Hi Junting

I am trying to find a proper reason for the statement of "PyFR has a good 
utilization of GPU acceleration technology" , or possibly refer to a paper. I 
understand that memory transfer between CPU and GPU is one of the big slowdowns 
of GPU computing.

Here is an example of a paper that looks at performance aspects:

https://ieeexplore.ieee.org/document/7876999

In the paper "pyfr an opensource frame work for solving advection-diffusion..." 
in 2014, it was said GEMM was optimized for large square matrices, where the 
constant operator in PyFR are small and square, and state matrices are short 
and fat. Is this improved?

GEMM can actually perform well in a range of scenarios. However, we have also 
developed technology for smaller/sparse matrices:

https://www.sciencedirect.com/science/article/pii/S0010465515004506

Peter

Dr Peter Vincent MSci ARCS DIC PhD FRAeS
Reader in Aeronautics and EPSRC Fellow
Department of Aeronautics
Imperial College London
South Kensington
London
SW7 2AZ
UK



On 25 Jul 2019, at 20:47, Junting Chen 
<[email protected]<mailto:[email protected]>> wrote:

Hello all,

I am trying to find a proper reason for the statement of "PyFR has a good 
utilization of GPU acceleration technology" , or possibly refer to a paper. I 
understand that memory transfer between CPU and GPU is one of the big slowdowns 
of GPU computing.

How would you say PyFR as a modern code uses GPU resource better than codes 
with histories then modified to utilize GPU acceleration?

In the paper "pyfr an opensource frame work for solving advection-diffusion..." 
in 2014, it was said GEMM was optimized for large square matrices, where the 
constant operator in PyFR are small and square, and state matrices are short 
and fat. Is this improved?


Junting Chen

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