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首页> 外文期刊>International journal of parallel programming >Introducing and Implementing the Allpairs Skeleton for Programming Multi-GPU Systems
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Introducing and Implementing the Allpairs Skeleton for Programming Multi-GPU Systems

机译：介绍和实现用于对多GPU系统进行编程的Allpairs骨架

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摘要

Algorithmic skeletons simplify software development: they abstract typical patterns of parallelism and provide their efficient implementations, allowing the application developer to focus on the structure of algorithms, rather than on implementation details. This becomes especially important for modern parallel systems with multiple graphics processing units (GPUs) whose programming is complex and error-prone, because state-of-the-art programming approaches like CUDA and OpenCL lack high-level abstractions. We define a new algorithmic skeleton for allpairs computations which occur in real-world applications, ranging from bioinformatics to physics. We develop the skeleton's generic parallel implementation for multi-GPU Systems in OpenCL. To enable the automatic use of the fast GPU memory, we identify and implement an optimized version of the allpairs skeleton with a customizing function that follows a certain memory access pattern. We use matrix multiplication as an application study for the allpairs skeleton and its two implementations and demonstrate that the skeleton greatly simplifies programming, saving up to 90% of lines of code as compared to OpenCL. The performance of our optimized implementation is up to 6.8 times higher as compared with the generic implementation and is competitive to the performance of a manually written optimized OpenCL code.
机译：算法框架简化了软件开发：它们抽象出典型的并行模式并提供有效的实现，从而使应用程序开发人员可以专注于算法的结构，而不是实现细节。这对于具有多个图形处理单元（GPU）的现代并行系统尤其重要，这些图形系统的编程非常复杂且容易出错，因为CUDA和OpenCL等最新的编程方法缺少高级抽象。我们为在现实世界中发生的从生物信息学到物理学的所有对计算定义了一个新的算法框架。我们为OpenCL中的多GPU系统开发框架的通用并行实现。为了能够自动使用快速GPU内存，我们使用遵循某些内存访问模式的自定义功能来识别并实现allpairs骨架的优化版本。我们使用矩阵乘法作为allpairs骨架及其两个实现的应用研究，并证明了该骨架极大地简化了编程，与OpenCL相比，最多可节省90％的代码行。与通用实现相比，我们的优化实现的性能高达6.8倍，与手动编写的优化OpenCL代码的性能相比具有竞争力。

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