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ScrimpCo: scalable matrix profile on commodity heterogeneous processors
The Journal of Supercomputing ( IF 3.3 ) Pub Date : 2020-02-19 , DOI: 10.1007/s11227-020-03199-w
Jose C. Romero , Antonio Vilches , Andrés Rodríguez , Angeles Navarro , Rafael Asenjo

The discovery of time series motifs and discords is considered a paramount and challenging problem regarding time series analysis. In this work, we present ScrimpCo, a heterogeneous implementation of a previous algorithm called SCRIMP that excels at finding relevant subsequences in time series. We propose and evaluate several static, dynamic and adaptive partition strategies targeting commodity processors, on both homogeneous (CPU multicore) and heterogeneous (CPU + GPU) architectures. For the CPU + GPU implementation, we explore a heterogeneous parallel_reduce pattern that computes part of the computation onto an OpenCL capable GPU, whereas the CPU cores take care of the other part. Our heterogeneous scheduler, built on top of TBB, pays special attention to appropriately balance the computational load among the GPU and CPU cores. The experimental results show that our homogeneous implementation scales linearly and that our heterogeneous implementation allows us to reach near-ideal performance on commodity processors that feature an on-chip GPU.

中文翻译:

ScrimpCo:商品异构处理器上的可扩展矩阵配置文件

时间序列主题和不一致的发现被认为是关于时间序列分析的首要和具有挑战性的问题。在这项工作中,我们提出了 ScrimpCo,这是先前称为 SCRIMP 的算法的异构实现,该算法擅长在时间序列中查找相关子序列。我们针对商品处理器在同构(CPU 多核)和异构(CPU + GPU)架构上提出并评估了几种静态、动态和自适应分区策略。对于 CPU + GPU 实现,我们探索了一种异构 parallel_reduce 模式,该模式将部分计算计算到支持 OpenCL 的 GPU 上,而 CPU 内核负责另一部分。我们的异构调度程序建立在 TBB 之上,特别注意适当平衡 GPU 和 CPU 内核之间的计算负载。
更新日期:2020-02-19
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