Distributed Memory Sparse Inverse Covariance Matrix Estimation on High-Performance Computing Architectures
| Autor: | Matthias Bollhöfer, Aryan Eftekhari, Olaf Schenk |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Speedup
Computer science Maximum likelihood Approximation algorithm 010501 environmental sciences Covariance Supercomputer 01 natural sciences 010104 statistics & probability Matrix (mathematics) Linear algebra Distributed memory 0101 mathematics Random variable Algorithm 0105 earth and related environmental sciences Sparse matrix Curse of dimensionality |
| Zdroj: | SC |
| DOI: | 10.1109/sc.2018.00023 |
| Popis: | We consider the problem of estimating sparse inverse covariance matrices for high-dimensional datasets using the l1-regularized Gaussian maximum likelihood method. This task is particularly challenging as the required computational resources increase superlinearly with the dimensionality of the dataset. We introduce a performant and scalable algorithm which builds on the current advancements of second-order, maximum likelihood methods. The routine leverages the intrinsic parallelism in the linear algebra operations and exploits the underlying sparsity of the problem. The computational bottlenecks are identified and the respective subroutines are parallelized using an MPI-OpenMP approach. Experiments conducted on a 5,320 node Cray XC50 system at the Swiss National Supercomputing Center show that, in comparison to the state-of-the-art algorithms, the proposed routine provides significant strong scaling speedup with ideal scalability up to 128 nodes. The developed framework is used to estimate the sparse inverse covariance matrix of both synthetic and real-world datasets with up to 10 million dimensions. |
| Databáze: | OpenAIRE |
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