CS219-GraphApplications

CS219-GraphApplications - Parallel Combinatorial BLAS and...

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1 Parallel Combinatorial BLAS and Applications in Graph Computations Aydın Buluç John R. Gilbert University of California, Santa Barbara Adapted from talks at SIAM conferences
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2 By analogy to numerical linear algebra, What would the combinatorial BLAS look like? Primitives for Graph Computations BLAS 3 BLAS 2 BLAS 1 BLAS 3 (n-by-n matrix-matrix multiply) BLAS 2 (n-by-n matrix-vector multiply) BLAS 1 (sum of scaled n-vectors) Peak
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3 Real-World Graphs Properties: Huge (billions of vertices/edges) Very sparse (typically m = O(n)) Scale-free [maybe] Community structure [maybe] Examples: World-wide web Science citation graphs Online social networks
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4 What Kinds of Computations? Some are inherently latency-bound. → S-T connectivity Many graph mining algorithms are computationally intensive. → Graph clustering → Centrality computations Huge Graphs Expensive Kernels + Massive Parallelism Very Sparse Graphs Sparse Data Structures (Matrices)
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The Case for Sparse Matrices Many irregular applications contain su cient coarse- grained parallelism that can ONLY be exploited using abstractions at proper level. Traditional graph computations Graphs in the language of linear algebra Data driven. Unpredictable communication patterns Fixed communication patterns. Overlapping opportunities Irregular and unstructured. Poor locality of reference Operations on matrix blocks. Exploits memory hierarchy
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This note was uploaded on 12/27/2011 for the course CMPSC 290h taught by Professor Chong during the Fall '09 term at UCSB.

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CS219-GraphApplications - Parallel Combinatorial BLAS and...

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