Browsing by Subject "Bipartite graphs"

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    Exploiting locality in sparse matrix-matrix multiplication on many-core rchitectures
    (IEEE Computer Society, 2017) Akbudak K.; Aykanat, Cevdet
    Exploiting spatial and temporal localities is investigated for efficient row-by-row parallelization of general sparse matrix-matrix multiplication (SpGEMM) operation of the form C=A,B on many-core architectures. Hypergraph and bipartite graph models are proposed for 1D rowwise partitioning of matrix A to evenly partition the work across threads with the objective of reducing the number of B-matrix words to be transferred from the memory and between different caches. A hypergraph model is proposed for B-matrix column reordering to exploit spatial locality in accessing entries of thread-private temporary arrays, which are used to accumulate results for C-matrix rows. A similarity graph model is proposed for B-matrix row reordering to increase temporal reuse of these accumulation array entries. The proposed models and methods are tested on a wide range of sparse matrices from real applications and the experiments were carried on a 60-core Intel Xeon Phi processor, as well as a two-socket Xeon processor. Results show the validity of the models and methods proposed for enhancing the locality in parallel SpGEMM operations. © 1990-2012 IEEE.
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    Send volume balancing in reduce operations
    (2020-07) Çavuşoğlu, Muhammed
    We investigate balancing send volume in applications that involve reduce operations. In such applications, a given computational-task-to-processor mapping produces partial results generated by processors to be reduced possibly by other processors, thus incurring inter-processor communication. We define the reduce communication task assignment problem as assigning the reduce communication tasks to processors in a way that minimizes the send volume load of the maximally loaded processor. We propose one novel independent-task-assignment-based algorithm and four novel bin-packing-based algorithms to solve the reduce communication task assignment problem. We validate our proposed algorithms on two kernel operations: sparse matrix-sparse matrix multiplication (SpGEMM) and sparse matrix-matrix multiplication (SpMM). Experimental results show improvements of up to 23% on average for the maximum communication volume cost metric in SpGEMM and up to 12% improvement on average in SpMM.