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Weifeng Liu, Brian Vinter
Sparse matrix-vector multiplication (SpMV) is a central building block for scientific software and graph applications. Recently, heterogeneous processors composed of different types of cores attracted much attention because of their flexible core configuration and high energy efficiency. In this paper, we propose a compressed sparse row (CSR) format based SpMV algorithm utilizing both types of […]
E. Coronado-Barrientos, G. Indalecio, A. Garcia-Loureiro
The present work is an analysis of the performance of the AXPY, DOT and SpMV functions using OpenCL. The code was tested on the NVIDIA Tesla S2050 GPU and Intel Xeon Phi 3120A coprocessor. Due to nature of the AXPY function, only two versions were implemented, the routine to be executed by the CPU and […]
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Sencer Nuri Yeralan, Timothy A. Davis, Sanjay Ranka
Sparse matrix factorization involves a mix of regular and irregular computation, which is a particular challenge when trying to obtain high-performance on the highly parallel general-purpose computing cores available on graphics processing units (GPUs). We present a sparse multifrontal QR factorization method that meets this challenge, and is up to eleven times faster than a […]
Weifeng Liu, Brian Vinter
General sparse matrix-matrix multiplication (SpGEMM) is a fundamental building block for numerous applications such as algebraic multigrid method (AMG), breadth first search and shortest path problem. Compared to other sparse BLAS routines, an efficient parallel SpGEMM implementation has to handle extra irregularity from three aspects: (1) the number of nonzero entries in the resulting sparse […]
Gloria Ortega Lopez
This thesis, entitled "High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study" investigates the computational issues related to the resolution of linear systems of equations which come from the discretization of physical models described by means of Partial Differential Equations (PDEs). These physical models are conceived for the […]
Weifeng Liu, Brian Vinter
Sparse matrix-vector multiplication (SpMV) is a fundamental building block for numerous applications. In this paper, we propose CSR5 (Compressed Sparse Row 5), a new storage format, which offers high-throughput SpMV on various platforms including CPUs, GPUs and Xeon Phi. First, the CSR5 format is insensitive to the sparsity structure of the input matrix. Thus the […]
Lukas Polok, Viorela Ila, Pavel Smrz
Sparse matrix multiplication is an important algorithm in a wide variety of problems, including graph algorithms, simulations and linear solving to name a few. Yet, there are but a few works related to acceleration of sparse matrix multiplication on a GPU. We present a fast, novel algorithm for sparse matrix multiplication, outperforming the previous algorithm […]
Lukas Polok, Viorela Ila, Pavel Smrz
Fast sorting is an important step in many parallel algorithms, which require data ranking, ordering or partitioning. Parallel sorting is a widely researched subject, and many algorithms were developed in the past. In this paper, the focus is on implementing highly efficient sorting routines for the sparse linear algebra operations, such as parallel sparse matrix […]
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Ang Li, Hammad Mazhar, Radu Serban, Dan Negrut
ViennaCL is a free open-source linear algebra library for computations on many-core architectures (GPUs, MIC) and multi-core CPUs. The library is written in C++ and supports CUDA, OpenCL, and OpenMP. In addition to core functionality and many other features including BLAS level 1-3 support and iterative solvers, the latest release family ViennaCL 1.6.x provides fast […]
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Arash Ashari
Sparse Matrix-Vector multiplication (SpMV) is one of the key operations in linear algebra. Overcoming thread divergence, load imbalance and un-coalesced and indirect memory access due to sparsity and irregularity are challenges to optimizing SpMV on GPUs. This dissertation develops solutions that address these challenges effectively. The first part of this dissertation focuses on a new […]
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Hammad Mazhar, Dan Negrut
This technical report provides performance numbers for several benchmark problems running on several different hardware platforms. The goal of this report is twofold. First, it helps us better understand how the performance of OpenCL changes on different platforms. Second, it provides a OpenCL-OpenMP comparison for a sparse matrix-vector multiplication operation. The VexCL library will be […]
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Emanuel H. Rubensson, Elias Rudberg
We present a library for parallel block-sparse matrix-matrix multiplication on distributed memory clusters. The library is based on the Chunks and Tasks programming model [Parallel Comput. 40, 328 (2014)]. Acting as matrix library developers, using this model we do not have to explicitly deal with distribution of work and data or communication between computational nodes […]
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