Webinar - PyTorch Performance on CDNA2 and RDNA3

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Calculating spectra of fluids in irregular geometry is a challenging task that requires the estimation of the eigenmodes of a Laplacian operator. For most situations, obtaining all of the eigenmodes requires too much memory and we are comfortable making the choice to examine only a subset of the kinetic energy spectra; dividing the spectra into modes that we can calculate and a portion that we deem unresolved. To find a subset eigenpairs while making efficient use of memory, we can use a matrix-free implementation of the implictly restarted Lanczos method. This webinar discusses the algorithm and the intended use case for this code to motivate the selection of PyTorch as the implementation framework. Next, we explore how to install ROCm and Pytorch with ROCm backends on CDNA2 and RDNA3 platforms that are both hosted within Fluid Numerics' on-premise cluster resources. The algorithm that was initially implemented using numpy, numba, and scipy is compared with our implementation in PyTorch in terms of runtime and energy consumption.
 
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