| Autor: |
Bongsuk Ko, Seunghun Han, Yongjun Park, Moongu Jeon, Byeongcheol Lee |
| Jazyk: |
angličtina |
| Rok vydání: |
2017 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 5, Pp 10081-10092 (2017) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2017.2708738 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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