research-article

Load-Triggered Warp Approximation on GPU

Authors:

Zhenhong Liu,

Daniel Wong,

Nam Sung KimAuthors Info & Claims

ISLPED '18: Proceedings of the International Symposium on Low Power Electronics and Design

Article No.: 26, Pages 1 - 6

https://doi.org/10.1145/3218603.3218626

Published: 23 July 2018 Publication History

Get Access

Abstract

Value similarity of operands across warps have been exploited to improve energy efficiency of GPUs. Prior work, however, incurs significant overheads to check value similarity for every instruction and does not improve performance as it does not reduce the number of executed instructions. This work proposes Lock 'n Load (LnL) which triggers approximate execution of code regions by only checking similarity of values returned from load instructions and fuses multiple approximated warps into a single warp.

References

[1]

M. Samadi, J. Lee, D. A. Jamshidi, A. Hormati, and S. Mahlke. SAGE: Self-Tuning Approximation for Graphics Engines. In IEEE/ACM International Symposium on Microarchitecture (MICRO), 2013.

Digital Library

Google Scholar

[2]

D. Wong, N. S. Kim, and M. Annavaram. Approximating Warps with Intra-warp Operand Value Similarity. In IEEE International Symposium on High Performance Computer Architecture (HPCA), 2016.

Google Scholar

[3]

S. Z. Gilani, N. S. Kim, and M.J. Schulte. Power-efficient Computing for Compute-intensive GPGPU Applications. In IEEE International Symposium on High Performance Computer Architecture (HPCA), 2013.

Digital Library

Google Scholar

[4]

A. Yilmazer, Z. Chen, and D. Kaeli. Scalar Waving: Improving the Efficiency of SIMD Execution on GPUs. In IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2014.

Digital Library

Google Scholar

[5]

S. Lee, K. Kim, G. Koo, H. Jeon, W. W. Ro, and M. Annavaram. Warped-Compression: Enabling Power Efficient GPUs through Register Compression. In IEEE/ACM International Symposium on Computer Architecture (ISCA), 2015.

Digital Library

Google Scholar

[6]

Z. Liu, S. Gilani, M. Annavaram, and N. S. Kim. G-Scalar: Cost-Effective Generalized Scalar Execution Architecture for Power-Efficient GPUs. In IEEE International Symposium on High Performance Computer Architecture (HPCA), 2017.

Google Scholar

[7]

S. Che, M. Boyer, J. Meng, D. Tarjan, J. W. Sheaffer, S.-H. Lee, and K. Skadron. Rodinia: A Benchmark Suite for Heterogeneous Computing. In IEEE International Symposium on Workload Characterization (IISWC), pages 44--54, Oct 2009.

Digital Library

Google Scholar

[8]

J. Leng, T. Hetherington, A. ElTantawy, S. Gilani, N. S. Kim, T. M. Aamodt, and V.J. Reddi. GPUWattch: Enabling Energy Optimizations in GPGPUs. In IEEE/ACM International Symposium on Computer Architecture (ISCA), 2013.

Digital Library

Google Scholar

[9]

H. Jeon, G.S. Ravi, N.S. Kim, and M. Annavaram. GPU Register File Virtualization. In IEEE/ACM International Symposium on Microarchitecture (MICRO), 2015.

Digital Library

Google Scholar

[10]

A. Bakhoda, G. Yuan, W. W. L. Fung, H. Wong, and T. M. Aamodt. Analyzing CUDA Workloads Using a Detailed GPU Simulator. In IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), 2009.

Crossref

Google Scholar

[11]

G. Ziegler. Textures and Surfaces. URL http://on-demand.gputechconf.com/gtc-express/2011/presentations/texture_webinar_aug_2011.pdf.

Google Scholar

[12]

NVIDIA. Fermi Architecture Whitepaper. URL http://www.nvidia.com/content/pdf/fermi_white_papers/nvidia_fermi_compute_architecture_whitepaper.pdf.

Google Scholar

[13]

NVIDIA. CUDA Toolkit 4.0. URL https://developer.nvidia.com/cuda-toolkit-40.

Google Scholar

Cited By

View all

Ho NWong W(2022)Tensorox: Accelerating GPU Applications via Neural Approximation on Unused Tensor CoresIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.309323933:2(429-443)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TPDS.2021.3093239
Ranganath KSuetterlein JManzano JSong SWong Dde Supinski BHall MGamblin T(2021)MAPAProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3480853(1-14)Online publication date: 14-Nov-2021
https://dl.acm.org/doi/10.1145/3458817.3480853
Atoofian E(2020)Approximate Cache in GPGPUsACM Transactions on Embedded Computing Systems10.1145/340790419:5(1-22)Online publication date: 26-Sep-2020
https://dl.acm.org/doi/10.1145/3407904
Show More Cited By

Index Terms

Load-Triggered Warp Approximation on GPU
1. Computer systems organization
  1. Architectures
    1. Parallel architectures
      1. Single instruction, multiple data

Recommendations

Neural acceleration for GPU throughput processors
MICRO-48: Proceedings of the 48th International Symposium on Microarchitecture

Graphics Processing Units (GPUs) can accelerate diverse classes of applications, such as recognition, gaming, data analytics, weather prediction, and multimedia. Many of these applications are amenable to approximate execution. This application ...
Dynamic warp formation: Efficient MIMD control flow on SIMD graphics hardware

Recent advances in graphics processing units (GPUs) have resulted in massively parallel hardware that is easily programmable and widely available in today's desktop and notebook computer systems. GPUs typically use single-instruction, multiple-data (...
Taming warp divergence
CGO '17: Proceedings of the 2017 International Symposium on Code Generation and Optimization

Graphics Processing Units (GPU) are designed to run a large number of threads in parallel. These threads run on Streaming

Multiprocessors (SM) which consist of a few tens of SIMD cores. A kernel is launched on the GPU with an execution

...

Comments

Information & Contributors

Information

Published In

ISLPED '18: Proceedings of the International Symposium on Low Power Electronics and Design

July 2018

327 pages

ISBN:9781450357043

DOI:10.1145/3218603

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 July 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ISLPED '18

Sponsor:

SIGDA

ISLPED '18: International Symposium on Low Power Electronics and Design

July 23 - 25, 2018

WA, Seattle, USA

Acceptance Rates

Overall Acceptance Rate 398 of 1,159 submissions, 34%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
185
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ho NWong W(2022)Tensorox: Accelerating GPU Applications via Neural Approximation on Unused Tensor CoresIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2021.309323933:2(429-443)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TPDS.2021.3093239
Ranganath KSuetterlein JManzano JSong SWong Dde Supinski BHall MGamblin T(2021)MAPAProceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis10.1145/3458817.3480853(1-14)Online publication date: 14-Nov-2021
https://dl.acm.org/doi/10.1145/3458817.3480853
Atoofian E(2020)Approximate Cache in GPGPUsACM Transactions on Embedded Computing Systems10.1145/340790419:5(1-22)Online publication date: 26-Sep-2020
https://dl.acm.org/doi/10.1145/3407904
Peroni DImani MNejatollahi HDutt NRosing T(2020)Data Reuse for Accelerated Approximate WarpsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.298612839:12(4623-4634)Online publication date: Dec-2020
https://doi.org/10.1109/TCAD.2020.2986128
Esfeden HAbdolrashidi ARahman SWong DAbu-Ghazaleh N(2020)BOW: Breathing Operand Windows to Exploit Bypassing in GPUs2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)10.1109/MICRO50266.2020.00084(996-1008)Online publication date: Oct-2020
https://doi.org/10.1109/MICRO50266.2020.00084
Liu ZYazdanbakhsh AWang DEsmaeilzadeh HKim NManne SHunter HAltman E(2019)AxMemoProceedings of the 46th International Symposium on Computer Architecture10.1145/3307650.3322215(685-697)Online publication date: 22-Jun-2019
https://dl.acm.org/doi/10.1145/3307650.3322215
Asghari Esfeden HKhorasani FJeon HWong DAbu-Ghazaleh NBahar IHerlihy MWitchel ELebeck A(2019)CORFProceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3297858.3304026(701-714)Online publication date: 4-Apr-2019
https://dl.acm.org/doi/10.1145/3297858.3304026

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Neural acceleration for GPU throughput processors

Dynamic warp formation: Efficient MIMD control flow on SIMD graphics hardware

Taming warp divergence

Comments

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Neural acceleration for GPU throughput processors

Dynamic warp formation: Efficient MIMD control flow on SIMD graphics hardware

Taming warp divergence

Comments

Information

Published In

Sponsors

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations