Software-based contention management for efficient compare-and-swap operations
SUMMARYMany concurrent data‐structure implementations – both blocking and non‐blocking – use the well‐known compare‐and‐swap (CAS) operation, supported in hardware by most modern multiprocessor architectures, for inter‐thread synchronization. A key weakness of the CAS operation is its performance in...
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25.09.2014
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| Abstract | SUMMARYMany concurrent data‐structure implementations – both blocking and non‐blocking – use the well‐known compare‐and‐swap (CAS) operation, supported in hardware by most modern multiprocessor architectures, for inter‐thread synchronization. A key weakness of the CAS operation is its performance in the presence of memory contention. When multiple threads concurrently attempt to apply CAS operations to the same shared variable, at most a single thread will succeed in changing the shared variable's value and the CAS operations of all other threads will fail. Moreover, significant degradation in performance occurs when variables manipulated by CAS become contention ‘hot spots’, because failed CAS operations congest the interconnect and memory devices and slow down successful CAS operations. In this work, we study the following question: can software‐based contention management improve the efficiency of hardware‐provided CAS operations? In other words, can a software contention management layer, encapsulating invocations of hardware CAS instructions, improve the performance of CAS‐based concurrent data structures? To address this question, we conduct what is, to the best of our knowledge, the first study on the impact of contention management algorithms on the efficiency of the CAS operation. We implemented several Java classes, that extend Java's AtomicReference class, and encapsulate calls to the native CAS instruction with simple contention management mechanisms tuned for different hardware platforms. A key property of our algorithms is the support for an almost‐transparent interchange with Java's AtomicReference objects, used in implementations of concurrent data structures. We evaluate the impact of these algorithms on both a synthetic micro‐benchmark and on CAS‐based concurrent implementations of widely‐used data structures such as stacks and queues. Our performance evaluation establishes that lightweight software‐based contention management support can greatly improve performance under medium and high contention levels while typically incurring only small overhead under low contention. In some cases, applying efficient contention management for CAS operations used by a simpler data‐structure implementation yields better results than highly optimized implementations of the same data structure that use native CAS operations directly. Copyright © 2014 John Wiley & Sons, Ltd. |
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| AbstractList | Many concurrent data‐structure implementations – both blocking and non‐blocking – use the well‐known compare‐and‐swap (CAS) operation, supported in hardware by most modern multiprocessor architectures, for inter‐thread synchronization. A key weakness of the CAS operation is its performance in the presence of memory contention. When multiple threads concurrently attempt to apply CAS operations to the same shared variable, at most a single thread will succeed in changing the shared variable's value and the CAS operations of all other threads will fail. Moreover, significant degradation in performance occurs when variables manipulated by CAS become contention ‘hot spots’, because failed CAS operations congest the interconnect and memory devices and slow down successful CAS operations. In this work, we study the following question: can software‐based contention management improve the efficiency of hardware‐provided CAS operations? In other words, can a software contention management layer, encapsulating invocations of hardware CAS instructions, improve the performance of CAS‐based concurrent data structures? To address this question, we conduct what is, to the best of our knowledge, the first study on the impact of contention management algorithms on the efficiency of the CAS operation. We implemented several Java classes, that extend Java's AtomicReference class, and encapsulate calls to the native CAS instruction with simple contention management mechanisms tuned for different hardware platforms. A key property of our algorithms is the support for an almost‐transparent interchange with Java's AtomicReference objects, used in implementations of concurrent data structures. We evaluate the impact of these algorithms on both a synthetic micro‐benchmark and on CAS‐based concurrent implementations of widely‐used data structures such as stacks and queues. Our performance evaluation establishes that lightweight software‐based contention management support can greatly improve performance under medium and high contention levels while typically incurring only small overhead under low contention. In some cases, applying efficient contention management for CAS operations used by a simpler data‐structure implementation yields better results than highly optimized implementations of the same data structure that use native CAS operations directly. Copyright © 2014 John Wiley & Sons, Ltd. SUMMARYMany concurrent data‐structure implementations – both blocking and non‐blocking – use the well‐known compare‐and‐swap (CAS) operation, supported in hardware by most modern multiprocessor architectures, for inter‐thread synchronization. A key weakness of the CAS operation is its performance in the presence of memory contention. When multiple threads concurrently attempt to apply CAS operations to the same shared variable, at most a single thread will succeed in changing the shared variable's value and the CAS operations of all other threads will fail. Moreover, significant degradation in performance occurs when variables manipulated by CAS become contention ‘hot spots’, because failed CAS operations congest the interconnect and memory devices and slow down successful CAS operations. In this work, we study the following question: can software‐based contention management improve the efficiency of hardware‐provided CAS operations? In other words, can a software contention management layer, encapsulating invocations of hardware CAS instructions, improve the performance of CAS‐based concurrent data structures? To address this question, we conduct what is, to the best of our knowledge, the first study on the impact of contention management algorithms on the efficiency of the CAS operation. We implemented several Java classes, that extend Java's AtomicReference class, and encapsulate calls to the native CAS instruction with simple contention management mechanisms tuned for different hardware platforms. A key property of our algorithms is the support for an almost‐transparent interchange with Java's AtomicReference objects, used in implementations of concurrent data structures. We evaluate the impact of these algorithms on both a synthetic micro‐benchmark and on CAS‐based concurrent implementations of widely‐used data structures such as stacks and queues. Our performance evaluation establishes that lightweight software‐based contention management support can greatly improve performance under medium and high contention levels while typically incurring only small overhead under low contention. In some cases, applying efficient contention management for CAS operations used by a simpler data‐structure implementation yields better results than highly optimized implementations of the same data structure that use native CAS operations directly. Copyright © 2014 John Wiley & Sons, Ltd. Many concurrent data-structure implementations - both blocking and non-blocking - use the well-known compare-and-swap (CAS) operation, supported in hardware by most modern multiprocessor architectures, for inter-thread synchronization. A key weakness of the CAS operation is its performance in the presence of memory contention. When multiple threads concurrently attempt to apply CAS operations to the same shared variable, at most a single thread will succeed in changing the shared variable's value and the CAS operations of all other threads will fail. Moreover, significant degradation in performance occurs when variables manipulated by CAS become contention 'hot spots', because failed CAS operations congest the interconnect and memory devices and slow down successful CAS operations. In this work, we study the following question: can software-based contention management improve the efficiency of hardware-provided CAS operations? In other words, can a software contention management layer, encapsulating invocations of hardware CAS instructions, improve the performance of CAS-based concurrent data structures? To address this question, we conduct what is, to the best of our knowledge, the first study on the impact of contention management algorithms on the efficiency of the CAS operation. We implemented several Java classes, that extend Java's AtomicReference class, and encapsulate calls to the native CAS instruction with simple contention management mechanisms tuned for different hardware platforms. A key property of our algorithms is the support for an almost-transparent interchange with Java's AtomicReference objects, used in implementations of concurrent data structures. We evaluate the impact of these algorithms on both a synthetic micro-benchmark and on CAS-based concurrent implementations of widely-used data structures such as stacks and queues. Our performance evaluation establishes that lightweight software-based contention management support can greatly improve performance under medium and high contention levels while typically incurring only small overhead under low contention. In some cases, applying efficient contention management for CAS operations used by a simpler data-structure implementation yields better results than highly optimized implementations of the same data structure that use native CAS operations directly. Copyright copyright 2014 John Wiley & Sons, Ltd. |
| Author | Hendler, Danny Dice, Dave Mirsky, Ilya |
| Author_xml | – sequence: 1 givenname: Dave surname: Dice fullname: Dice, Dave organization: Oracle Labs – sequence: 2 givenname: Danny surname: Hendler fullname: Hendler, Danny email: Correspondence to: Danny Hendler, Ben-Gurion University of the Negev and Telekom Innovation Laboratories., hendlerd@cs.bgu.ac.il organization: Ben-Gurion University of the Negev and Telekom Innovation Laboratories – sequence: 3 givenname: Ilya surname: Mirsky fullname: Mirsky, Ilya organization: Ben-Gurion University of the Negev and Telekom Innovation Laboratories |
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| References_xml | – reference: Scherer WN, III, Lea D, Scott ML. Scalable synchronous queues. Communications of the ACM 2009; 52(5):100-111. – reference: Anderson TE. The performance of spin lock alternatives for shared-memory multiprocessors. IEEE Transactions on Parallel and Distributed Systems 1990; 1(1):6-16. (Available from: http://dx.doi.org/10.1109/71.80120). – reference: Herlihy M, Lim BH, Shavit N. Scalable concurrent counting. ACM Transactions on Computer Systems 1995; 13(4):343-364. – reference: Herlihy MP. Wait-free synchronization. ACM Transactions On Programming Languages and Systems 1991; 13(1):123-149. – reference: Ladan-Mozes E, Shavit N. An optimistic approach to lock-free FIFO queues. Distributed Computing 2008; 20(5):323-341. – reference: Herlihy M, Shavit N. The Art of Multiprocessor Programming. Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, 2008. – reference: Intel Corporation. Intel Itanium Architecture Software Developer's Manual, 2006. – reference: Herlihy M, Shavit N, Waarts O. Linearizable counting networks. Distributed Computing 1996; 9(4):193-203. – reference: Hendler D, Shavit N, Yerushalmi L. A scalable lock-free stack algorithm. Journal of Parallel and Distributed Computing 2010; 70(1):1-12. – reference: Sun Microsystems. Ultrasparc Architecture 2005, Draft D0.9.2, 2008. – reference: Motorola. MC68000 Programmer's Reference Manual, 1992. – reference: Mellor-Crummey JM, Scott ML. Algorithms for scalable synchronization on shared-memory multiprocessors. ACM Transactions on Computer Systems (TOCS) 1991; 9(1):21-65. – reference: Attiya H, Welch J. Distributed Computing: Fundamentals, Simulations and Advanced Topics (2nd edn). John Wiley & Sons Interscience, 2004. – reference: Shalev O, Shavit N. Split-ordered lists: lock-free extensible hash tables. Journal of the ACM 2006; 53(3):379-405. – reference: IBM. 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Operating Systems Review 2010; 44(3):102-109. – volume: 70 start-page: 1 issue: 1 year: 2010 end-page: 12 article-title: A scalable lock‐free stack algorithm publication-title: Journal of Parallel and Distributed Computing – start-page: 16 year: 2011 end-page: 31 – year: 1983 – volume: 44 start-page: 102 issue: 3 year: 2010 end-page: 109 article-title: Scalable concurrent hash tables via relativistic programming publication-title: Operating Systems Review – start-page: 41:1 year: 2011 end-page: 41:11 – start-page: 355 year: 2010 end-page: 364 – start-page: 258 year: 2005 end-page: 264 – volume: 52 start-page: 100 issue: 5 year: 2009 end-page: 111 article-title: Scalable synchronous queues publication-title: Communications of the ACM – volume: 9 start-page: 21 issue: 1 year: 1991 end-page: 65 article-title: Algorithms for scalable synchronization on shared‐memory multiprocessors publication-title: ACM Transactions on Computer Systems (TOCS) – volume: 1 start-page: 6 issue: 1 year: 1990 end-page: 16 article-title: The performance of spin lock alternatives for shared‐memory multiprocessors publication-title: IEEE Transactions on Parallel and Distributed Systems – start-page: 302 year: 2010 end-page: 317 – start-page: 92 year: 2003 end-page: 101 – volume: 53 start-page: 379 issue: 3 year: 2006 end-page: 405 article-title: Split‐ordered lists: lock‐free extensible hash tables publication-title: Journal of the ACM – year: 1992 – volume: 9 start-page: 193 issue: 4 year: 1996 end-page: 203 article-title: Linearizable counting networks publication-title: Distributed Computing – start-page: 257 year: 2012 end-page: 266 – start-page: 43 year: 2013 end-page: 52 – start-page: 79 year: 2010 end-page: 93 – volume: 20 start-page: 323 issue: 5 year: 2008 end-page: 341 article-title: An optimistic approach to lock‐free FIFO queues publication-title: Distributed Computing – start-page: 22 year: 2002 end-page: 30 – start-page: 267 year: 2006 end-page: 275 – year: 2008 – year: 2006 – year: 2004 – start-page: 253 year: 2005 end-page: 262 – start-page: 151 year: 2012 end-page: 160 – start-page: 151 year: 2010 end-page: 162 – volume: 13 start-page: 123 issue: 1 year: 1991 end-page: 149 article-title: Wait‐free synchronization publication-title: ACM Transactions On Programming Languages and Systems – start-page: 475 year: 2011 end-page: 488 – volume: 13 start-page: 343 issue: 4 year: 1995 end-page: 364 article-title: Scalable concurrent counting publication-title: ACM Transactions on Computer Systems – year: 1993 – start-page: 350 year: 2008 end-page: 364 – start-page: 595 year: 2013 end-page: 606 – ident: e_1_2_9_19_1 doi: 10.1007/978-3-540-87779-0_24 – ident: e_1_2_9_20_1 doi: 10.1145/1147954.1147958 – volume-title: MC68000 Programmer's Reference Manual year: 1992 ident: e_1_2_9_25_1 – ident: e_1_2_9_29_1 doi: 10.1145/872035.872048 – ident: e_1_2_9_11_1 doi: 10.1007/s00446-007-0050-0 – ident: e_1_2_9_28_1 – ident: e_1_2_9_6_1 doi: 10.1145/2370036.2145849 – volume-title: IBM System/370 Extended Architecture, Principles of Operation, Publication No. SA22‐7085 year: 1983 ident: e_1_2_9_36_1 – ident: e_1_2_9_21_1 doi: 10.1145/1842733.1842750 – ident: e_1_2_9_12_1 doi: 10.1145/248052.248106 – volume-title: The Art of Multiprocessor Programming year: 2008 ident: e_1_2_9_35_1 – ident: e_1_2_9_8_1 doi: 10.1145/1810479.1810540 – ident: e_1_2_9_26_1 doi: 10.1145/2486159.2486182 – ident: e_1_2_9_22_1 doi: 10.1145/114005.102808 – ident: e_1_2_9_3_1 doi: 10.1145/210223.210225 – ident: e_1_2_9_16_1 doi: 10.1007/978-3-642-24100-0_44 – ident: e_1_2_9_2_1 doi: 10.1002/0471478210 – ident: e_1_2_9_9_1 doi: 10.1007/978-3-642-15763-9_8 – ident: e_1_2_9_17_1 doi: 10.1145/2312005.2312035 – volume-title: Ultrasparc Architecture 2005, Draft D0.9.2 year: 2008 ident: e_1_2_9_24_1 – ident: e_1_2_9_34_1 doi: 10.1109/ICNP.1998.723722 – volume-title: Intel Itanium Architecture Software Developer's Manual year: 2006 ident: e_1_2_9_23_1 – ident: e_1_2_9_30_1 doi: 10.1145/1073814.1073863 – ident: e_1_2_9_10_1 doi: 10.1145/1506409.1506431 – ident: e_1_2_9_14_1 doi: 10.1016/j.jpdc.2009.08.011 – ident: e_1_2_9_13_1 doi: 10.1145/1073970.1074013 – ident: e_1_2_9_18_1 doi: 10.1145/2063384.2063439 – ident: e_1_2_9_31_1 doi: 10.1109/71.80120 – ident: e_1_2_9_15_1 doi: 10.1007/978-3-642-15291-7_16 – ident: e_1_2_9_33_1 – ident: e_1_2_9_4_1 doi: 10.1007/s004460050019 – ident: e_1_2_9_27_1 doi: 10.1007/978-3-642-40047-6_60 – ident: e_1_2_9_7_1 doi: 10.1007/978-3-642-17653-1_23 – ident: e_1_2_9_5_1 doi: 10.1007/978-3-642-24100-0_2 – ident: e_1_2_9_32_1 doi: 10.1145/103727.103729 |
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| Snippet | SUMMARYMany concurrent data‐structure implementations – both blocking and non‐blocking – use the well‐known compare‐and‐swap (CAS) operation, supported in... Many concurrent data‐structure implementations – both blocking and non‐blocking – use the well‐known compare‐and‐swap (CAS) operation, supported in hardware by... Many concurrent data-structure implementations - both blocking and non-blocking - use the well-known compare-and-swap (CAS) operation, supported in hardware by... |
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| SubjectTerms | Algorithms Compare-and-swap concurrent algorithms contention management Data storage Data structures Hardware Management Mathematical models Performance enhancement Synchronism |
| Title | Software-based contention management for efficient compare-and-swap operations |
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