多设备异构体系结构的自动OpenCL代码生成

Pei Li, E. Brunet, François Trahay, C. Parrot, Gaël Thomas, R. Namyst
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引用次数: 13

摘要

使用多个加速器(如gpu或Xeon Phis)对于提高大型数据并行应用程序的性能和增加其工作负载的大小很有吸引力。然而,为多个加速器编写应用程序在今天仍然具有挑战性,因为从单个加速器到多个加速器确实需要处理潜在的非统一域分解、加速器间数据移动和动态负载平衡。手动编写这样的代码既耗时又容易出错。在本文中,我们提出了一种新的编程工具,称为STEPOCL,以及一种新的领域特定语言,旨在简化多加速器应用程序的开发。我们通过三个应用程序评估了STEPOCL的性能和有用性,并表明:(i)使用STEPOCL编写的应用程序的性能与加速器的数量呈线性增长,(ii)使用STEPOCL编写的应用程序的性能与手写版本竞争,(iii)在多个设备上运行较大的工作负载,这些设备不适合单个设备的内存,(iv)由于STEPOCL,编写多个加速器应用程序所需的代码行数大致除以10。
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Automatic OpenCL Code Generation for Multi-device Heterogeneous Architectures
Using multiple accelerators, such as GPUs or Xeon Phis, is attractive to improve the performance of large data parallel applications and to increase the size of their workloads. However, writing an application for multiple accelerators remains today challenging because going from a single accelerator to multiple ones indeed requires to deal with potentially non-uniform domain decomposition, inter-accelerator data movements, and dynamic load balancing. Writing such code manually is time consuming and error-prone. In this paper, we propose a new programming tool called STEPOCL along with a new domain specific language designed to simplify the development of an application for multiple accelerators. We evaluate both the performance and the usefulness of STEPOCL with three applications and show that: (i) the performance of an application written with STEPOCL scales linearly with the number of accelerators, (ii) the performance of an application written using STEPOCL competes with a handwritten version, (iii) larger workloads run on multiple devices that do not fit in the memory of a single device, (iv) thanks to STEPOCL, the number of lines of code required to write an application for multiple accelerators is roughly divided by ten.
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