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Benchmarking OpenCL, OpenACC, OpenMP, and CUDA: Programming Productivity, Performance, and Energy Consumption
Linnaeus University, Faculty of Technology, Department of Computer Science. (Parallel Computing)
Linköping University.
Linnaeus University, Faculty of Technology, Department of Computer Science. (Parallel Computing)
Cracow University of Technology, Poland.
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2017 (English)In: ProceedingARMS-CC '17 Proceedings of the 2017 Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, New York, NY, USA: Association for Computing Machinery (ACM), 2017, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

Many modern parallel computing systems are heterogeneous at their node level. Such nodes may comprise general purpose CPUs and accelerators (such as, GPU, or Intel Xeon Phi) that provide high performance with suitable energy-consumption characteristics. However, exploiting the available performance of heterogeneous architectures may be challenging. There are various parallel programming frameworks (such as, OpenMP, OpenCL, OpenACC, CUDA) and selecting the one that is suitable for a target context is not straightforward. In this paper, we study empirically the characteristics of OpenMP, OpenACC, OpenCL, and CUDA with respect to programming productivity, performance, and energy. To evaluate the programming productivity we use our homegrown tool CodeStat, which enables us to determine the percentage of code lines required to parallelize the code using a specific framework. We use our tools MeterPU and x-MeterPU to evaluate the energy consumption and the performance. Experiments are conducted using the industry-standard SPEC benchmark suite and the Rodinia benchmark suite for accelerated computing on heterogeneous systems that combine Intel Xeon E5 Processors with a GPU accelerator or an Intel Xeon Phi co-processor.

Place, publisher, year, edition, pages
New York, NY, USA: Association for Computing Machinery (ACM), 2017. p. 1-6
National Category
Computer Systems Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-67141DOI: 10.1145/3110355.3110356ISBN: 978-1-4503-5116-4 (print)OAI: oai:DiVA.org:lnu-67141DiVA, id: diva2:1129227
Conference
ARMS-CC '17: the 2017 Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, 28 July, 2017
Available from: 2017-08-01 Created: 2017-08-01 Last updated: 2018-12-13Bibliographically 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

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Publisher's full texthttp://dl.acm.org/citation.cfm?doid=3110355.3110356

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Memeti, SuejbPllana, Sabri

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