Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
¸ÖƼÄÚ¾î ȯ°æÀ» À§ÇÑ °ÈÇнÀ ±â¹Ý ij½Ã ÆÄƼ¼Å´× |
¿µ¹®Á¦¸ñ(English Title) |
A Reinforcement Learning-Based Cache Partitioning Scheme for Multi-Core Environments |
ÀúÀÚ(Author) |
ÃÖµ¿±Ô
±èÁ¾¼®
¿ìÈ«¿í
¼ÀǼº
Donggyu Choi
Jongseok Kim
Honguk Woo
Euiseong Seo
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¿ø¹®¼ö·Ïó(Citation) |
VOL 48 NO. 06 PP. 0618 ~ 0628 (2021. 06) |
Çѱ۳»¿ë (Korean Abstract) |
¸¹Àº »ó¿ë ÇÁ·Î¼¼¼µéÀº LLC(last-level cache)¸¦ ¸ðµç ÄÚ¾îµéÀÌ °øÀ¯ÇÏ¿© »ç¿ëÇϱ⠶§¹®¿¡ LLC°¡ º´¸ñÀÌ µÇ¾î Àüü ½Ã½ºÅÛ ¼º´ÉÀÌ ÀúÇϵȴÙ. Çϵå¿þ¾î ij½Ã ÆÄƼ¼Å´× ±â¹ýÀ» Àû¿ëÇÏ¿© ÀÌ·± ¹®Á¦¸¦ ÇØ°áÇÒ ¼ö ÀÖÁö¸¸, ÀûÀýÇÑ Ä³½Ã ÆÄƼ¼ÇÀ» °áÁ¤ÇÏ´Â °ÍÀº Áö´ÉÀûÀÎ ¾Ë°í¸®ÁòÀ» ÇÊ¿ä·Î ÇÑ´Ù. °ÈÇнÀÀ» »ç¿ëÇÑ Ä³½Ã ÆÄƼ¼Å´×Àº ¾ÖÇø®ÄÉÀ̼ÇÀÇ ¼ö°¡ Áõ°¡ÇÔ¿¡ µû¶ó ¸ðµ¨ º¹Àâµµ°¡ Æø¹ßÀûÀ¸·Î Áõ°¡ÇÑ´Ù. º» ³í¹®Àº ij½Ã ÆÄƼ¼Å´× ¹®Á¦ÀÇ °ÈÇнÀ Àû¿ëÀ» À§ÇØ ¸ðµ¨ º¹ÀâµµÀÇ Æø¹ßÀû Áõ°¡¸¦ ¾ïÁ¦ÇÏ°í ´ÙÁß ¾ÖÇø®ÄÉÀ̼ǿ¡ ´ëÇØ È®Àå °¡´ÉÇÑ °ÈÇнÀ ±â¹Ý ij½Ã ÆÄƼ¼Å´×À» Á¦¾ÈÇÑ´Ù. ¸ÕÀú ÀûÀº ¼öÀÇ ¾ÖÇø®ÄÉÀ̼ǿ¡ ´ëÇØ Ä³½Ã ÆÄƼ¼Å´×À» ÇÏ´Â °ÈÇнÀ ¸ðµ¨À» ÇнÀÇÑ´Ù. ±× ÈÄ Ä³½Ã »ç¿ë Ư¼º ¿¹Ãø ÁöµµÇнÀ ¸ðµ¨À» ÅëÇØ ¾òÀº Á¤º¸·Î ¾ÖÇø®ÄÉÀ̼ǵéÀ» Ŭ·¯½ºÅ͸µÇÏ¿© °ÈÇнÀ ¸ðµ¨À» È®Àå Àû¿ëÇÑ´Ù. À̸¦ ÅëÇØ ´õ ¸¹Àº ¾ÖÇø®ÄÉÀ̼ǿ¡ ´ëÇؼµµ ij½Ã ÆÄƼ¼Å´×À» ÇÏ¿© ÃÖ´ë 19.75%ÀÇ ¼º´É Çâ»óÀ» ¾òÀ» ¼ö ÀÖ¾ú´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Most processors currently in use provide the shared last-level cache (LLC). Therefore, when multiple applications compete intensely for the LLC, its hit ratio is adversely impacted by extremely frequent cache line replacement; this may result in a significant degradation of the overall performance. The hardware-based cache partitioning techniques can relieve this issue by isolating the cache space of a core from others. However, it is necessary to use an adaptive and intelligent cache partitioning algorithm to dynamically determine the optimal cache partition. Reinforcement learning is an appropriate approach for this kind of problems. However, its model complexity skyrockets as the number of the applications to partition increases. This paper proposes a reinforcement learning-based cache partitioning scheme that can support a large number of running applications. Firstly, we built a reinforcement learning model and made it learn to perform cache partitioning for a small number of applications. We then extended it by clustering applications with the information obtained via supervised learning models for cache-use characteristic predictions, which enabled cache partitioning for more applications and resulted in performance gains of up to 19.75%.
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Å°¿öµå(Keyword) |
¸ÖƼÄÚ¾î
ij½Ã ÆÄƼ¼Å´×
ij½Ã °ü¸®
°ÈÇнÀ
ÁöµµÇнÀ
multi-core
cache partitioning
cache management
reinforcement learning
supervised learning
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