GPUs (Graphics processing units) are commonly used in high-performance computing
to improve the execution time of parallelizable programs. SeisSol, as such a program,
can currently only use GPUs manufactured by NVIDIA. In this thesis, we extend the
GPU-related libraries to work additionally on GPUs manufactured by AMD. We examine
the performance of the extended libraries on the AMD Radeon Instinct MI50 using the
Roofline model and compare it to results measured on the NVIDIA Tesla V100.
Furthermore, we investigate using multiple AMD GPUs, which communicate via
OpenMPI. The Radeon Instinct MI50 achieved a good performance but was slower than
the NVIDIA GPU. We detected a problem in the communication between the GPUs,
which is restricted to collective communication. This does not affect SeisSol since it uses
mainly point-to-point communication.
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GPUs (Graphics processing units) are commonly used in high-performance computing
to improve the execution time of parallelizable programs. SeisSol, as such a program,
can currently only use GPUs manufactured by NVIDIA. In this thesis, we extend the
GPU-related libraries to work additionally on GPUs manufactured by AMD. We examine
the performance of the extended libraries on the AMD Radeon Instinct MI50 using the
Roofline model and compare it to results measured on the NVIDIA Tesla V100.
Fu...
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