稀疏矩阵向量乘法的一种新型多gpu并行优化模型

Jiaquan Gao, Yuanshen Zhou, Kesong Wu
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摘要

在图形处理器(gpu)上加速稀疏矩阵向量乘法(SpMV)是近年来备受关注的问题。我们观察到,在特定的多GPU平台上,根据预先确定的规则将矩阵划分为几个块,并将每个块分配给具有适当存储格式的GPU,通常可以大大提高SpMV性能。这促使我们提出了一种新的多gpu并行SpMV优化模型。我们的模型包括两个阶段。在第一阶段,定义一个简单的规则将任意给定的矩阵划分到多个gpu之间,然后提出一个独立于问题而依赖于设备资源的性能模型来准确预测SpMV内核的执行时间。利用这些模型,我们在第二阶段构建了一个最优的多gpu并行SpMV算法,该算法可以自动快速地为平台生成任何问题。鉴于我们的SpMV模型是通用的,独立的…
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A Novel Multi-GPU Parallel Optimization Model for The Sparse Matrix-Vector Multiplication
Accelerating the sparse matrix-vector multiplication (SpMV) on the graphics processing units (GPUs) has attracted considerable attention recently. We observe that on a specific multiple-GPU platform, the SpMV performance can usually be greatly improved when a matrix is partitioned into several blocks according to a predetermined rule and each block is assigned to a GPU with an appropriate storage format. This motivates us to propose a novel multi-GPU parallel SpMV optimization model. Our model involves two stages. In the first stage, a simple rule is defined to divide any given matrix among multiple GPUs, and then a performance model, which is independent of the problems and dependent on the resources of devices, is proposed to accurately predict the execution time of SpMV kernels. Using these models, we construct in the second stage an optimally multi-GPU parallel SpMV algorithm that is automatically and rapidly generated for the platform for any problem. Given that our model for SpMV is general, indepen...
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