Balanced Partitioning of Several Cache-Oblivious Algorithms
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Frigo et al. proposed an ideal cache model and a recursive technique to design sequential cache-efficient algorithms in a cache-oblivious fashion. Ballard et al. pointed out that it is a fundamental open problem to extend the technique to an arbitrary architecture. Ballard et al. raised another open question on how to parallelize Strassen's algorithm exactly and efficiently on an arbitrary number of processors. We propose a novel way of partitioning a cache-oblivious algorithm to achieve perfect strong scaling on an arbitrary number, even a prime number, of processors within a certain range in a shared-memory setting. Our approach is Processor-Aware but Cache-Oblivious (PACO). We demonstrate our approach on several important cache-oblivious algorithms, including LCS, 1D, GAP, classic rectangular matrix multiplication on a semiring, and Strassen's algorithm. We discuss how to extend our approach to a distributed-memory architecture, or even a heterogeneous computing system. Hence, our work may provide a new perspective on the fundamental open problem of extending the recursive cache-oblivious technique to an arbitrary architecture. We provide an almost exact solution to the open problem on parallelizing Strassen. Our approach may provide a new perspective on extending the recursive cache-oblivious technique to an arbitrary architecture. All our algorithms demonstrate better scalability or better overall parallel cache complexities than the best known algorithms. Preliminary experiments justify our theoretical prediction that the PACO algorithms can outperform significantly state-of-the-art Processor-Oblivious (PO) and Processor-Aware (PA) counterparts.
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