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NumaGiC: a Garbage Collector for Big Data on Big NUMA Machines

Abstract : On contemporary cache-coherent Non-Uniform Memory Access (ccNUMA) architectures, applications with a large memory footprint suffer from the cost of the garbage collector (GC), because, as the GC scans the reference graph, it makes many remote memory accesses, saturating the interconnect between memory nodes. We address this problem with NumaGiC, a GC with a mostly-distributed design. In order to maximise memory access locality during collection, a GC thread avoids accessing a different memory node, instead notifying a remote GC thread with a message; nonetheless, NumaGiC avoids the drawbacks of a pure distributed design, which tends to decrease parallelism. We compare NumaGiC with Parallel Scavenge and NAPS on two different ccNUMA architectures running on the Hotspot Java Virtual Machine of OpenJDK 7. On Spark and Neo4j, two industry-strength analytics applications, with heap sizes ranging from 160 GB to 350 GB, and on SPECjbb2013 and SPECjbb2005, Numa-GiC improves overall performance by up to 45% over NAPS (up to 94% over Parallel Scavenge), and increases the performance of the collector itself by up to 3.6× over NAPS (up to 5.4× over Parallel Scavenge).
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Contributor : Lokesh Gidra Connect in order to contact the contributor
Submitted on : Wednesday, July 22, 2015 - 7:05:56 PM
Last modification on : Friday, January 21, 2022 - 3:21:48 AM
Long-term archiving on: : Friday, October 23, 2015 - 10:13:37 AM


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Lokesh Gidra, Gaël Thomas, Julien Sopena, Marc Shapiro, Nhan Nguyen. NumaGiC: a Garbage Collector for Big Data on Big NUMA Machines. 20th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), ACM SIGOPS, ACM SIGPLAN, ACM SIGARCH, Mar 2015, Istanbul, Turkey. pp.661-673, ⟨10.1145/2694344.2694361⟩. ⟨hal-01178790⟩



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