An Improved Abstract GPU Model with Data Transfer
An Improved Abstract GPU Model with Data Transfer
复制标题
一种改进的带数据传输的抽象GPU模型
DOI：
10.1109/icppw.2017.28
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Prudence W. H. Wong
中科院分区：
文献类型：
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作者：
T. Carroll;Prudence W. H. Wong
GPU通常用作协处理器，以加速计算密集型任务，这要归功于其大规模并行架构。有研究到不同的抽象并行模型，这使得研究人员设计和分析并行算法。然而，大多数分析GPU算法的工作都是基于软件的工具，用于分析GPU算法。最近，已经提出了一些抽象的GPU模型，但是它们不能捕获GPU的所有元素。特别是，它们错过了CPU和GPU之间的数据传输，这在实践中可能会导致瓶颈并显着降低性能。我们提出了一个全面的模型，称为抽象传输GPU，据我们所知，这是第一个抽象的GPU模型来捕捉CPU和GPU之间的数据传输。我们通过实验表明，现有的抽象GPU模型不能充分捕捉所有的GPU算法的实际运行时间在所有情况下，因为它们不捕捉数据传输。我们表明，通过捕获数据传输与我们的模型，我们能够获得更准确的预测GPU算法的实际运行时间。预计我们的模型有助于改进由CPU和GPU组成的异构系统的设计和分析，并将使研究人员能够做出更明智的实施决策，因为他们将意识到数据传输将如何影响他们的程序。
GPUs are commonly used as coprocessors to accelerate a compute-intensive task, thanks to their massively parallel architecture. There is study into different abstract parallel models, which allow researchers to design and analyse parallel algorithms. However, most work on analysing GPU algorithms has been software based tools for profiling a GPU algorithm. Recently, some abstract GPU models have been proposed, yet they do not capture all elements of a GPU. In particular, they miss the data transfer between CPU and GPU, which in practice can cause a bottleneck and reduce performance dramatically. We propose a comprehensive model called Abstract Transferring GPU which to our knowledge is the first abstract GPU model to capture data transfer between CPU and GPU. We show via experiments, that existing abstract GPU models cannot sufficiently capture all of the actual running of a GPU algorithm time in all cases, as they do not capture data transfer. We show that by capturing data transfer with our model, we are able to obtain more accurate predictions of the GPU algorithm actual running time. It is expected that our model helps improve design and analysis of heterogeneous systems consisting of CPU and GPU, and will allow researchers to make better informed implementation decisions, as they will be aware how data transfer will affect their programs.