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A Comparative Study of Programming Environments Exploiting Heterogeneous Systems
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DOI：10.1109/ACCESS.2017.2708738.png)
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摘要
En 中文
This paper compares programming environments that exploit heterogeneous systems to process a large amount of data efficiently. Our motivation is to investigate the feasibility of the adaptive, transparent migration of intensive computation for a large amount of data across heterogeneous programming languages and processors for high performance and programmability. We compare a variety of programming environments composed of programming languages, such as Java and C, memory space models, such as distinct and shared memory, and parallel processors, such as general-purpose CPUs and graphics processing units (GPUs) to examine their performance-programmability tradeoffs. In addition, we introduce a software based shared virtual memory that creates a view of the host memory inside GPU kernels to enable seamless computation offloading from the host to the device. This paper reveals a programmability-performance hierarchy in which programs increase their performance at the cost of decreasing programmability. The experimental results suggest the desirability of a well-balanced system.
Keyword:
Big data processing
heterogeneous systems
programming environment
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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