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Stencil computation is one of the most important kernels in various scientific and engineering applications. A variety of work has focused on vectorization techniques, aiming at exploiting the in-core data parallelism. Briefly, they either incur data alignment conflicts or hurt the data locality when integrated with tiling. In this paper, a novel transpose layout is devised to preserve the data locality for tiling in the data space and reduce the data reorganization overhead for vectorization simultaneously. We then propose an approach of temporal computation folding designed to further reduce the redundancy of arithmetic calculations by exploiting the register reuse, alleviating the increased register pressure, and deducing generalization with a linear regression model. Experimental results on the AVX-2 and AVX-512 CPUs show that our approach obtains a competitive performance.
We reduce the cost of communication and synchronization in graph processing by analyzing the fastest way to process graphs: pushing the updates to a shared state or pulling the updates to a private state.We investigate the applicability of this push-
A classic result in algorithmic information theory is that every infinite binary sequence is computable from a Martin-Loef random infinite binary sequence. Proved independently by Kucera and Gacs, this result answered a question by Charles Bennett an
Stencil computation is one of the most important kernels in various scientific and engineering applications. A variety of work has focused on vectorization and tiling techniques, aiming at exploiting the in-core data parallelism and data locality res
Erasure codes are an integral part of many distributed storage systems aimed at Big Data, since they provide high fault-tolerance for low overheads. However, traditional erasure codes are inefficient on reading stored data in degraded environments (w
Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not