Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪

Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably lo...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhan, Huixin [verfasserIn] Sheng, Victor S. [verfasserIn] Lin, Wei-Ming [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Reinforcement learning Simultaneous multithreading Actor–critic Machine learning

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 186
Übergeordnetes Werk:	volume:186

DOI / URN:	10.1016/j.eswa.2021.115717

Katalog-ID:	ELV055597025

Internformat


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245	1	0	\|a Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪
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520			\|a Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably long due to many program execution-dependent factors that result in performance degradation. Such an issue becomes even more problematic in an SMT environment in which these registers are shared among concurrently running threads. Smartly managing this critical shared resource to ensure that slower threads do not block faster threads’ execution is essential to the advancement of SMT performance. In this paper, an actor–critic style reinforcement learning (RL) algorithm is proposed to dynamically assigning an upper-bound (cap) of the rename registers any thread is allowed to use according to the threads’ real-time demand. In particular, a critic network projects the current Issue Queues (IQ) usage, register file usage, and the cap value to a reward; an actor network is trained to project the current IQ usage and register file usage to the optimal real-time cap value via ascending the instructions per cycle (IPC) gradient within the trajectory distribution. The proposed method differs from the state-of-the-art (Wang and Lin, 2018) as the cap for the rename registers for each thread is adjusted in real-time according to the policy and state transition from self-play. The proposed method shows an improvement in IPC up to 162.8% in a 4-threaded system, 154.8% in a 6-threaded system and up to 101.7% in an 8-threaded system. The code is now available open source at https://github.com/98k-bot/RL-based-SMT-Register-Renaming-Policy.
650		4	\|a Reinforcement learning
650		4	\|a Simultaneous multithreading
650		4	\|a Actor–critic
650		4	\|a Machine learning
700	1		\|a Sheng, Victor S. \|e verfasserin \|0 (orcid)0000-0003-4960-174X \|4 aut
700	1		\|a Lin, Wei-Ming \|e verfasserin \|0 (orcid)0000-0002-9350-6646 \|4 aut
773	0	8	\|i Enthalten in \|t Expert systems with applications \|d Amsterdam [u.a.] : Elsevier Science, 1990 \|g 186 \|h Online-Ressource \|w (DE-627)320577961 \|w (DE-600)2017237-0 \|w (DE-576)11481807X \|7 nnns
773	1	8	\|g volume:186
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912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
951			\|a AR
952			\|d 186

Indexfelder

author_variant	h z hz v s s vs vss w m l wml
matchkey_str	zhanhuixinshengvictorslinweiming:2021----:enocmnlannbsdeitreaigoiyosmlae
hierarchy_sort_str	2021
bklnumber	54.72
publishDate	2021
allfields	10.1016/j.eswa.2021.115717 doi (DE-627)ELV055597025 (ELSEVIER)S0957-4174(21)01099-X DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhan, Huixin verfasserin (orcid)0000-0001-8926-1941 aut Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪ 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably long due to many program execution-dependent factors that result in performance degradation. Such an issue becomes even more problematic in an SMT environment in which these registers are shared among concurrently running threads. Smartly managing this critical shared resource to ensure that slower threads do not block faster threads’ execution is essential to the advancement of SMT performance. In this paper, an actor–critic style reinforcement learning (RL) algorithm is proposed to dynamically assigning an upper-bound (cap) of the rename registers any thread is allowed to use according to the threads’ real-time demand. In particular, a critic network projects the current Issue Queues (IQ) usage, register file usage, and the cap value to a reward; an actor network is trained to project the current IQ usage and register file usage to the optimal real-time cap value via ascending the instructions per cycle (IPC) gradient within the trajectory distribution. The proposed method differs from the state-of-the-art (Wang and Lin, 2018) as the cap for the rename registers for each thread is adjusted in real-time according to the policy and state transition from self-play. The proposed method shows an improvement in IPC up to 162.8% in a 4-threaded system, 154.8% in a 6-threaded system and up to 101.7% in an 8-threaded system. The code is now available open source at https://github.com/98k-bot/RL-based-SMT-Register-Renaming-Policy. Reinforcement learning Simultaneous multithreading Actor–critic Machine learning Sheng, Victor S. verfasserin (orcid)0000-0003-4960-174X aut Lin, Wei-Ming verfasserin (orcid)0000-0002-9350-6646 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 186 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:186 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz VZ AR 186
spelling	10.1016/j.eswa.2021.115717 doi (DE-627)ELV055597025 (ELSEVIER)S0957-4174(21)01099-X DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhan, Huixin verfasserin (orcid)0000-0001-8926-1941 aut Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪ 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably long due to many program execution-dependent factors that result in performance degradation. Such an issue becomes even more problematic in an SMT environment in which these registers are shared among concurrently running threads. Smartly managing this critical shared resource to ensure that slower threads do not block faster threads’ execution is essential to the advancement of SMT performance. In this paper, an actor–critic style reinforcement learning (RL) algorithm is proposed to dynamically assigning an upper-bound (cap) of the rename registers any thread is allowed to use according to the threads’ real-time demand. In particular, a critic network projects the current Issue Queues (IQ) usage, register file usage, and the cap value to a reward; an actor network is trained to project the current IQ usage and register file usage to the optimal real-time cap value via ascending the instructions per cycle (IPC) gradient within the trajectory distribution. The proposed method differs from the state-of-the-art (Wang and Lin, 2018) as the cap for the rename registers for each thread is adjusted in real-time according to the policy and state transition from self-play. The proposed method shows an improvement in IPC up to 162.8% in a 4-threaded system, 154.8% in a 6-threaded system and up to 101.7% in an 8-threaded system. The code is now available open source at https://github.com/98k-bot/RL-based-SMT-Register-Renaming-Policy. Reinforcement learning Simultaneous multithreading Actor–critic Machine learning Sheng, Victor S. verfasserin (orcid)0000-0003-4960-174X aut Lin, Wei-Ming verfasserin (orcid)0000-0002-9350-6646 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 186 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:186 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz VZ AR 186
allfields_unstemmed	10.1016/j.eswa.2021.115717 doi (DE-627)ELV055597025 (ELSEVIER)S0957-4174(21)01099-X DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhan, Huixin verfasserin (orcid)0000-0001-8926-1941 aut Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪ 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably long due to many program execution-dependent factors that result in performance degradation. Such an issue becomes even more problematic in an SMT environment in which these registers are shared among concurrently running threads. Smartly managing this critical shared resource to ensure that slower threads do not block faster threads’ execution is essential to the advancement of SMT performance. In this paper, an actor–critic style reinforcement learning (RL) algorithm is proposed to dynamically assigning an upper-bound (cap) of the rename registers any thread is allowed to use according to the threads’ real-time demand. In particular, a critic network projects the current Issue Queues (IQ) usage, register file usage, and the cap value to a reward; an actor network is trained to project the current IQ usage and register file usage to the optimal real-time cap value via ascending the instructions per cycle (IPC) gradient within the trajectory distribution. The proposed method differs from the state-of-the-art (Wang and Lin, 2018) as the cap for the rename registers for each thread is adjusted in real-time according to the policy and state transition from self-play. The proposed method shows an improvement in IPC up to 162.8% in a 4-threaded system, 154.8% in a 6-threaded system and up to 101.7% in an 8-threaded system. The code is now available open source at https://github.com/98k-bot/RL-based-SMT-Register-Renaming-Policy. Reinforcement learning Simultaneous multithreading Actor–critic Machine learning Sheng, Victor S. verfasserin (orcid)0000-0003-4960-174X aut Lin, Wei-Ming verfasserin (orcid)0000-0002-9350-6646 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 186 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:186 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz VZ AR 186
allfieldsGer	10.1016/j.eswa.2021.115717 doi (DE-627)ELV055597025 (ELSEVIER)S0957-4174(21)01099-X DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhan, Huixin verfasserin (orcid)0000-0001-8926-1941 aut Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪ 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably long due to many program execution-dependent factors that result in performance degradation. Such an issue becomes even more problematic in an SMT environment in which these registers are shared among concurrently running threads. Smartly managing this critical shared resource to ensure that slower threads do not block faster threads’ execution is essential to the advancement of SMT performance. In this paper, an actor–critic style reinforcement learning (RL) algorithm is proposed to dynamically assigning an upper-bound (cap) of the rename registers any thread is allowed to use according to the threads’ real-time demand. In particular, a critic network projects the current Issue Queues (IQ) usage, register file usage, and the cap value to a reward; an actor network is trained to project the current IQ usage and register file usage to the optimal real-time cap value via ascending the instructions per cycle (IPC) gradient within the trajectory distribution. The proposed method differs from the state-of-the-art (Wang and Lin, 2018) as the cap for the rename registers for each thread is adjusted in real-time according to the policy and state transition from self-play. The proposed method shows an improvement in IPC up to 162.8% in a 4-threaded system, 154.8% in a 6-threaded system and up to 101.7% in an 8-threaded system. The code is now available open source at https://github.com/98k-bot/RL-based-SMT-Register-Renaming-Policy. Reinforcement learning Simultaneous multithreading Actor–critic Machine learning Sheng, Victor S. verfasserin (orcid)0000-0003-4960-174X aut Lin, Wei-Ming verfasserin (orcid)0000-0002-9350-6646 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 186 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:186 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz VZ AR 186
allfieldsSound	10.1016/j.eswa.2021.115717 doi (DE-627)ELV055597025 (ELSEVIER)S0957-4174(21)01099-X DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhan, Huixin verfasserin (orcid)0000-0001-8926-1941 aut Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪ 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably long due to many program execution-dependent factors that result in performance degradation. Such an issue becomes even more problematic in an SMT environment in which these registers are shared among concurrently running threads. Smartly managing this critical shared resource to ensure that slower threads do not block faster threads’ execution is essential to the advancement of SMT performance. In this paper, an actor–critic style reinforcement learning (RL) algorithm is proposed to dynamically assigning an upper-bound (cap) of the rename registers any thread is allowed to use according to the threads’ real-time demand. In particular, a critic network projects the current Issue Queues (IQ) usage, register file usage, and the cap value to a reward; an actor network is trained to project the current IQ usage and register file usage to the optimal real-time cap value via ascending the instructions per cycle (IPC) gradient within the trajectory distribution. The proposed method differs from the state-of-the-art (Wang and Lin, 2018) as the cap for the rename registers for each thread is adjusted in real-time according to the policy and state transition from self-play. The proposed method shows an improvement in IPC up to 162.8% in a 4-threaded system, 154.8% in a 6-threaded system and up to 101.7% in an 8-threaded system. The code is now available open source at https://github.com/98k-bot/RL-based-SMT-Register-Renaming-Policy. Reinforcement learning Simultaneous multithreading Actor–critic Machine learning Sheng, Victor S. verfasserin (orcid)0000-0003-4960-174X aut Lin, Wei-Ming verfasserin (orcid)0000-0002-9350-6646 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 186 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:186 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 54.72 Künstliche Intelligenz VZ AR 186
language	English
source	Enthalten in Expert systems with applications 186 volume:186
sourceStr	Enthalten in Expert systems with applications 186 volume:186
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Reinforcement learning Simultaneous multithreading Actor–critic Machine learning
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Zhan, Huixin @@aut@@ Sheng, Victor S. @@aut@@ Lin, Wei-Ming @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV055597025
language_de	englisch
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author	Zhan, Huixin
spellingShingle	Zhan, Huixin ddc 004 bkl 54.72 misc Reinforcement learning misc Simultaneous multithreading misc Actor–critic misc Machine learning Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪
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topic_title	004 VZ 54.72 bkl Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪ Reinforcement learning Simultaneous multithreading Actor–critic Machine learning
topic	ddc 004 bkl 54.72 misc Reinforcement learning misc Simultaneous multithreading misc Actor–critic misc Machine learning
topic_unstemmed	ddc 004 bkl 54.72 misc Reinforcement learning misc Simultaneous multithreading misc Actor–critic misc Machine learning
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title	Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪
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title_full	Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪
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title_sort	reinforcement learning-based register renaming policy for simultaneous multithreading cpus▪
title_auth	Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪
abstract	Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably long due to many program execution-dependent factors that result in performance degradation. Such an issue becomes even more problematic in an SMT environment in which these registers are shared among concurrently running threads. Smartly managing this critical shared resource to ensure that slower threads do not block faster threads’ execution is essential to the advancement of SMT performance. In this paper, an actor–critic style reinforcement learning (RL) algorithm is proposed to dynamically assigning an upper-bound (cap) of the rename registers any thread is allowed to use according to the threads’ real-time demand. In particular, a critic network projects the current Issue Queues (IQ) usage, register file usage, and the cap value to a reward; an actor network is trained to project the current IQ usage and register file usage to the optimal real-time cap value via ascending the instructions per cycle (IPC) gradient within the trajectory distribution. The proposed method differs from the state-of-the-art (Wang and Lin, 2018) as the cap for the rename registers for each thread is adjusted in real-time according to the policy and state transition from self-play. The proposed method shows an improvement in IPC up to 162.8% in a 4-threaded system, 154.8% in a 6-threaded system and up to 101.7% in an 8-threaded system. The code is now available open source at https://github.com/98k-bot/RL-based-SMT-Register-Renaming-Policy.
abstractGer	Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably long due to many program execution-dependent factors that result in performance degradation. Such an issue becomes even more problematic in an SMT environment in which these registers are shared among concurrently running threads. Smartly managing this critical shared resource to ensure that slower threads do not block faster threads’ execution is essential to the advancement of SMT performance. In this paper, an actor–critic style reinforcement learning (RL) algorithm is proposed to dynamically assigning an upper-bound (cap) of the rename registers any thread is allowed to use according to the threads’ real-time demand. In particular, a critic network projects the current Issue Queues (IQ) usage, register file usage, and the cap value to a reward; an actor network is trained to project the current IQ usage and register file usage to the optimal real-time cap value via ascending the instructions per cycle (IPC) gradient within the trajectory distribution. The proposed method differs from the state-of-the-art (Wang and Lin, 2018) as the cap for the rename registers for each thread is adjusted in real-time according to the policy and state transition from self-play. The proposed method shows an improvement in IPC up to 162.8% in a 4-threaded system, 154.8% in a 6-threaded system and up to 101.7% in an 8-threaded system. The code is now available open source at https://github.com/98k-bot/RL-based-SMT-Register-Renaming-Policy.
abstract_unstemmed	Simultaneous multithreading (SMT) improves the performance of superscalar CPUs by exploiting thread-level parallelism with shared entries for better utilization of resources. A key issue for this out-of-order execution is that the occupancy latency of a physical rename register can be undesirably long due to many program execution-dependent factors that result in performance degradation. Such an issue becomes even more problematic in an SMT environment in which these registers are shared among concurrently running threads. Smartly managing this critical shared resource to ensure that slower threads do not block faster threads’ execution is essential to the advancement of SMT performance. In this paper, an actor–critic style reinforcement learning (RL) algorithm is proposed to dynamically assigning an upper-bound (cap) of the rename registers any thread is allowed to use according to the threads’ real-time demand. In particular, a critic network projects the current Issue Queues (IQ) usage, register file usage, and the cap value to a reward; an actor network is trained to project the current IQ usage and register file usage to the optimal real-time cap value via ascending the instructions per cycle (IPC) gradient within the trajectory distribution. The proposed method differs from the state-of-the-art (Wang and Lin, 2018) as the cap for the rename registers for each thread is adjusted in real-time according to the policy and state transition from self-play. The proposed method shows an improvement in IPC up to 162.8% in a 4-threaded system, 154.8% in a 6-threaded system and up to 101.7% in an 8-threaded system. The code is now available open source at https://github.com/98k-bot/RL-based-SMT-Register-Renaming-Policy.
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title_short	Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪
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Reinforcement learning-based register renaming policy for simultaneous multithreading CPUs▪

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