A Fast Analysis for Thread-Local Garbage Collection with Dynamic Class Loading

Jones, Richard; King, Andy

doi:10.1109/scam.2005.1

Cited by 19 publications

(10 citation statements)

References 28 publications

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“…Local and newly instantiated objects are allocated in thread-specific heaplets since they are, for high probability, accessed by the thread itself [8,25,19,1,16,2]. Furthermore, heaplets can be collected independently since objects are local and no need to synchronize all mutator threads.…”

Section: Related Workmentioning

confidence: 99%

A study of connected object locality in NUMA heaps

Alnowaiser

2014

Proceedings of the Workshop on Memory Systems Performance and Correctness

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Reference locality is vital to the performance of parallel Garbage Collection (GC) running on Non-Uniform Memory Access (NUMA) machines. A GC thread may trace remotely placed objects that descend from the root set or, for load balance, a GC thread may steal non-local objects from other threads' work lists. Processing distant live objects could introduce contention in the interconnect links between memory nodes and it could increase memory access latency. Researchers have proposed various techniques to improve GC tracing locality. However, few studies attempt to optimize the locality of connected objects in NUMA object graph. In this paper, we study the locality of a rooted subgraph, a unit of object connectivity in the object graph. A rooted subgraph is a set of references containing one root reference, and every reference is reachable from the root. We empirically study the locality of rooted subgraphs of DaCapo and SPECjbb2005 benchmark suites. The results show that more than 80% of objects in a rooted subgraph are located in the same memory node as the root object. We then propose a GC locality optimization that uses the root memory node as a heuristic to guide GC threads processing local objects.

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Section: Related Workmentioning

confidence: 99%

A study of connected object locality in NUMA heaps

Alnowaiser

2014

Proceedings of the Workshop on Memory Systems Performance and Correctness

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“…Static analyses overestimate the number of objects actually shared. Most are conservative in the face of (or ignore) dynamic class loading; Jones & King [15] use an optimistic, and hence more precise, analysis that falls back at run-time to more conservative assumptions should a dynamically loaded class invalidate parts of a prior analysis. Runtime analysis delivers more precise results but requires read or write barriers to detect escape [17].…”

Section: Shared-used and Shared-reachable Objectsmentioning

confidence: 99%

A black-box approach to understanding concurrency in DaCapo

et al. 2012

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Increasing levels of hardware parallelism are one of the main challenges for programmers and implementers of managed runtimes. Any concurrency or scalability improvements must be evaluated experimentally. However, application benchmarks available today may not reflect the highly concurrent applications we anticipate in the future. They may also behave in ways that VM developers do not expect. We provide a set of platform independent concurrency-related metrics and an in-depth observational study of current state of the art benchmarks, discovering how concurrent they really are, how they scale the work and how they synchronise and communicate via shared memory.

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“…Thread-local heaps enforce local access to thread-specific objects [2,7,14,18,28]. New objects are initially allocated in thread-local heaps until they are referenced by non-local objects.…”

Section: Related Workmentioning

confidence: 99%

Topology-Aware Parallelism for NUMA Copying Collectors

Alnowaiser

Singer

2016

Languages and Compilers for Parallel Computing

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Abstract. NUMA-aware parallel algorithms in runtime systems attempt to improve locality by allocating memory from local NUMA nodes. Researchers have suggested that the garbage collector should profile memory access patterns or use object locality heuristics to determine the target NUMA node before moving an object. However, these solutions are costly when applied to every live object in the reference graph. Our earlier research suggests that connected objects represented by the rooted sub-graphs provide abundant locality and they are appropriate for NUMA architecture.In this paper, we utilize the intrinsic locality of rooted sub-graphs to improve parallel copying collector performance. Our new topology-aware parallel copying collector preserves rooted sub-graph integrity by moving the connected objects as a unit to the target NUMA node. In addition, it distributes and assigns the copying tasks to appropriate (i.e. NUMA node local) GC threads. For load balancing, our solution enforces locality on the work-stealing mechanism by stealing from local NUMA nodes only. We evaluated our approach on SPECjbb2013, DaCapo 9.12 and Neo4j. Results show an improvement in GC performance by up to 2.5x speedup and 37 % better application performance.

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A Fast Analysis for Thread-Local Garbage Collection with Dynamic Class Loading

Cited by 19 publications

References 28 publications

A study of connected object locality in NUMA heaps

A study of connected object locality in NUMA heaps

A black-box approach to understanding concurrency in DaCapo

Topology-Aware Parallelism for NUMA Copying Collectors

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