NumaGiC: A garbage collector for big data on big NUMA machines
Paper i proceeding, 2015

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).

Multicore

Garbage collection

NUMA

Författare

L. Gidra

Université Pierre et Marie Curie (UPMC)

G. Thomas

Services repartis, architecture modelisation validation administration de reseaux

J. Sopena

Université Pierre et Marie Curie (UPMC)

M. Shapiro

Université Pierre et Marie Curie (UPMC)

Dang Nhan Nguyen

Chalmers, Data- och informationsteknik, Nätverk och system

20th International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2015; Istanbul; Turkey; 14 March 2015 through 18 March 2015

661-673

Ämneskategorier

Datavetenskap (datalogi)

DOI

10.1145/2694344.2694361

ISBN

9781450328357

Mer information

Senast uppdaterat

2018-05-29