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Managed programming languages including Java and Scala are very popular for data analytics and mobile applications. However, they often face challenging issues due to the overhead caused by the automatic memory management to detect and reclaim free available memory. It has been observed that during their Garbage Collection (GC), excessively long pauses can account for up to 40 % of the total execution time. Therefore, mitigating the GC overhead has been an active research topic to satisfy today's application requirements. This paper proposes a new technique called SwapVA to improve data copying in the copying/moving phases of GCs and reduce the GC pause time, thereby mitigating the issue of GC overhead. Our contribution is twofold. First, a SwapVA system call is introduced as a zero-copy technique to accelerate the GC copying/moving phase. Second, for the demonstration of its effectiveness, we have integrated SwapVA into SVAGC as an implementation of scalable Full GC on multi-core systems. Based on our results, the proposed solutions can dramatically reduce the GC pause in applications with large objects by as much as 70.9% and 97%, respectively, in the Sparse.large/4 (one quarter of the default input size) and Sigverify benchmarks.
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Towards understanding the costs of avoiding out-of-thin-air results
Eliminating so-called “out-of-thin-air” (OOTA) results is an open problem with many existing programming language memory models including Java, C, and C++. OOTA behaviors are problematic in that they break both formal and informal modular reasoning about program behavior. Defining memory model semantics that are easily understood, allow existing optimizations, and forbid OOTA results remains an open problem. This paper explores two simple solutions to this problem that forbid OOTA results. One solution is targeted towards C/C++-like memory models in which racing operations are explicitly labeled as atomic operations and a second solution is targeted towards Java-like languages in which all memory operations may create OOTA executions. Our solutions provide a per-candidate execution criterion that makes it possible to examine a single execution and determine whether the memory model permits the execution. We implemented and evaluated both solutions in the LLVM compiler framework. Our results show that on an ARMv8 processor the first solution has no overhead on average and a maximum overhead of 6.3% on 43 concurrent data structures, and that the second solution has an average overhead of 3.1% and a maximum overhead of 17.6% on the SPEC CPU2006 C/C++ benchmarks.
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- PAR ID:
- 10605182
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Programming Languages
- Volume:
- 2
- Issue:
- OOPSLA
- ISSN:
- 2475-1421
- Format(s):
- Medium: X Size: p. 1-29
- Size(s):
- p. 1-29
- Sponsoring Org:
- National Science Foundation
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