Jesmin Jahan Tithi

Cache-oblivious wavefront: improving parallelism of recursive dynamic programming algorithms without losing cache-efficiency

State-of-the-art cache-oblivious parallel algorithms for dynamic programming (DP) problems usually guarantee asymptotically optimal cache performance without any tuning of cache parameters, but they often fail to exploit the theoretically best parallelism at the same time. While these algorithms achieve cache-optimality through the use of a recursive divide-and-conquer (DAC) strategy, scheduling tasks at the granularity of task dependency introduces artificial dependencies in addition to those arising from the defining recurrence equations. We removed the artificial dependency by scheduling tasks ready for execution as soon as all its real dependency constraints are satisfied, while preserving the cache-optimality by inheriting the DAC strategy. We applied our approach to a set of widely known dynamic programming problems, such as Floyd-Warshalls All-Pairs Shortest Paths, Stencil, and LCS. Theoretical analyses show that our techniques improve the span of 2-way DAC-based Floyd Warshalls algorithm on an

AUTOGEN: automatic discovery of cache-oblivious parallel recursive algorithms for solving dynamic programs

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Exploiting spatial architectures for edit distance algorithms

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High-Performance Energy-Efficient Recursive Dynamic Programming with Matrix-Multiplication-Like Flexible Kernels

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An Efficient Cache-oblivious Parallel Viterbi Algorithm

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Accelerated molecular mechanical and solvation energetics on multicore CPUs and manycore GPUs

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Avoiding Locks and Atomic Instructions in Shared-Memory Parallel BFS Using Optimistic Parallelization

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Provably Efficient Scheduling of Cache-oblivious Wavefront Algorithms

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Designing Autocorrelated Genes

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Autogen: Automatic Discovery of Efficient Recursive Divide-8-Conquer Algorithms for Solving Dynamic Programming Problems

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