Scalable sparse LU factorization is critical for efficient numerical simulation of circuits and electrical power grids. In this work, we present a new scalable sparse direct solver called Basker. Basker introduces a new algorithm to parallelize the Gilbert-Peierls algorithm for sparse LU factorization. As architectures evolve, there exists a need for algorithms that are hierarchical […]

January 22, 2016 by hgpu

We discuss an approach for solving sparse or dense banded linear systems ${bf A} {bf x} = {bf b}$ on a Graphics Processing Unit (GPU) card. The matrix ${bf A} in {mathbb{R}}^{N times N}$ is possibly nonsymmetric and moderately large; i.e., $10000 leq N leq 500000$. The ${it split and parallelize}$ (${tt SaP}$) approach seeks […]

September 30, 2015 by hgpu

To face the ever demanding requirements in term of accuracy and speed of scientific simulations, the High Performance community is constantly increasing the demands in term of parallelism, adding thus tremendous value to parallel libraries strongly optimized for highly complex architectures.Enabling HPC applications to perform efficiently when invoking multiple parallel libraries simultaneously is a great […]

June 26, 2015 by hgpu

The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of the computing resources. The pressure to maintain reasonable levels of performance and portability, forces the application developers to leave the traditional programming paradigms and explore alternative solutions. PaStiX is a parallel sparse direct solver, based on a dynamic […]

January 30, 2014 by hgpu

We present a sparse direct solver using multilevel task scheduling on a modern heterogeneous compute node consisting of a multi-core host processor and multiple GPU accelerators. Our direct solver is based on the multifrontal method, which is characterized by exploiting dense subproblems (fronts) related in an assembly tree. Critical to high performance of the solver […]

February 21, 2013 by hgpu

Graphics processing units (GPU) have been investigated to release the computational capability in various scientific applications. Recent research shows that prudential consideration needs to be given to take the advantages of GPUs while avoiding the deficiency. In this paper, the impact of GPU acceleration to implicit integrators and explicit integrators in transient stability is investigated. […]

November 7, 2012 by hgpu

The current trend in the high performance computing shows a dramatic increase in the number of cores on the shared memory compute nodes. Algorithms, especially those related to linear algebra, need to be adapted to these new computer architectures in order to be efficient. PASTIX is a sparse parallel direct solver, that incorporates a dynamic […]

May 24, 2012 by hgpu

As hardware accelerators and especially GPUs become more and more popular to accelerate the compute intensive parts of an algorithm, standard high performance computing packages are starting to benefit from this trend. We present the first GPU acceleration of the ANSYS direct sparse solver. We explain how such a multifrontal solver may be accelerated using […]

February 22, 2011 by hgpu

We report on our experience with integrating and using graphics processing units (GPUs) as fast parallel floating-point co-processors to accelerate two fundamental computational scientific kernels on the GPU: sparse direct factorization and nonlinear interior-point optimization. Since a full re-implementation of these complex kernels is typically not feasible, we identify the matrix-matrix multiplication as a first […]

November 2, 2010 by hgpu