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检索条件"主题词=data-graph computations"

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Executing Dynamic data-graph computations Deterministically Using Chromatic Scheduling

Executing Dynamic Data-Graph Computations Deterministically ...

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26th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)

作者： Kaler, Tim Hasenplaugh, William Schardl, Tao B. Leiserson, Charles E. MIT Comp Sci & Artificial Intelligence Lab 32 Vassar St Cambridge MA 02139 USA

ISBN: (纸本)9781450328210

A data-graph computation - popularized by such programming systems as Galois, Pregel, graphLab, Powergraph, and graphChi - is an algorithm that performs local updates on the vertices of a graph. During each round of a data-graph computation, an update function atomically modifies the data associated with a vertex as a function of the vertex's prior data and that of adjacent vertices. A dynamic data-graph computation updates only an active subset of the vertices during a round, and those updates determine the set of active vertices for the next round. This paper introduces PRISM, a chromatic-scheduling algorithm for executing dynamic data-graph computations. PRISM uses a vertex-coloring of the graph to coordinate updates performed in a round, precluding the need for mutual-exclusion locks or other nondeterministic data synchronization. A multibag data structure is used by PRISM to maintain a dynamic set of active vertices as an unordered set partitioned by color. We analyze PRISM using work-span analysis. Let G = (V,E) be a degree-Delta graph colored with colors, and suppose that Q subset of V is the set of active vertices in a round. Define size(Q) = vertical bar Q vertical bar+ Sigma (v is an element of q) deg (v) which is proportional to the space required to store the vertices of Q using a sparsegraph layout. We show that a P-processor execution of PRISM performs updates in Q using O(chi(lg(Q/chi) + lg Delta D) + lgP) span and Theta(size(Q)+ chi + P) work. These theoretical guarantees are matched by good empirical performance. We modified graphLab to incorporate PRISM and studied seven application benchmarks on a 12-core multicore machine. PRISM executes the benchmarks 1:2-2:1 times faster than graphLab's nondeterministic lock-based scheduler while providing deterministic behavior. This paper also presents PRISM-R, a variation of PRISM that executes dynamic data-graph computations deterministically even when updates modify global variables with associative operat

关键词： data-graph computations multicore multithreading parallel programming chromatic scheduling determinism scheduling work stealing

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Executing dynamic data-graph computations deterministically using chromatic scheduling 14

Executing dynamic data-graph computations deterministically ...

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Proceedings of the 26th ACM symposium on Parallelism in algorithms and architectures

作者： Tim Kaler William Hasenplaugh Tao B. Schardl Charles E. Leiserson MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781450328210

A data-graph computation — popularized by such programming systems as Galois, Pregel, graphLab, Powergraph, and graphChi — is an algorithm that performs local updates on the vertices of a graph. During each round of a data-graph computation, an update function atomically modifies the data associated with a vertex as a function of the vertex's prior data and that of adjacent vertices. A dynamic data-graph computation updates only an active subset of the vertices during a round, and those updates determine the set of active vertices for the next *** paper introduces PRISM, a chromatic-scheduling algorithm for executing dynamic data-graph computations. PRISM uses a vertex-coloring of the graph to coordinate updates performed in a round, precluding the need for mutual-exclusion locks or other nondeterministic data synchronization. A multibag data structure is used by PRISM to maintain a dynamic set of active vertices as an unordered set partitioned by color. We analyze PRISM using work-span analysis. Let G=(V,E) be a degree-Δ graph colored with Χ colors, and suppose that Q⊆V is the set of active vertices in a round. Define size(Q)=[Q] + Σv∈Qdeg(v), which is proportional to the space required to store the vertices of Q using a sparse-graph layout. We show that a P-processor execution of PRISM performs updates in Q using O(Χ(lg (Q/Χ)+lgΔ)+ lgP) span and Θ(size(Q)+Χ+P) work. These theoretical guarantees are matched by good empirical performance. We modified graphLab to incorporate PRISM and studied seven application benchmarks on a 12-core multicore machine. PRISM executes the benchmarks 1.2–2.1 times faster than graphLab's nondeterministic lock-based scheduler while providing deterministic *** paper also presents PRISM-R, a variation of PRISM that executes dynamic data-graph computations deterministically even when updates modify global variables with associative operations. PRISM-R satisfies the same theoretical bounds as PRISM, but its implementation is

关键词： determinism reducers multithreading parallel programming work stealing data-graph computations chromatic scheduling multicore

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Laika: Efficient In-Place Scheduling for 3D Mesh graph computations 18

Laika: Efficient In-Place Scheduling for 3D Mesh Graph Compu...

引用

30th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)

作者： Gruevski, Predrag Hasenplaugh, William Lugato, David Thomas, James J. Kensho Technol Inc Cambridge MA 02138 USA MIT CSAIL Cambridge MA 02139 USA CEA Cesta Le Barp France CS Stanford Stanford CA USA

ISBN: (纸本)9781450357999

Scientific computing problems are frequently solved using data-graph computations - algorithms that perform local updates on application-specific data associated with vertices of a graph, over many time steps. The data-graph in such computations is commonly a mesh graph, where vertices have positions in 3D space, and edges connect physically nearby vertices. A scheduler controls the parallel execution of the algorithm. Two classes of parallel schedulers exist: double-buffering and in-place. Double-buffering schedulers do not incur synchronization overheads due to an absence of read-write conflicts, but require two copies of the vertices, as well as a higher iteration count due to a slower convergence rate. computations for which this difference in convergence rate is significant (e.g., multigrid method) are frequently performed using an in-place scheduler, which incurs synchronization overheads to avoid read-write conflicts on the single copy of vertex data. We present LAIKA, a deterministic in-place scheduler we created using a principled three-step design strategy for high-performance schedulers. LAIKA reorders the input graph using a Hilbert space-filling curve to improve cache locality and minimizes parallel coordination overhead by explicitly curbing excess execution parallelism. Consequently, Laika has significantly lower scheduling overhead than alternative in-place schedulers and is even faster per iteration than the parallel double-buffered implementation on a reordered input graph. We derive an improved bound on the expected number of cache misses incurred during a traversal of a graph reordered using a space-filling curve. We also prove that on a mesh graph G = (V, E), Laika performs O(vertical bar V vertical bar+vertical bar E vertical bar) total work and achieves linear expected speedup with P = O(vertical bar V vertical bar/log(2)vertical bar V vertical bar) workers. On 48 cores, LAIKA yields 38.4x parallel speedup and empirically fares well against co

关键词： data-graph computations multithreading parallel programming scheduling work-stealing cache locality

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