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GPU Sparse Matrix Multiplication with CUDA

Sean Rose

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tutorial

May 9, 2013 by hgpu

Exploration of Multifrontal Method with GPU in Power Flow Computation

Xue Li, Fangxing Li, Joshua M. Clark

Tags: Computer science, CUDA, Factorization, Linear Algebra, nVidia, Sparse matrix, Tesla M2070

May 4, 2013 by hgpu

GPU-based Steady-State Solution of the Chemical Master Equation

Marco Maggioni, Tanya Berger-Wolf, Jie Liang

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Tags: Biology, CUDA, Macromolecule, nVidia, nVidia GeForce GTX 580, Probability, Sparse matrix

May 1, 2013 by hgpu

Reduce, Reuse, Recycle (R^3): a Design Methodology for Sparse Matrix Vector Multiplication on Reconfigurable Platforms

Kevin Townsend, Joseph Zambreno

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Tags: Computer science, CUDA, nVidia, Sparse matrix, Tesla M2090, Tesla S1070

April 23, 2013 by hgpu

A Hardware Multithreaded SpMV Kernel for the Convey HC-2ex

Robert Halstead, Walid Najjar

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Tags: Computer science, CUDA, FPGA, nVidia, nVidia GeForce GTX 280, Sparse matrix

April 8, 2013 by hgpu

Optimizing Sparse Matrix-Matrix Multiplication for the GPU

Steven Dalton, Nathan Bell, Luke N. Olson

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Sorting, Sparse matrix, Tesla C2075

April 7, 2013 by hgpu

CPU and/or GPU: Revisiting the GPU Vs. CPU Myth

Kishore Kothapalli, Dip Sankar Banerjee, P. J. Narayanan, Surinder Sood, Aman Kumar Bahl, Shashank Sharma, Shrenik Lad, Krishna Kumar Singh, Kiran Matam, Sivaramakrishna Bharadwaj, Rohit Nigam, Parikshit Sakurikar, Aditya Deshpande, Ishan Misra, Siddharth Choudhary, Shubham Gupta

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Tags: Computer science, Databases, Hybrid computing, Image processing, nVidia, nVidia GeForce GT 520, Sparse matrix, Tesla T10

March 14, 2013 by hgpu

Performance Evaluation of Sparse Matrix Multiplication Kernels on Intel Xeon Phi

Erik Saule, Kamer Kaya, Umit V. Catalyurek

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Tags: Computer science, CUDA, Hardware Architecture, Intel, Intel Phi, Matrix multiplication, nVidia, Performance, Sparse matrix, Tesla C2050, Tesla K20

February 6, 2013 by hgpu

Implementing Sparse Matrix-Vector Multiplication with QCSR on GPU

Jilin Zhang, Enyi Liu, Jian Wan, Yongjian Ren, Miao Yue, Jue Wang

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Tags: Computer science, CUDA, nVidia, Sparse matrix

January 12, 2013 by hgpu

Approximate Subdivision Surface Evaluation in the Language of Linear Algebra

Michael Driscoll

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Tags: 3D Graphics and Realism, Computer science, CUDA, Image generation, Linear Algebra, nVidia, nVidia GeForce GTX 480, Sparse matrix

January 5, 2013 by hgpu

A Parallel Preconditioned Bi-Conjugate Gradient Stabilized Solver for the Poisson Problem

Ning Zhao, Xuben Wang

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 610 M, Sparse matrix

December 20, 2012 by hgpu

Sparse Matrix Multiplication using CUDA and Mex Interface

Abhishek Deshpande

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Tags: Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 480, Sparse matrix

December 20, 2012 by hgpu

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

Kokkidio: Fast, expressive, portable code, based on Kokkos and Eigen

LLaMA-Inference-Bench

LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators

MATAR

On a Simplified Approach to Achieve Parallel Performance and Portability Across CPU and GPU Architectures

Over-synchronization in GPU Programs

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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