How to Transfer Strided Data into Accelerator
Source abstract
Abstract
Data transfer from a host central processing unit (CPU) into an accelerator is a performance bottleneck for applications accelerated by accelerators (such as general purpose digital signal processing (GPDSP), many integrated core (MIC), and general purpose graphics processing unit (GPGPU)). It is complicated and inefficient to transfer non-contiguous data with special respect to strided data. In this work, we present three approaches to transfer strided data for different scenarios: Redundant copy (RC), selective copy (SC), and transfer after transformed (TaT). We propose a space and time efficient method named TaT, in which strided data are transformed on the CPU first and then transferred into the accelerator. We simulated regions-of-interest (ROI) coding and validate proposed techniques. TaT was superior to RC on space efficiency and close to SC on saving space, but better than SC on time waste respectively.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Data Storage Technologies · Algorithms and Data Compression · Advanced Data Compression Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer, National University of Defense Technology
References (41)
Show source-provided references
1. 10.1177/00405175900600040410.1177/004051759006000404 ↗
2. 10.1016/j.compstruct.2018.02.04010.1016/j.compstruct.2018.02.040 ↗
3. Wu R. -C · Proceedings of the 4th IASTED Asian Conference on Power and Energy Systems, AsiaPES · 2008
4. Arafa A. A · Proceedings of the DTIS’ 09–2009 4th IEEE International Conference on Design and Technology of Integrated Systems in Nanoscale Era
5. 10.1109/TAES.2008.466773210.1109/TAES.2008.4667732 ↗
6. 10.1109/TIM.2010.204713010.1109/TIM.2010.2047130 ↗
7. Liu J. · Journal of Hunan University Natural Sciences · 2013 · 40 · 148
8. Tao G. · The International Journal of High Performance Computing Applications · 2014 · 28 · 25510.1177/1094342014524240 ↗
9. 10.1007/s11390-014-1484-z10.1007/s11390-014-1484-z ↗
10. Michalakes J. · IEEE International Symposium on Parallel and Distributed Processing · 2008 · 1
11. Nukada A. · International Conference for High Performance Computing, Networking, Storage and Analysis · 2008 · 1
12. Can X. · Applied Mathematics & Information Sciences · 2011 · 5 · 129
13. He B. · ACM/IEEE Supercomputing Conference · 2007 · 1
14. Michael M · IEEE Computer · 2003 · 36 · 10610.1109/MC.2003.1236476 ↗
15. Ian B. · ACM Transaction on Graph · 2004 · 23 · 77710.1145/1015706.1015800 ↗
16. Ian B. · ACM Workshop on General-Purpose Computing on Graphics Processors · 2004 · 20
17. 10.1145/1345206.134522010.1145/1345206.1345220 ↗
18. Zhe F. · ACM/IEEE Supercomputing Conference · 2004 · 47
19. 10.1016/j.jpdc.2016.04.00710.1016/j.jpdc.2016.04.007 ↗
20. Kasagi A. · Proceedings of the International Conference on Parallel Processing · 2014 · 251
21. Nakano K · Proceedings of the 3rd International Conference on Networking and Computing · 2012 · 58
22. Song L. · Proceedings of the 47th Annual IEEE/ACM International Symposium on Microarchitecture · 2015 · 659
23. 10.1007/978-3-319-29778-1_1110.1007/978-3-319-29778-1_11 ↗
24. Lu Y. T · ISC · 2015
25. Du Y.-F · Chinese Journal of Computer Science · 2013 · 40 · 19
26. Zheng H. · Acta Electronica Sinica · 2006 · 34 · 618
27. 10.1109/TSMCA.2009.201989310.1109/TSMCA.2009.2019893 ↗
28. Michalakes J. · IEEE International Symposium on Parallel and Distributed Processing · 2008 · 1
29. Guiang C. S · The 3rd LCI International Conference on Linux Clusters · 2003
30. Seidel J. · John von Neumann Institute for Computing, NIC Series · 2007 · 38 · 425
31. http://www.top500.org (accessed November2020).
32. Jun Y. X · Journal of Computer Science and Technology · 2011 · 26 · 34410.1007/s02011-011-1137-8 ↗
33. Yang D. Y.-F · Journal of Northeastern University · 2014 · 35 · 102
34. Guiang C. S · The 3rd LCI International Conference on Linux Clusters · 2003
35. Seidel J. · John von Neumann Institute for Computing, NIC Series · 2007 · 38 · 425
36. Yeom J. -S · ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis · 2010
37. Zheng H. · Acta Electronica Sinica · 2006 · 34 · 618
38. 10.1109/TSMCA.2009.201989310.1109/TSMCA.2009.2019893 ↗
39. Ian B. · ACM Workshop on General-Purpose Computing on Graphics Processors · 2004 · p20
40. 10.1145/1345206.134522010.1145/1345206.1345220 ↗
41. Satish N · International Conference on High Performance Computing · 2009 · 400