• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Computational Science >Automatic Mapping for OpenCL-Programs on CPU/GPU Heterogeneous Platforms
【24h】

Automatic Mapping for OpenCL-Programs on CPU/GPU Heterogeneous Platforms

机译：CPU / GPU异构平台上的OpenCl-Program自动映射

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Heterogeneous computing systems with multiple CPUs and GPUs are increasingly popular. Today, heterogeneous platforms are deployed in many setups, ranging from low-power mobile systems to high performance computing systems. Such platforms are usually programmed using OpenCL which allows to execute the same program on different types of device. Nevertheless, programming such platforms is a challenging job for most non-expert programmers. To enable an efficient application runtime on heterogeneous platforms, programmers require an efficient workload distribution to the available compute devices. The decision how the application should be mapped is non-trivial. In this paper, we present a new approach to build accurate predictive-models for OpenCL programs. We use a machine learning-based predictive model to estimate which device allows best application speed-up. With the LLVM compiler framework we develop a tool for dynamic code-feature extraction. We demonstrate the effectiveness of our novel approach by applying it to different prediction schemes. Using our dynamic feature extraction techniques, we are able to build accurate predictive models, with accuracies varying between 77% and 90%, depending on the prediction mechanism and the scenario. We evaluated our method on an extensive set of parallel applications. One of our findings is that dynamically extracted code features improve the accuracy of the predictive-models by 6.1% on average (maximum 9.5%) as compared to the state of the art.
机译：具有多个CPU和GPU的异构计算系统越来越受欢迎。如今，异构平台在许多设置中部署，从低功率移动系统到高性能计算系统。这些平台通常使用OpenCL编程，允许在不同类型的设备上执行相同的程序。尽管如此，为大多数非专家程序员来说，这些平台的编程是一个有挑战性的工作。为了在异构平台上启用有效的应用程序运行时，程序员需要高效的工作负载分发到可用的计算设备。该决定如何映射应用程序是非微不足道的。在本文中，我们提出了一种为OpenCL程序构建准确的预测模型的新方法。我们使用基于机器学习的预测模型来估计哪种设备允许最佳应用程序加速。使用LLVM编译器框架，我们开发了一种动态代码特征提取的工具。我们通过将其应用于不同预测方案来证明我们的新方法的有效性。使用我们的动态特征提取技术，我们能够建立准确的预测模型，精度在77％和90％之间变化，具体取决于预测机制和场景。我们在广泛的并行应用程序中评估了我们的方法。我们的研究结果之一是，与现有技术相比，动态提取的代码特征提高了预测模型的准确性6.1％（最多9.5％）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号