Data Integration Tasks on Heterogeneous Systems Using OpenCL
Data Integration Tasks on Heterogeneous Systems Using OpenCL
复制标题
使用 OpenCL 的异构系统上的数据集成任务
DOI：
10.1145/3318170.3318187
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Chamberlain, Roger D.
中科院分区：
文献类型：
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作者：
Faber, Clayton J.;Cabrera, Anthony M.;Booker, Orondé;Maayan, Gabe;Chamberlain, Roger D.
在大数据时代，许多新的算法被开发出来，试图找到最有效的方法来执行海量数据的计算。然而，对于这些应用程序中的许多应用程序来说，经常忽略的是预处理步骤。数据集成基准测试套件(DIBS)[1]旨在以与硬件无关的方式了解数据集转换的特征。虽然从表面上看，这些应用程序具有很高的数据并行性，但它们的规范中有一些警告，可能会影响这一特征。尽管如此，OpenCL仍然可以成为这些应用程序的有效部署环境。在这项工作中，我们从DIB中提出的每一类数据转换中提取一个子集，并在OpenCL中实现它们，以评估它们在异构系统中的性能。为了针对不同的系统，我们选择了一个常见的应用程序，并尝试将其部署到OpenCL可针对的三个平台(CPU、GPU和FPGA)。应用程序通过其平均转换数据速率进行评估(参见图1)。我们说明了每个计算设备在数据集成空间中的优势，以及在OpenCL平台中允许主机/设备通信的不同通信方案。
In the era of big data, many new algorithms are developed to try and find the most efficient way to perform computations with massive amounts of data. However, what is often overlooked is the preprocessing step for many of these applications. The Data Integration Benchmark Suite (DIBS) [1] was designed to understand the characteristics of dataset transformations in a hardware agnostic way. While on the surface these applications have a high amount of data parallelism, there are caveats in their specification that can potentially affect this characteristic. Even still, OpenCL can be an effective deployment environment for these applications.In this work we take a subset of the data transformations from each category presented in DIBS and implement them in OpenCL to evaluate their performance for heterogeneous systems. For targeting heterogeneous systems, we take a common application and attempt to deploy it to three platforms targetable by OpenCL (CPU, GPU, and FPGA). The applications are evaluated by their average transformation data rate (see Figure 1). We illustrate the advantages of each compute device in the data integration space along with different communications schemes allowed for host/device communication in the OpenCL platform.
DIBS：数据集成基准套件
DOI：
10.1145/3185768.3186307
发表时间：
2018
期刊：
Companion of the 2018 ACM/SPEC International Conference on Performance Engineering
影响因子：
--
作者：
A. Cabrera;Clayton J. Faber;Kyle Cepeda;Robert Derber;Cooper Epstein;Jason Zheng;R. Cytron;R. Chamberlain
通讯作者：
R. Chamberlain