Open this publication in new window or tab >>Show others...
2017 (English)In: Proceedings of the 2017 Workshop on Adaptive Resource Management and Scheduling for Cloud Computing, 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
Association for Computing Machinery (ACM), 2017
Keywords
heterogeneous computing, parallel computing, parallel programming models, comparative study, OpenCL, OpenACC, OpenMP, CUDA, Programming productivity, Performance, Energy consumption, GPU, Xeon-Phi, MeterPU
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-168604 (URN)10.1145/3110355.3110356 (DOI)9781450351164 (ISBN)
Conference
2017 Workshop on Adaptive Resource Management and Scheduling for Cloud Computing (ARMS-CC'17), Washington, DC, USA
2020-08-262020-08-262020-08-27