Browsing by Subject "Maximum communication volume"
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Item Open Access Reduce operations: send volume balancing while minimizing latency(IEEE, 2020) Karsavuran, M. Ozan; Acer, S.; Aykanat, CevdetCommunication hypergraph model was proposed in a two-phase setting for encapsulating multiple communication cost metrics (bandwidth and latency), which are proven to be important in parallelizing irregular applications. In the first phase, computational-task-to-processor assignment is performed with the objective of minimizing total volume while maintaining computational load balance. In the second phase, communication-task-to-processor assignment is performed with the objective of minimizing total number of messages while maintaining communication-volume balance. The reduce-communication hypergraph model suffers from failing to correctly encapsulate send-volume balancing. We propose a novel vertex weighting scheme that enables part weights to correctly encode send-volume loads of processors for send-volume balancing. The model also suffers from increasing the total communication volume during partitioning. To decrease this increase, we propose a method that utilizes the recursive bipartitioning framework and refines each bipartition by vertex swaps. For performance evaluation, we consider column-parallel SpMV, which is one of the most widely known applications in which the reduce-task assignment problem arises. Extensive experiments on 313 matrices show that, compared to the existing model, the proposed models achieve considerable improvements in all communication cost metrics. These improvements lead to an average decrease of 30 percent in parallel SpMV time on 512 processors for 70 matrices with high irregularity.Item Open Access Send volume balancing in reduce operations(2020-07) Çavuşoğlu, MuhammedWe 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.