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检索条件"主题词=distributed-memory graph analytics"

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Phoenix: A Substrate for Resilient distributed graph analytics 19

Phoenix: A Substrate for Resilient Distributed Graph Analyti...

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24th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Dathathri, Roshan Gill, Gurbinder Loc Hoang Pingali, Keshav Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781450362405

This paper presents Phoenix, a communication and synchronization substrate that implements a novel protocol for recovering from fail-stop faults when executing graph analytics applications on distributed-memory machines. The standard recovery technique in this space is checkpointing, which rolls back the state of the entire computation to a state that existed before the fault occurred. The insight behind Phoenix is that this is not necessary since it is sufficient to continue the computation from a state that will ultimately produce the correct result. We show that for graph analytics applications, the necessary state adjustment can be specified easily by the programmer using a thin API supported by Phoenix. Phoenix has no observable overhead during fault-free execution, and it is resilient to any number of faults while guaranteeing that the correct answer will be produced at the end of the computation. This is in contrast to other systems in this space which may either have overheads even during fault-free execution or produce only approximate answers when faults occur during execution. We incorporated Phoenix into D-Galois, the state-of-the-art distributed graph analytics system, and evaluated it on two production clusters. Our evaluation shows that in the absence of faults, Phoenix is similar to 24x faster than graphX, which provides fault tolerance using the Spark system. Phoenix also outperforms the traditional checkpoint-restart technique implemented in D-Galois: in fault-free execution, Phoenix has no observable overhead, while the checkpointing technique has 31% overhead. Furthermore, Phoenix mostly outperforms checkpointing when faults occur, particularly in the common case when only a small number of hosts fail simultaneously.

关键词： fault tolerance distributed-memory graph analytics big data self-stabilizing algorithms

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Gluon: A Communication-Optimizing Substrate for distributed Heterogeneous graph analytics 2018

Gluon: A Communication-Optimizing Substrate for Distributed ...

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39th ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI)

作者： Dathathri, Roshan Gill, Gurbinder Loc Hoang Hoang-Vu Dang Brooks, Alex Dryden, Nikoli Snir, Marc Pingali, Keshav Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Univ Illinois Dept Comp Sci Champaign IL USA

ISBN: (纸本)9781450356985

This paper introduces a new approach to building distributed-memory graph analytics systems that exploits heterogeneity in processor types (CPU and GPU), partitioning policies, and programming models. The key to this approach is Gluon, a communication-optimizing substrate. Programmers write applications in a shared-memory programming system of their choice and interface these applications with Gluon using a lightweight API. Gluon enables these programs to run on heterogeneous clusters and optimizes communication in a novel way by exploiting structural and temporal invariants of graph partitioning policies. To demonstrate Gluon's ability to support different programming models, we interfaced Gluon with the Galois and Ligra shared-memory graph analytics systems to produce distributed-memory versions of these systems named D-Galois and D-Ligra, respectively. To demonstrate Gluon's ability to support heterogeneous processors, we interfaced Gluon with IrGL, a state-of-the-art single-GPU system for graph analytics, to produce D-IrGL, the first multi-GPU distributed-memory graph analytics system. Our experiments were done on CPU clusters with up to 256 hosts and roughly 70,000 threads and on multi-GPU clusters with up to 64 GPUs. The communication optimizations in Gluon improve end-to-end application execution time by similar to 2.6x on the average. D-Galois and D-IrGL scale well and are faster than Gemini, the state-of-the-art distributed CPU graph analytics system, by factors of similar to 3.9x and similar to 4.9x, respectively, on the average.

关键词： distributed-memory graph analytics communication optimizations heterogeneous architectures GPUs big data

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