@article{zhou_zhao_zhang_shen_2020, title={HARP: Holistic Analysis for Refactoring Python-Based Analytics Programs}, ISSN={["0270-5257"]}, DOI={10.1145/3377811.3380434}, abstractNote={Modern machine learning programs are often written in Python, with the main computations specified through calls to some highly optimized libraries (e.g., TensorFlow, PyTorch). How to maximize the computing efficiency of such programs is essential for many application domains, which has drawn lots of recent attention. This work points out a common limitation in existing efforts: they focus their views only on the static computation graphs specified by library APIs, but leave the influence from the hosting Python code largely unconsidered. The limitation often causes them to miss the big picture and hence many important optimization opportunities. This work proposes a new approach named HARP to address the problem. HARP enables holistic analysis that spans across computation graphs and their hosting Python code. HARP achieves it through a set of novel techniques: analytics-conscious speculative analysis to circumvent Python complexities, a unified representation augmented computation graphs to capture all dimensions of knowledge related with the holistic analysis, and conditioned feedback mechanism to allow risk-controlled aggressive analysis. Refactoring based on HARP gives 1.3-3X and 2.07X average speedups on a set of TensorFlow and PyTorch programs.}, journal={2020 ACM/IEEE 42ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2020)}, author={Zhou, Weijie and Zhao, Yue and Zhang, Guoqiang and Shen, Xipeng}, year={2020}, pages={506–517} }
 @article{zhao_zhou_shen_yiu_2018, title={Overhead-Conscious Format Selection for SpMV-Based Applications}, ISSN={["1530-2075"]}, DOI={10.1109/IPDPS.2018.00104}, abstractNote={Sparse matrix vector multiplication (SpMV) is an important kernel in many applications and is often the major performance bottleneck. The storage format of sparse matrices critically affects the performance of SpMV. Although there have been previous studies on selecting the appropriate format for a given matrix, they have ignored the influence of runtime prediction overhead and format conversion overhead. For many common uses of SpMV, such overhead is part of the execution times and may outweigh the benefits of new formats. Ignoring them makes the predictions from previous solutions frequently suboptimal and sometimes inferior. On the other hand, the overhead is difficult to consider, as it, along with the benefits of having a new format, varies from matrix to matrix, and from application to application. This work proposes a solution. It first explores the pros and cons of various possible treatments to the overhead in the format selection problem. It then presents an explicit approach which involves several regression models for capturing the influence of the overhead and benefits of format conversions on the overall program performance. It proposes a two-stage lazy-and-light scheme to help control the risks in the 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 1.14X to 1.43X, significantly larger than the 0.82X to 1.24X upperbound speedups overhead-oblivious methods could give.}, journal={2018 32ND IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS)}, author={Zhao, Yue and Zhou, Weijie and Shen, Xipeng and Yiu, Graham}, year={2018}, pages={950–959} }