Parallel computing method for sparse matrix vector multiplication of Shenwei architecture

A sparse matrix and architecture technology, applied in complex mathematical operations, etc., can solve problems such as limiting instruction levels, writing conflicts, and impacts, and achieve the effects of improving space and time locality, reducing waiting time for caching, and reducing the number of interactions

Active Publication Date: 2018-08-24
BEIHANG UNIV
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Problems solved by technology

These irregularities are due to the randomness of the memory access order, and it is difficult to take advantage of the locality of the data
Since this irregular pattern has a lot to do with the random order of the input sparse matrix, it is difficult to solve it at the compiler...

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  • Parallel computing method for sparse matrix vector multiplication of Shenwei architecture
  • Parallel computing method for sparse matrix vector multiplication of Shenwei architecture

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Embodiment Construction

[0034] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not constitute a conflict with each other.

[0035] The basic idea of ​​the present invention is to divide the level of the matrix into four levels of fleet, block, tile and slice according to the system structure of Sunway, and these levels correspond to different hardware structures and computing levels respectively.

[0036] The parallel calculation method for multiplication of sparse matrix and dense vector provided by the present inve...

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Abstract

The present invention relates to a parallel computing method for sparse matrix vector multiplication of a Shenwei architecture. The method comprises: dividing a sparse matrix from an original single-level data structure into a multi-level data structure, wherein the multi-level data structure comprises four levels of fleet, block, tile, and slice; and corresponding the multi-level data structure to a Shenwei hardware architecture and the calculation hierarchy. According to the method disclosed by the present invention, the spatial and temporal locality of the data is improved, and the number of times of interactions between the core group and the memory is reduced.

Description

technical field [0001] The invention relates to the field of high-performance computers, in particular to a parallel computing optimization method for multiplying sparse matrices and dense vectors applicable to the Sunway supercomputer architecture. Background technique [0002] The multiplication of sparse matrix and dense vector (Sparse Matrix-vector Multiply, abbreviated as SpMV) is a very important but independent computing core in many domain programs. It has a wide range of applications in many fields involving high performance computing, such as fluid mechanics and molecular dynamics. Moreover, problems in the field of graph computing such as PageRank and breadth-first search can also be abstracted into SpMV problems. [0003] The SpMV problem has two prominent problems in the calculation process, namely the irregularity of the calculation and memory access modes. These irregularities are caused by the randomness of the memory access order, and it is difficult to ta...

Claims

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Application Information

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IPC IPC(8): G06F17/16
CPCG06F17/16
Inventor 杨海龙刘常喜李云春栾钟治
Owner BEIHANG UNIV
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