This file contains the HiCMA library and scripts for reproducing the results presented in the Euro-Par 2018 conference paper entitled "Exploiting Data Sparsity for Large-Scale Matrix Computations".The aim of this paper was to use the Hierarchical matrix Computations on Manycore Architectures (HiCMA) library as the basis to address the data sparsity structure of dense matrices on shared and shared to distributed-memory systems. HiCMA was extended to provide a high-performance implementation of the Cholesky factorization on distributed-memory systems.A complete list of HiCMA features can be found at: https://github.com/ecrc/hicma. The dataset is compressed in .gz format and can be uncompressed by standard compression utilities. The data are s...
Due to the evolution of massively parallel computers towards deeper levels of parallelism and memory...
Abstract. A style for programming problems from matrix algebra is developed with a familiar example ...
In this dissertation we have identified vector processing shortcomings related to the efficient stor...
Hierarchical matrix (H-matrix) techniques can be used to efficiently treat dense matrices. With an H...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
In this work, we consider the reformulation of hierarchical (H) matrix algorithms for many-core proc...
We present a distributed-memory library for computations with dense structured matrices. A matrix is...
In this work, we consider the reformulation of hierarchical ($\mathcal{H}$) matrix algorithm...
In this document, we describe two strategies of distribution of computations that can be used to imp...
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Usi...
This paper discusses the scalability of Cholesky, LU, and QR factorization routines on MIMD distribu...
International audienceHierarchical matrices (H-matrices) have become important in applications where...
Abstract—Partitioned Global Address Space (PGAS) languages offer programmers a shared memory view th...
Hierarchically semiseparable (HSS) matrix algorithms are emerging techniques in constructing the sup...
The current state and foreseeable future of high performance scientific computing (HPC) can be descr...
Due to the evolution of massively parallel computers towards deeper levels of parallelism and memory...
Abstract. A style for programming problems from matrix algebra is developed with a familiar example ...
In this dissertation we have identified vector processing shortcomings related to the efficient stor...
Hierarchical matrix (H-matrix) techniques can be used to efficiently treat dense matrices. With an H...
Many matrices in scientific computing, statistical inference, and machine learning exhibit sparse an...
In this work, we consider the reformulation of hierarchical (H) matrix algorithms for many-core proc...
We present a distributed-memory library for computations with dense structured matrices. A matrix is...
In this work, we consider the reformulation of hierarchical ($\mathcal{H}$) matrix algorithm...
In this document, we describe two strategies of distribution of computations that can be used to imp...
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Usi...
This paper discusses the scalability of Cholesky, LU, and QR factorization routines on MIMD distribu...
International audienceHierarchical matrices (H-matrices) have become important in applications where...
Abstract—Partitioned Global Address Space (PGAS) languages offer programmers a shared memory view th...
Hierarchically semiseparable (HSS) matrix algorithms are emerging techniques in constructing the sup...
The current state and foreseeable future of high performance scientific computing (HPC) can be descr...
Due to the evolution of massively parallel computers towards deeper levels of parallelism and memory...
Abstract. A style for programming problems from matrix algebra is developed with a familiar example ...
In this dissertation we have identified vector processing shortcomings related to the efficient stor...