| Autor: |
Sui, Yicheng, Sun, Yufei, Shi, Changqing, Wang, Haotian, Zhang, Zhiqiang, Wang, Jiahao, Zhang, Yuzhi |
| Zdroj: |
CCF Transactions on High Performance Computing; 20240101, Issue: Preprints p1-14, 14p |
| Abstrakt: |
Currently, most Deep Learning (DL) frameworks support only CUDA and ROCm environments, limiting their use to NVIDIA and AMD GPUs. Since current High-Performance Computing (HPC) usually uses different types of heterogeneous devices to accelerate computing, some HPCs cannot utilize heterogeneous devices for computing based on the DL frameworks. To address this problem, we introduce OpenCL-PyTorch, a PyTorch extension based on OpenCL. This extension enables the deployment of DL models on a broader range of OpenCL devices, encompassing CPUs, GPUs, and other accelerators. A standout feature of OpenCL-PyTorch is our novel unified OpenCL device and memory management approach, which significantly enhances performance. We rigorously evaluated OpenCL-PyTorch with various DL models, confirming its accuracy and effectiveness. The validation of the management approach further underscores the importance of our unified device and memory management in optimizing operator performance. |
| Databáze: |
Supplemental Index |
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