Mapping-Free GPU Offloading in OpenMP Using Unified Memory

Jia Sian Hong, Yi Ping You

研究成果: Conference contribution同行評審

摘要

With the increasing demand for heterogeneous computing, OpenMP has introduced an offloading feature that allows programmers to offload a task to a device (e.g., a GPU or an FPGA) by adding appropriate directives to the task since version 4.0. Compared to other low-level programming models, such as CUDA and OpenCL, OpenMP significantly reduces the burden on programmers to ensure that tasks are performed correctly on the device. However, OpenMP still has a data-mapping problem, which arises from the separate memory spaces between the host and the device. It is still necessary for programmers to specify data-mapping directives to indicate how data are transferred between the host and the device. When using complex data structures such as linked lists and graphs, it becomes more difficult to compose reliable and efficient data-mapping directives. Moreover, the OpenMP runtime library may incur substantial overhead due to data-mapping management. In this paper, we propose a compiler and runtime collaborative framework, called OpenMP-UM, to address the data-mapping problem. Using the CUDA unified memory mechanism, OpenMP-UM eliminates the need for data-mapping directives and reduces the overhead associated with data-mapping management. The key concept behind OpenMP-UM is to use unified memory as the default memory storage for all host data, including automatic, static, and dynamic data. Experiments have demonstrated that OpenMP-UM not only removed programmers' burden in writing data-mapping to offload in OpenMP applications but also achieved an average of 7.3x speedup for applications that involve deep copies and an average of 1.02x speedup for regular applications.

原文English
主出版物標題52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings
發行者Association for Computing Machinery
頁面104-111
頁數8
ISBN(電子)9798400708435
DOIs
出版狀態Published - 7 8月 2023
事件52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings - Salt Lake City, United States
持續時間: 7 8月 202310 8月 2023

出版系列

名字ACM International Conference Proceeding Series

Conference

Conference52nd International Conference on Parallel Processing, ICPP 2023 - Workshops Proceedings
國家/地區United States
城市Salt Lake City
期間7/08/2310/08/23

指紋

深入研究「Mapping-Free GPU Offloading in OpenMP Using Unified Memory」主題。共同形成了獨特的指紋。

引用此