skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Panda: A Compiler Framework for Concurrent CPU $+$ GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers

Abstract

We present a new compiler framework for truly heterogeneous 3D stencil computation on GPU clusters. Our framework consists of a simple directive-based programming model and a tightly integrated source-to-source compiler. Annotated with a small number of directives, sequential stencil C codes can be automatically parallelized for large-scale GPU clusters. The most distinctive feature of the compiler is its capability to generate hybrid MPI$+$ CUDA$+$ OpenMP code that uses concurrent CPU$+$ GPU computing to unleash the full potential of powerful GPU clusters. The auto-generated hybrid codes hide the overhead of various data motion by overlapping them with computation. Test results on the Titan supercomputer and the Wilkes cluster show that auto-translated codes can achieve about 90 % of the performance of highly optimized handwritten codes, for both a simple stencil benchmark and a real-world application in cardiac modeling. The user-friendliness and performance of our domain-specific compiler framework allow harnessing the full power of GPU-accelerated supercomputing without painstaking coding effort.

Authors:
ORCiD logo [1];  [2];  [1]
  1. Simula Research Lab., Oslo (Norway); Univ. of Oslo (Norway)
  2. Univ. of California, San Diego, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1525220
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
International Journal of Parallel Programming
Additional Journal Information:
Journal Volume: 45; Journal Issue: 3; Journal ID: ISSN 0885-7458
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Sourouri, Mohammed, Baden, Scott B., and Cai, Xing. Panda: A Compiler Framework for Concurrent CPU $+$ GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers. United States: N. p., 2016. Web. https://doi.org/10.1007/s10766-016-0454-1.
Sourouri, Mohammed, Baden, Scott B., & Cai, Xing. Panda: A Compiler Framework for Concurrent CPU $+$ GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers. United States. https://doi.org/10.1007/s10766-016-0454-1
Sourouri, Mohammed, Baden, Scott B., and Cai, Xing. Wed . "Panda: A Compiler Framework for Concurrent CPU $+$ GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers". United States. https://doi.org/10.1007/s10766-016-0454-1. https://www.osti.gov/servlets/purl/1525220.
@article{osti_1525220,
title = {Panda: A Compiler Framework for Concurrent CPU $+$ GPU Execution of 3D Stencil Computations on GPU-accelerated Supercomputers},
author = {Sourouri, Mohammed and Baden, Scott B. and Cai, Xing},
abstractNote = {We present a new compiler framework for truly heterogeneous 3D stencil computation on GPU clusters. Our framework consists of a simple directive-based programming model and a tightly integrated source-to-source compiler. Annotated with a small number of directives, sequential stencil C codes can be automatically parallelized for large-scale GPU clusters. The most distinctive feature of the compiler is its capability to generate hybrid MPI$+$ CUDA$+$ OpenMP code that uses concurrent CPU$+$ GPU computing to unleash the full potential of powerful GPU clusters. The auto-generated hybrid codes hide the overhead of various data motion by overlapping them with computation. Test results on the Titan supercomputer and the Wilkes cluster show that auto-translated codes can achieve about 90 % of the performance of highly optimized handwritten codes, for both a simple stencil benchmark and a real-world application in cardiac modeling. The user-friendliness and performance of our domain-specific compiler framework allow harnessing the full power of GPU-accelerated supercomputing without painstaking coding effort.},
doi = {10.1007/s10766-016-0454-1},
journal = {International Journal of Parallel Programming},
number = 3,
volume = 45,
place = {United States},
year = {2016},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 4 works
Citation information provided by
Web of Science

Figures / Tables:

Fig. 1 Fig. 1: An architectural view of the Panda source-to-source compiler, which adopts a modular design. Each module may consist of numerous sub-modules, but for brevity, only the most important sub-modules are depicted.

Save / Share:

Works referenced in this record:

An auto-tuning framework for parallel multicore stencil computations
conference, April 2010

  • Kamil, Shoaib; Chan, Cy; Oliker, Leonid
  • 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS)
  • DOI: 10.1109/IPDPS.2010.5470421

High-performance code generation for stencil computations on GPU architectures
conference, January 2012

  • Holewinski, Justin; Pouchet, Louis-Noël; Sadayappan, P.
  • Proceedings of the 26th ACM international conference on Supercomputing - ICS '12
  • DOI: 10.1145/2304576.2304619

Mint: realizing CUDA performance in 3D stencil methods with annotated C
conference, January 2011

  • Unat, Didem; Cai, Xing; Baden, Scott B.
  • Proceedings of the international conference on Supercomputing - ICS '11
  • DOI: 10.1145/1995896.1995932

A Survey of CPU-GPU Heterogeneous Computing Techniques
journal, July 2015

  • Mittal, Sparsh; Vetter, Jeffrey S.
  • ACM Computing Surveys, Vol. 47, Issue 4
  • DOI: 10.1145/2788396

CPU+GPU Programming of Stencil Computations for Resource-Efficient Use of GPU Clusters
conference, October 2015

  • Sourouri, Mohammed; Langguth, Johannes; Spiga, Filippo
  • 2015 IEEE 18th International Conference on Computational Science and Engineering (CSE)
  • DOI: 10.1109/CSE.2015.33

Physis: an implicitly parallel programming model for stencil computations on large-scale GPU-accelerated supercomputers
conference, January 2011

  • Maruyama, Naoya; Nomura, Tatsuo; Sato, Kento
  • Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '11
  • DOI: 10.1145/2063384.2063398

Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines
conference, January 2013

  • Ragan-Kelley, Jonathan; Barnes, Connelly; Adams, Andrew
  • Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation - PLDI '13
  • DOI: 10.1145/2491956.2462176

PATUS: A Code Generation and Autotuning Framework for Parallel Iterative Stencil Computations on Modern Microarchitectures
conference, May 2011

  • Christen, Matthias; Schenk, Olaf; Burkhart, Helmar
  • Distributed Processing Symposium (IPDPS), 2011 IEEE International Parallel & Distributed Processing Symposium
  • DOI: 10.1109/IPDPS.2011.70

A Study on Balancing Parallelism, Data Locality, and Recomputation in Existing PDE Solvers
conference, November 2014

  • Olschanowsky, Catherine; Strout, Michelle Mills; Guzik, Stephen
  • SC14: International Conference for High Performance Computing, Networking, Storage and Analysis
  • DOI: 10.1109/SC.2014.70

Towards automatic translation of OpenMP to MPI
conference, January 2005

  • Basumallik, Ayon; Eigenmann, Rudolf
  • Proceedings of the 19th annual international conference on Supercomputing - ICS '05
  • DOI: 10.1145/1088149.1088174

Understanding stencil code performance on multicore architectures
conference, January 2011

  • Rahman, Shah M. Faizur; Yi, Qing; Qasem, Apan
  • Proceedings of the 8th ACM International Conference on Computing Frontiers - CF '11
  • DOI: 10.1145/2016604.2016641

Auto-generation and auto-tuning of 3D stencil codes on GPU clusters
conference, January 2012

  • Zhang, Yongpeng; Mueller, Frank
  • Proceedings of the Tenth International Symposium on Code Generation and Optimization - CHO '12
  • DOI: 10.1145/2259016.2259037

Early evaluation of directive-based GPU programming models for productive exascale computing
conference, November 2012

  • Lee, Seyong; Vetter, Jeffrey S.
  • 2012 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis
  • DOI: 10.1109/SC.2012.51

Abstract Machine Models and Proxy Architectures for Exascale Computing
conference, November 2014

  • Ang, J. A.; Barrett, R. F.; Benner, R. E.
  • 2014 Hardware-Software Co-Design for High Performance Computing (Co-HPC)
  • DOI: 10.1109/Co-HPC.2014.4

Distributed memory code generation for mixed Irregular/Regular computations
conference, January 2015

  • Ravishankar, Mahesh; Dathathri, Roshan; Elango, Venmugil
  • Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming - PPoPP 2015
  • DOI: 10.1145/2688500.2688515

PARTANS: An autotuning framework for stencil computation on multi-GPU systems
journal, January 2013

  • Lutz, Thibaut; Fensch, Christian; Cole, Murray
  • ACM Transactions on Architecture and Code Optimization, Vol. 9, Issue 4
  • DOI: 10.1145/2400682.2400718

Tuned and wildly asynchronous stencil kernels for hybrid CPU/GPU systems
conference, January 2009

  • Venkatasubramanian, Sundaresan; Vuduc, Richard W.; none, none
  • Proceedings of the 23rd international conference on Conference on Supercomputing - ICS '09
  • DOI: 10.1145/1542275.1542312

OpenMPC: Extended OpenMP Programming and Tuning for GPUs
conference, November 2010

  • Lee, Seyong; Eigenmann, Rudolf
  • 2010 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
  • DOI: 10.1109/SC.2010.36

High Performance Stencil Code Algorithms for GPGPUs
journal, January 2011


STELLA: a domain-specific tool for structured grid methods in weather and climate models
conference, January 2015

  • Gysi, Tobias; Osuna, Carlos; Fuhrer, Oliver
  • Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '15
  • DOI: 10.1145/2807591.2807627

Peta-scale phase-field simulation for dendritic solidification on the TSUBAME 2.0 supercomputer
conference, January 2011

  • Shimokawabe, Takashi; Aoki, Takayuki; Takaki, Tomohiro
  • Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '11
  • DOI: 10.1145/2063384.2063388

SnuCL: an OpenCL framework for heterogeneous CPU/GPU clusters
conference, January 2012

  • Kim, Jungwon; Seo, Sangmin; Lee, Jun
  • Proceedings of the 26th ACM international conference on Supercomputing - ICS '12
  • DOI: 10.1145/2304576.2304623

Hybrid Hexagonal/Classical Tiling for GPUs
conference, January 2014

  • Grosser, Tobias; Cohen, Albert; Holewinski, Justin
  • Proceedings of Annual IEEE/ACM International Symposium on Code Generation and Optimization - CGO '14
  • DOI: 10.1145/2581122.2544160

Scalable Heterogeneous CPU-GPU Computations for Unstructured Tetrahedral Meshes
journal, July 2015

  • Langguth, Johannes; Sourouri, Mohammed; Lines, Glenn Terje
  • IEEE Micro, Vol. 35, Issue 4
  • DOI: 10.1109/MM.2015.70

Optimization of geometric multigrid for emerging multi- and manycore processors
conference, November 2012

  • Williams, Samuel; Kalamkar, Dhiraj D.; Singh, Amik
  • 2012 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis
  • DOI: 10.1109/SC.2012.85

Roofline: an insightful visual performance model for multicore architectures
journal, April 2009

  • Williams, Samuel; Waterman, Andrew; Patterson, David
  • Communications of the ACM, Vol. 52, Issue 4
  • DOI: 10.1145/1498765.1498785

Hybridizing S3D into an Exascale application using OpenACC: An approach for moving to multi-petaflops and beyond
conference, November 2012

  • Levesque, John M.; Sankaran, Ramanan; Grout, Ray
  • 2012 SC - International Conference for High Performance Computing, Networking, Storage and Analysis, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis
  • DOI: 10.1109/SC.2012.69

High-Productivity Framework on GPU-Rich Supercomputers for Operational Weather Prediction Code ASUCA
conference, November 2014

  • Shimokawabe, Takashi; Aoki, Takayuki; Onodera, Naoyuki
  • SC14: International Conference for High Performance Computing, Networking, Storage and Analysis
  • DOI: 10.1109/SC.2014.26

    Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.