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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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Data-Copying in Generative Models: A Formal Framework
There has been some recent interest in detecting and addressing memorization of training data by deep neural networks. A formal framework for memorization in generative models, called “data-copying” was proposed by Meehan et. al (2020). We build upon their work to show that their framework may fail to detect certain kinds of blatant memorization. Motivated by this and the theory of non-parametric methods, we provide an alternative definition of data-copying that applies more locally. We provide a method to detect data-copying, and provably show that it works with high probability when enough data is available. We also provide lower bounds that characterize the sample requirement for reliable detection.
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- Award ID(s):
- 1804829
- PAR ID:
- 10475787
- Publisher / Repository:
- Journal of Machine Learning Research
- Date Published:
- Journal Name:
- Proceedings of Machine Learning Research
- ISSN:
- 2640-3498
- Format(s):
- Medium: X
- Location:
- Hawaii
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
