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Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
GPGPU ÀÚ¿ø È°¿ë °³¼±À» À§ÇÑ ºí·Ï Áö¿¬½Ã°£ ±â¹Ý ¿öÇÁ ½ºÄÉÁÙ¸µ ±â¹ý |
¿µ¹®Á¦¸ñ(English Title) |
A Novel Cooperative Warp and Thread Block Scheduling Technique for Improving the GPGPU Resource Utilization |
ÀúÀÚ(Author) |
Do Cong Thuan
ÃÖ¿ë
±èÁ¾¸é
±èöȫ
Do Cong Thuan
Yong Choi
Jong Myon Kim
Cheol Hong Kim
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¿ø¹®¼ö·Ïó(Citation) |
VOL 06 NO. 05 PP. 0219 ~ 0230 (2017. 05) |
Çѱ۳»¿ë (Korean Abstract) |
¸ÖƼ½º·¹µù ±â¹ýÀÌ Àû¿ëµÈ GPGPU´Â ³»ºÎ º´·Ä ÀÚ¿øµéÀ» ±â¹ÝÀ¸·Î µ¥ÀÌÅ͸¦ °í¼ÓÀ¸·Î ó¸®ÇÏ°í ¸Þ¸ð¸® Á¢±Ù½Ã°£À» °¨¼Ò½Ãų ¼ö ÀÖ´Ù. CUDA, OpenCL µî°ú °°Àº ÇÁ·Î±×·¡¹Ö ¸ðµ¨À» È°¿ëÇÏ¸é ½º·¹µå ·¹º§ 󸮸¦ ÅëÇØ ÀÀ¿ëÇÁ·Î±×·¥ÀÇ °í¼Ó º´·Ä ¼öÇàÀÌ °¡´ÉÇÏ´Ù. ÇÏÁö¸¸, GPGPU´Â ¹ü¿ë ¸ñÀûÀÇ ÀÀ¿ëÇÁ·Î±×·¥À» ¼öÇàÇÔ¿¡ ÀÖ¾î ³»ºÎ Çϵå¿þ¾î ÀÚ¿øµéÀ» È¿°úÀûÀ¸·Î »ç¿ëÇÏÁö ¸øÇÑ´Ù´Â ´ÜÁ¡À» º¸ÀÌ°í ÀÖ´Ù. ÀÌ´Â GPGPU¿¡¼ »ç¿ëÇÏ´Â ±âÁ¸ÀÇ ¿öÇÁ/½º·¹µå ºí·Ï ½ºÄÉÁÙ·¯°¡ ¸Þ¸ð¸® Á¢±Ù½Ã°£ÀÌ ±ä ¸í·É¾î¸¦ ó¸®Çϴµ¥ ÀÖ¾î¼ ºñÈ¿À²ÀûÀ̱⠶§¹®ÀÌ´Ù. ÀÌ¿Í °°Àº ¹®Á¦Á¡À» ÇØ°áÇϱâ À§ÇØ º» ³í¹®¿¡¼´Â GPGPU ÀÚ¿ø È°¿ë·üÀ» °³¼±Çϱâ À§ÇÑ »õ·Î¿î ¿öÇÁ ½ºÄÉÁÙ¸µ ±â¹ýÀ» Á¦¾ÈÇÏ°íÀÚ ÇÑ´Ù. Á¦¾ÈÇÏ´Â ¿öÇÁ ½ºÄÉÁÙ¸µ ±â¹ýÀº ½º·¹µå ºí·ÏÀÇ ¿öÇÁµé Áß ±ä ¸Þ¸ð¸® Á¢±Ù½Ã°£À» °¡Áø ¿öÇÁ¿Í ªÀº ¸Þ¸ð¸® Á¢±Ù½Ã°£À» °¡Áø ¿öÇÁµéÀ» ±¸ºÐÇÑ ÈÄ, ±ä ¸Þ¸ð¸® Á¢±Ù½Ã°£À» °¡Áø ¿öÇÁ¸¦ ¿ì¼± ÇÒ´çÇÏ°í, ªÀº ¸Þ¸ð¸® Á¢±Ù½Ã°£À» °¡Áø ¿öÇÁ¸¦ ³ªÁß¿¡ ÇÒ´çÇÏ¿© ó¸®ÇÑ´Ù. ¶ÇÇÑ, ¸Þ¸ð¸®¿Í ³»ºÎ ¿¬°á¸Á¿¡¼ ³ôÀº °æÇÕÀÌ ¹ß»ýÇßÀ» ¶§ µ¿ÀûÀ¸·Î ½ºÆ®¸®¹Ö ¸ÖƼÇÁ·Î¼¼¼ÀÇ ¼ö¸¦ °¨¼Ò½ÃÄÑ ¿öÇÁ ½ºÄÉÁÙ·¯¸¦ È¿°úÀûÀ¸·Î »ç¿ëÇÒ ¼ö ÀÖ´Â ±â¹ýµµ Á¦¾ÈÇÑ´Ù. ½ÇÇè°á°ú¿¡ µû¸£¸é, 15°³ÀÇ ½ºÆ®¸®¹Ö ¸ÖƼÇÁ·Î¼¼¼¸¦ °¡Áø GPGPU Ç÷§Æû¿¡¼ Á¦¾ÈµÈ ¿öÇÁ ½ºÄÉÁÙ¸µ ±â¹ýÀº ±âÁ¸ÀÇ ¶ó¿îµå·Îºó ¿öÇÁ ½ºÄÉÁÙ¸µ ±â¹ý°ú ºñ±³ÇÏ¿© Æò±Õ 7.5%ÀÇ ¼º´É(IPC)ÀÌ Çâ»óµÊÀ» È®ÀÎÇÒ ¼ö ÀÖ´Ù. ¶ÇÇÑ, Á¦¾ÈµÈ µÎ °³ÀÇ ±â¹ýÀ» µ¿½Ã¿¡ Àû¿ëÇÏ¿´À» °æ¿ì¿¡´Â Æò±Õ 8.9%ÀÇ ¼º´É(IPC) Çâ»óÀ» º¸ÀδÙ.
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¿µ¹®³»¿ë (English Abstract) |
General-Purpose Graphics Processing Units (GPGPUs) build massively parallel architecture and apply multithreading technology to explore parallelism. By using programming models like CUDA, and OpenCL, GPGPUs are becoming the best in exploiting plentiful thread-level parallelism caused by parallel applications. Unfortunately, modern GPGPU cannot efficiently utilize its available hardware resources for numerous general-purpose applications. One of the primary reasons is the inefficiency of existing warp/thread block schedulers in hiding long latency instructions, resulting in lost opportunity to improve the performance. This paper studies the effects of hardware thread scheduling policy on GPGPU performance. We propose a novel warp scheduling policy that can alleviate the drawbacks of the traditional round-robin policy. The proposed warp scheduler first classifies the warps of a thread block into two groups, warps with long latency and warps with short latency and then schedules the warps with long latency before the warps with short latency. Furthermore, to support the proposed warp scheduler, we also propose a supplemental technique that can dynamically reduce the number of streaming multiprocessors to which will be assigned thread blocks when encountering a high contention degree at the memory and interconnection network. Based on our experiments on a 15-streaming multiprocessor GPGPU platform, the proposed warp scheduling policy provides an average IPC improvement of 7.5% over the baseline round-robin warp scheduling policy. This paper also shows that the GPGPU performance can be improved by approximately 8.9% on average when the two proposed techniques are combined.
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Å°¿öµå(Keyword) |
GPGPU
º´·Ä¼º
¼º´É
¿öÇÁ ½ºÄÉÁ층
ÀÚ¿ø È°¿ë
GPGPU
Parallelism
Performance
Warp Scheduling
Resource Utilization
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