On the Algorithmic Impact of Scientific Computing on Machine Learning
Year: 2024
Type: dissertation
Abstract: To alleviate the quadratic complexity of matrix computations, we developed the codes GOFMM and Newton-CG, both with lower computational complexities. We developed GOFMM for highly-parallel execution, showing good scalability up to 128 nodes and runtime benefits against ScaLAPACK or STRUMPACK for a wide range of problems in PDEs, kernel matrices, or graph Laplacians. Newton-CG is developed for GPU data-parallelism in deep learning, performing on par or better than the optimizers SGD and Adam.
Author: Reiz, Severin Maximilian
Language: English
Topic: Matrix Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6904744316
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