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HSTREAM: A directive-based language extension for heterogeneous stream computing
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). (Parallel Computing)
Linnéuniversitetet, Fakulteten för teknik (FTK), Institutionen för datavetenskap och medieteknik (DM). (Parallel Computing)
2018 (engelsk)Inngår i: 2018 21st IEEE International Conference on Computational Science and Engineering (CSE) / [ed] Pop, F; Negru, C; GonzalezVelez, H; Rak, J, IEEE, 2018, s. 138-145Konferansepaper, Publicerat paper (Fagfellevurdert)
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. 

sted, utgiver, år, opplag, sider
IEEE, 2018. s. 138-145
Serie
IEEE International Conference on Computational Science and Engineering, ISSN 1949-0828
Emneord [en]
stream computing, heterogeneous parallel computing systems, source-to-source compilation
HSV kategori
Forskningsprogram
Data- och informationsvetenskap, Datavetenskap; Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:lnu:diva-79191DOI: 10.1109/CSE.2018.00026ISI: 000458738400019Scopus ID: 2-s2.0-85061051044ISBN: 978-1-5386-7649-3 (digital)ISBN: 978-1-5386-7650-9 (tryckt)OAI: oai:DiVA.org:lnu-79191DiVA, id: diva2:1270493
Konferanse
The 21st IEEE International Conference on Computational Science and Engineering (CSE 2018), 29-31 Oct. 2018, Bucharest
Tilgjengelig fra: 2018-12-13 Laget: 2018-12-13 Sist oppdatert: 2019-08-29bibliografisk kontrollert
Inngår i avhandling
1. Programming and Optimization of Big-Data Applications on Heterogeneous Computing Systems
Åpne denne publikasjonen i ny fane eller vindu >>Programming and Optimization of Big-Data Applications on Heterogeneous Computing Systems
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Växjö: Linnaeus University Press, 2018
Serie
Linnaeus University Dissertations ; 335/2018
Emneord
Big Data, Heterogeneous Parallel Computing, Software Optimization, Source-to-source Compilation
HSV kategori
Forskningsprogram
Data- och informationsvetenskap; Data- och informationsvetenskap, Datavetenskap
Identifikatorer
urn:nbn:se:lnu:diva-79192 (URN)978-91-88898-14-2 (ISBN)978-91-88898-15-9 (ISBN)
Disputas
2018-12-20, D1136, Hus D, Växjö, 15:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-12-17 Laget: 2018-12-13 Sist oppdatert: 2018-12-17bibliografisk kontrollert

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