Distributed Memory Sparse Inverse Covariance Matrix Estimation on High-Performance Computing Architectures
Informazioni aggiuntive
Autori
Eftekhari A.,
Bollhoefer M.,
Schenk O.
Tipo
Contributo in atti di conferenza
Anno
2018
Lingua
Inglese
Abstract
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. Numerical examples 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 approximate the sparse inverse covariance matrix of both synthetic and real-world datasets with up to 10 million dimensions.
Atti di conferenza
Proceedings of the ACM/IEEE International Conference on High Performance Computing, Networking, Storage and Analysis
Mese
novembre
Editore
ACM
Nome conferenza
SC18
Luogo conferenza
Dallas, Texas
Data conferenza
November 11 – 16, 2018
Parole chiave
inverse covariance matrix, sparse matrices, approximate matrix inverse, high-performance computing
