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Current Result Document : 6 / 8

ÇѱÛÁ¦¸ñ(Korean Title) OpenCLÀ» ÀÌ¿ëÇÑ µ·»ç °¨½Ã ÀÀ¿ëÀÇ È¿À²ÀûÀΠŽºÅ© ºÐ¹è
¿µ¹®Á¦¸ñ(English Title) Efficient Task Distribution for Pig Monitoring Applications Using OpenCL
ÀúÀÚ(Author) ±èÁø¼º   ÃÖÀ±Ã¢   ±èÀçÇР  Á¤¿¬¿ì   Á¤¿ëÈ­   ¹Ú´ëÈñ   ±èÇÐÀç   Jinseong Kim   Younchang Choi   Jaehak Kim   Yeonwoo Chung   Yongwha Chung   Daihee Park   Hakjae Kim  
¿ø¹®¼ö·Ïó(Citation) VOL 06 NO. 10 PP. 0407 ~ 0414 (2017. 10)
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(Korean Abstract)
´Ù¼öÀÇ Å½ºÅ©·Î ±¸¼ºµÈ µ·»ç °¨½Ã ÀÀ¿ëÀº ³»ÀçµÈ µ¥ÀÌÅÍ º´·Ä¼ºÀ» È°¿ëÇÏ°í ¼º´É°¡¼Ó±â¸¦ »ç¿ëÇÏ¿© º´·Ä 󸮰¡ °¡´ÉÇÏ´Ù. º» ³í¹®¿¡¼­´Â ¸ÖƼÄÚ¾î CPU¿Í ¸Å´ÏÄÚ¾î GPU·Î ±¸¼ºµÈ À̱âÁ¾ ÄÄÇ»Æà Ç÷§Æû¿¡¼­ µ·»ç °¨½Ã ÀÀ¿ë ¼öÇà ½Ã Å½ºÅ© ºÐ¹è ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. Áï, °¢ ŽºÅ©º°·Î OpenCLÀ» ÀÌ¿ëÇÑ º´·Ä ÇÁ·Î±×·¥À» ÀÛ¼ºÇÑ µÚ, deviceCPU¿Í deviceGPU °¢°¢¿¡¼­ ¼öÇà½ÃÄÑ ÃøÁ¤µÈ ¼öÇà½Ã°£À» ±âÁØÀ¸·Î °¡Àå ÀûÇÕÇÑ Ã³¸®±â¸¦ °áÁ¤ÇÑ´Ù. Á¦¾È ¹æ¹ýÀº °£´ÜÇÏÁö¸¸ ¸Å¿ì È¿°úÀûÀÌ°í, CPU¿Í GPU·Î ±¸¼ºµÈ À̱âÁ¾ ÄÄÇ»Æà Ç÷§Æû¿¡¼­ ´Ù¼öÀÇ Å½ºÅ©·Î ±¸¼ºµÈ ´Ù¸¥ ÀÀ¿ëÀ» º´·ÄÈ­ÇÏ´Â °æ¿ì¿¡µµ Àû¿ëµÉ ¼ö ÀÖ´Ù. ½ÇÇè °á°ú, »óÀÌÇÑ À̱âÁ¾ ÄÄÇ»Æà Ç÷§Æû¿¡¼­ ÃÖÀûÀÇ Å½ºÅ© ºÐ¹è·Î ¼öÇàÇÑ °æ¿ì°¡ Àüü ŽºÅ©µéÀ» deviceGPU¿¡¼­ ¼öÇàÇÑ GPU-only ¹æ¹ý¿¡ ºñ±³ÇÏ¿© °¢°¢ 2.7¹è, 8.7¹è, 2.7¹è ¼º´É °³¼±ÀÌ µÇ¾úÀ½À» È®ÀÎÇÏ¿´´Ù.
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(English Abstract)
Pig monitoring applications consisting of many tasks can take advantage of inherent data parallelism and enable parallel processing using performance accelerators. In this paper, we propose a task distribution method for pig monitoring applications into a heterogenous computing platform consisting of a multicore-CPU and a manycore-GPU. That is, a parallel program written in OpenCL is developed, and then the most suitable processor is determined based on the measured execution time of each task. The proposed method is simple but very effective, and can be applied to parallelize other applications consisting of many tasks on a heterogeneous computing platform consisting of a CPU and a GPU. Experimental results show that the performance of the proposed task distribution method on three different heterogeneous computing platforms can improve the performance of the typical GPU-only method where every tasks are executed on a deviceGPU by a factor of 1.5, 8.7 and 2.7, respectively.
Å°¿öµå(Keyword) º´·Ä ÇÁ·Î±×·¡¹Ö   À̱âÁ¾ ÄÄÇ»Æà  ÄÄÇ»ÅÍ ºñÀü   Parallel Programming   Heterogeneous-Computing   Computer Vision  
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