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GPU Portability.pdf

sathre-portability-gpu-accelerated-hpcasia19.pdf
Preview of GPU Portability
🔗 Source: synergy.cs.vt.edu
📊 Size: 637 KB
👤 Author: Paul Sathre, Mark Gardner, and Wu-chun Feng
⬇️ Downloads: 212

Summary

Accelerator-based supercomputers have grown to 42% performance share on the TOP500, with GPUs being the most common accelerator, but coding for them can be non-trivial due to the lack of a common programming abstraction. CUDA is a widely-used programming model, but it is vendor-owned and only executes on NVIDIA GPUs. OpenCL is a vendor-neutral alternative that provides functional portability across parallel computing devices. An automated CUDA-to-OpenCL source-to-source translator, CU2CL, can translate CUDA codes to OpenCL, enabling them to run on non-CUDA devices. Three real-world accelerated applications were translated using CU2CL, demonstrating the practicality of running CUDA codes on non-CUDA devices and preserving performance and correctness. The translated codes showed performance improvements of up to three-fold on non-CUDA GPUs.

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Description

Accelerator-based supercomputers have grown to 42% performance share on the TOP500, with GPUs being the most common accelerator, but coding for them can be...

Technical Information

  • File Format: PDF
  • File Size: 637 KB
  • Pages: 8
  • Language: EN
  • Author: Paul Sathre, Mark Gardner, and Wu-chun Feng
  • Total Downloads: 212
  • Last Updated: 5 hours ago

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This PDF document about GPU Portability provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into GPU Portability.

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