Characterizing and optimizing hotspot parallel garbage collection on multicore systems

Suo, Kun; Rao, Jia; Jiang, Hong; Srisa-an, Witawas

doi:10.1145/3190508.3190512

Cited by 14 publications

(4 citation statements)

References 34 publications

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“…If the CAS instruction succeeds, the GC thread will install the forwarding pointer by storing the new address of an object to the value field of the entry (Line 31-32). During scanning, if the GC thread finds that the forwarding pointer has been installed by others, it will directly return with the value of the pointer (Line [35][36][37][38][39].…”

Section: Header Mapmentioning

confidence: 99%

“…Gidra et al [14][15][16] introduce NUMA-aware optimizations into GC to reduce expensive cross-node memory access and improve the GC performance. Suo et al [37] uncover the mismatch between the JVM synchronization protocol and the OS scheduler. Qian et al [33] refine the work-stealing algorithm to improve the success rate and load balance.…”

Section: Gc Optimizationsmentioning

confidence: 99%

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Bridging the performance gap for copy-based garbage collectors atop non-volatile memory

Yang

Chen

et al. 2021

Proceedings of the Sixteenth European Conference on Computer Systems

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Non-volatile memory (NVM) is expected to revolutionize the memory hierarchy with not only non-volatility but also large capacity and power efficiency. Memory-intensive applications, which are often written in managed languages like Java, would run atop NVM for better cost-efficiency. Unfortunately, such applications may suffer from performance slowdown due to the unmanaged performance gap between DRAM and NVM. This paper studies the performance of a series of Java applications atop NVM and uncovers that the copy-based garbage collection (GC), the mainstream GC algorithm, is an NVM-unfriendly component in JVM. GC becomes a severe performance bottleneck especially when memory resource is scarce. To this end, this paper analyzes the memory behavior of copy-based GC and uncovers that its inappropriate usage on NVM bandwidth is the main reason for its performance slowdown. This paper thus proposes two NVM-aware optimizations: write cache and header map, to effectively manage the limited NVM bandwidth. It further improves the GC performance with hardware instructions like non-temporal memory accesses and prefetching. We have implemented the optimizations on two mainstream copy-based garbage collectors in OpenJDK. Evaluation with various memory-intensive applications shows that our optimizations can improve the GC time, application execution time, application tail latency by up to 2.69×, 11.0%, and 5.09×, respectively. CCS Concepts: • Software and its engineering → Runtime environments; • Hardware → Memory and dense storage.

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Section: Header Mapmentioning

confidence: 99%

Section: Gc Optimizationsmentioning

confidence: 99%

Bridging the performance gap for copy-based garbage collectors atop non-volatile memory

Yang

Chen

et al. 2021

Proceedings of the Sixteenth European Conference on Computer Systems

View full text Add to dashboard Cite

show abstract

“…As mentioned in the survey [31], researchers begin to optimize existing garbage collectors [53,41,50,30,36] or design new garbage collectors [44,32] for big data applications. For example, Suo et al [50] proposed dynamic GC load balancing strategy to optimize the concurrency of Parallel GC in multicore environment. NG2C [32] extends G1 collector to reduce the time-consuming object copy between generations.…”

Section: Related Workmentioning

confidence: 99%

An experimental evaluation of garbage collectors on big data applications

Guo

Dou

et al. 2019

Proc. VLDB Endow.

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Popular big data frameworks, ranging from Hadoop MapReduce to Spark, rely on garbage-collected languages, such as Java and Scala. Big data applications are especially sensitive to the effectiveness of garbage collection (i.e., GC), because they usually process a large volume of data objects that lead to heavy GC overhead. Lacking indepth understanding of GC performance has impeded performance improvement in big data applications. In this paper, we conduct the first comprehensive evaluation on three popular garbage collectors, i.e., Parallel, CMS, and G1, using four representative Spark applications. By thoroughly investigating the correlation between these big data applications' memory usage patterns and the collectors' GC patterns, we obtain many findings about GC inefficiencies. We further propose empirical guidelines for application developers, and insightful optimization strategies for designing big-datafriendly garbage collectors.

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“…Największą część badań skierowanych w problematykę odśmiecania pamięci były prace porównawcze. W składzie literatury, która pozwoliła przyjrzeć się bliżej problematyce odzyskiwania pamięci znalazły się prace porównujące wydajność konkretnych algorytmów i ich wpływ na działanie aplikacji bazując na predefiniowanych testach wzorcowych [2,4,5,6,7,8,9]. Przyjrzano się także pracom autorów implementacji odzyskiwaczy pamięci, w których porównywano ich osiągnięcia względem istniejących już algorytmów [3,11].…”

Section: Przegląd Literaturyunclassified

A performance comparison of garbage collector algorithms in Java Virtual Machine

Kopeć

Smółka

2019

jcsi

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In programming languages with automatic memory management garbage collection plays an important role of cleaning unused memory. Garbage collection algorithms have been developed for many years and aim to maximize the application’s performance. This paper presents and compares a performance of five garbage collection algorithms present in current version of Java 12 in three applications with different object lifetime span. The analysis covered the system responsiveness, garbage collector workload and application throughput at high application load.

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Characterizing and optimizing hotspot parallel garbage collection on multicore systems

Cited by 14 publications

References 34 publications

Bridging the performance gap for copy-based garbage collectors atop non-volatile memory

Bridging the performance gap for copy-based garbage collectors atop non-volatile memory

An experimental evaluation of garbage collectors on big data applications

A performance comparison of garbage collector algorithms in Java Virtual Machine

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