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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Enabling Runtime SpMV Format Selection through an Overhead Conscious Method
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Enabling Runtime SpMV Format Selection through an Overhead Conscious Method

机译：通过开销方法启用运行时SPMV格式选择

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摘要

Sparse matrix-vector multiplication (SpMV) is an important kernel and its performance is critical for many applications. Storage format selection is to select the best format to store a sparse matrix; it is essential for SpMV performance. Prior studies have focused on predicting the format that helps SpMV run fastest, but have ignored the runtime prediction and format conversion overhead. This work shows that the runtime overhead makes the predictions from previous solutions frequently sub-optimal and sometimes inferior regarding the end-to-end time. It proposes a new paradigm for SpMV storage selection, an overhead-conscious method. Through carefully designed regression models and neural network-based time series prediction models, the method captures the influence imposed on the overall program performance by the overhead and the benefits of format prediction and conversions. The method employs a novel two-stage lazy-and-light scheme to help control the possible negative effects of format predictions, and at the same time, maximize the overall format conversion benefits. Experiments show that the technique outperforms previous techniques significantly. It improves the overall performance of applications by 1.21X to 1.53X, significantly larger than the 0.83X to 1.25X upper-bound speedups overhead-oblivious methods could give.
机译：稀疏矩阵矢量乘法（SPMV）是一个重要的内核，其性能对于许多应用程序至关重要。存储格式选择是选择存储稀疏矩阵的最佳格式;这对于SPMV性能至关重要。先前的研究专注于预测帮助SPMV运行最快的格式，但忽略了运行时预测和格式转换开销。这项工作表明，运行时开销使先前解决方案的预测频繁地是次优且有时在端到端时间的下方。它提出了一种用于SPMV存储选择的新范式，一个有意识的方法。通过精心设计的回归模型和基于神经网络的时间序列预测模型，该方法通过开销和格式预测和转换的益处捕获对整体节目性能的影响。该方法采用新颖的两级惰性和光线方案来帮助控制格式预测的可能的负面影响，同时最大化整体格式转换益处。实验表明，该技术显着优于先前的技术。它将应用程序的整体性能提高1.21倍至1.53倍，显着大于0.83倍至1.25倍的上限加速度的开销令人遗憾的方法。

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