lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
HSTREAM: A directive-based language extension for heterogeneous stream computing
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (Parallel Computing)
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM). (Parallel Computing)
2018 (English)In: 2018 21st IEEE International Conference on Computational Science and Engineering (CSE) / [ed] Pop, F; Negru, C; GonzalezVelez, H; Rak, J, IEEE, 2018, p. 138-145Conference paper, Published paper (Refereed)
Abstract [en]

Big data streaming applications require utilization of heterogeneous parallel computing systems, which may comprise multiple multi-core CPUs and many-core accelerating devices such as NVIDIA GPUs and Intel Xeon Phis. Programming such systems require advanced knowledge of several hardware architectures and device-specific programming models, including OpenMP and CUDA. In this paper, we present HSTREAM, a compiler directive-based language extension to support programming stream computing applications for heterogeneous parallel computing systems. HSTREAM source-to-source compiler aims to increase the programming productivity by enabling programmers to annotate the parallel regions for heterogeneous execution and generate target specific code. The HSTREAM runtime automatically distributes the workload across CPUs and accelerating devices. We demonstrate the usefulness of HSTREAM language extension with various applications from the STREAM benchmark. Experimental evaluation results show that HSTREAM can keep the same programming simplicity as OpenMP, and the generated code can deliver performance beyond what CPUs-only and GPUs-only executions can deliver. 

Place, publisher, year, edition, pages
IEEE, 2018. p. 138-145
Series
IEEE International Conference on Computational Science and Engineering, ISSN 1949-0828
Keywords [en]
stream computing, heterogeneous parallel computing systems, source-to-source compilation
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science; Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-79191DOI: 10.1109/CSE.2018.00026ISI: 000458738400019Scopus ID: 2-s2.0-85061051044ISBN: 978-1-5386-7649-3 (electronic)ISBN: 978-1-5386-7650-9 (print)OAI: oai:DiVA.org:lnu-79191DiVA, id: diva2:1270493
Conference
The 21st IEEE International Conference on Computational Science and Engineering (CSE 2018), 29-31 Oct. 2018, Bucharest
Available from: 2018-12-13 Created: 2018-12-13 Last updated: 2019-08-29Bibliographically approved
In thesis
1. Programming and Optimization of Big-Data Applications on Heterogeneous Computing Systems
Open this publication in new window or tab >>Programming and Optimization of Big-Data Applications on Heterogeneous Computing Systems
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The next-generation sequencing instruments enable biological researchers to generate voluminous amounts of data. In the near future, it is projected that genomics will be the largest source of big-data. A major challenge of big data is the efficient analysis of very large data-sets. Modern heterogeneous parallel computing systems, which comprise multiple CPUs, GPUs, and Intel Xeon Phis, can cope with the requirements of big-data analysis applications. However, utilizing these resources to their highest possible extent demands advanced knowledge of various hardware architectures and programming frameworks. Furthermore, optimized software execution on such systems demands consideration of many compile-time and run-time system parameters.

In this thesis, we study and develop parallel pattern matching algorithms for heterogeneous computing systems. We apply our pattern matching algorithm for DNA sequence analysis. Experimental evaluation results show that our parallel algorithm can achieve more than 50x speedup when executed on host CPUs and more than 30x when executed on Intel Xeon Phi compared to the sequential version executed on the CPU.

Thereafter, we combine machine learning and search-based meta-heuristics to determine near-optimal parameter configurations of parallel matching algorithms for efficient execution on heterogeneous computing systems. We use our approach to distribute the workload of the DNA sequence analysis application across the available host CPUs and accelerating devices and to determine the system configuration parameters of a heterogeneous system that comprise Intel Xeon CPUs and Xeon Phi accelerator. Experimental results show that the execution that uses the resources of both host CPUs and accelerating device outperforms the host-only and the device-only executions.

Furthermore, we propose programming abstractions, a source-to-source compiler, and a run-time system for heterogeneous stream computing. Given a source code annotated with compiler directives, the source-to-source compiler can generate device-specific code. The run-time system can automatically distribute the workload across the available host CPUs and accelerating devices. Experimental results show that our solution significantly reduces the programming effort and the generated code delivers better performance than the CPUs-only or GPUs-only executions.

Place, publisher, year, edition, pages
Växjö: Linnaeus University Press, 2018
Series
Linnaeus University Dissertations ; 335/2018
Keywords
Big Data, Heterogeneous Parallel Computing, Software Optimization, Source-to-source Compilation
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science; Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-79192 (URN)978-91-88898-14-2 (ISBN)978-91-88898-15-9 (ISBN)
Public defence
2018-12-20, D1136, Hus D, Växjö, 15:00 (English)
Opponent
Supervisors
Available from: 2018-12-17 Created: 2018-12-13 Last updated: 2018-12-17Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Memeti, SuejbPllana, Sabri

Search in DiVA

By author/editor
Memeti, SuejbPllana, Sabri
By organisation
Department of computer science and media technology (CM)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 110 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf