engleski

LLVM AMDGPU for High Performance Computing: are we competitive yet?

Advances in AMDGPU LLVM backend and radeonsi Gallium compute stack for Radeon Graphics Core Next (GCN) GPUs have closed the feature gap between the open source and proprietary drivers. During 2016, we have collaborated with AMDGPU developers to make GROMACS, a popular open source OpenCL-accelerated scientific software package for simulating molecular dynamics, run on Radeon GPUs using Mesa graphics library, libclc, Clang OpenCL compiler, and AMDGPU LLVM backend. This is the first fully open source OpenCL stack that has ever ran GROMACS and possibly any similarly popular scientific software. Aside from GROMACS, there is a number of widely used applications and libraries for scientific computing that support OpenCL [1]. These applications and libraries can be used as a test for AMDGPU and other parts of the OpenCL stack on a real-world code. Supporting these applications and libraries would also give them a standards-compliant OpenCL stack as a test platform, which ensures that they do not depend on vendor-specific quirks present in other stacks. Supporting them would also expand the number of hardware and software options that users can choose from. The talk will present state of the art of Mesa and LLVM for running scientific software utilizing OpenCL on Radeon GPUs. For software packages that do run on Mesa and LLVM right now, benchmarks against the proprietary AMDGPU-PRO driver will be presented and analyzed. For others, there is an ongoing effort to track and fix issues discovered [2]. Scientific software packages that do work in time for the conference will have benchmarks presented and analyzed, and otherwise, the required bug fixes and missing features in AMDGPU discussed. The next generation of AMD hardware, codenamed Vega, based on the GCN architecture, and utilizing the same LLVM backend as the existing hardware, might offer competitive performance/price and performance/power ratios compared to the other vendors in the High Performance Computing space. Used by such hardware, LLVM/Clang could become the compiler of choice for GPU computing, while the open source drivers and libraries could become the norm on supercomputers and workstations alike. [1] https://en.wikipedia.org/wiki/List_of_OpenCL_applications#Scientific_computing [2] https://bugs.freedesktop.org/show_bug.cgi?id=99553

AMDGPU ; OpenCL ; scientific computing

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