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Developer Productivity in HPC Application Development: An Overview of Recent Techniques

Published: 31 May 2016 Publication History

Abstract

Increasing computing power with evolving hardware architectures has lead to change in programming paradigm from serial to parallel. Unlike the sequential counterpart, application building for High Performance Computing (HPC) is extremely challenging for developers. In order to improve the programmer productivity, it is necessary to address the challenges such as: i) How to abstract the hardware and low level complexities to make programming easier? ii) What features should a design assistance tool have to simplify application development? iii) How should the programming languages be enhanced for HPC? iv) What sort of prediction techniques can be developed to assist programmers to predict potential speedup? v) Can refactoring techniques solve the issue of parallelizing existing serial code? In this talk we make an attempt to present a landscape of the existing approaches to assist the software building process in HPC from a developer's point of view, and highlight some important research questions. We also discuss the state of practice in the industry and some of the application specific tools developed for HPC.

References

[1]
Krste Asanovic, Rastislav Bodik, James Demmel, Tony Keaveny, Kurt Keutzer, John Kubiatowicz, Nelson Morgan, David Patterson, Koushik Sen, John Wawrzynek, David Wessel, and Katherine Yelick. 2009. A View of the ParallelComputing Landscape. Commun. ACM 52, 10 (oct 2009), 56--67
[2]
Wen-mei W. Hwu. 2011. GPU Computing Gems Jade Edition (1st ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
[3]
Rubin, Ely Levy, Amnon Barak, and Tal Ben-Nun. 2014. MAPS: Opti- mizing Massively Parallel Applications Using Device-Level Memory Abstraction. ACM Trans. Archit. Code Optim. 11, 4, Article 44 (Dec. 2014),
[4]
S. Sarkar, S. Mitra, and A. Srinivasan. 2012. Reuse and Refactoring of GPU Kernels to Design Complex Applications. In Parallel and Distributed Processing with Applications (ISPA), 2012 IEEE 10th International Symposium on. 134--141
[5]
J. Meng, V. A. Morozov, et al. GROPHECY: GPU Performance Projection from CPU Code Skeletons. In Proc. of ACM Intl. Conf. for High Performance Computing, Networking, Storage and Analysis (SC), 2011

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cover image ACM Conferences
SEM4HPC '16: Proceedings of the ACM Workshop on Software Engineering Methods for Parallel and High Performance Applications
May 2016
54 pages
ISBN:9781450343510
DOI:10.1145/2916026
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 ACM 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]

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Publication History

Published: 31 May 2016

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Author Tags

  1. gpgpu
  2. parallel programming
  3. refactoring
  4. reusability
  5. simd
  6. verification

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  • Research-article

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  • SERB

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HPDC'16
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SEM4HPC '16 Paper Acceptance Rate 5 of 11 submissions, 45%;
Overall Acceptance Rate 8 of 16 submissions, 50%

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