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
PPoPP '21: Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
February 2021
507 pages
ISBN:9781450382946
DOI:10.1145/3437801
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 February 2021

Check for updates

Badges

Author Tags

  1. GPU
  2. SASS
  3. compiler

Qualifiers

  • Poster

Conference

PPoPP '21

Acceptance Rates

PPoPP '21 Paper Acceptance Rate 31 of 150 submissions, 21%;
Overall Acceptance Rate 230 of 1,014 submissions, 23%

Upcoming Conference

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)44
  • Downloads (Last 6 weeks)5
Reflects downloads up to 17 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)STuning-DL: Model-Driven Autotuning of Sparse GPU Kernels for Deep LearningIEEE Access10.1109/ACCESS.2024.340232612(70581-70599)Online publication date: 2024

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media