high performance computing on graphics processing units: hgpu.org

hgpu.org » Matrix multiplication

PTX2Kernel: Converting PTX Code into Compilable Kernels

Qiankun Dong, Tao Li, Shuai Zhang, Xiaofan Jiao, Jiabing Leng

View

Download (PDF)

Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 480, PTX, Tesla K40

March 22, 2015 by hgpu

Fast Sparse Matrix Multiplication on GPU

Lukas Polok, Viorela Ila, Pavel Smrz

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 680, OpenCL, Package, Sorting, Sparse matrix, Tesla K40

March 18, 2015 by hgpu

Fast Radix Sort for Sparse Linear Algebra on GPU

Lukas Polok, Viorela Ila, Pavel Smrz

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 780, OpenCL, Sorting, Sparse matrix

March 18, 2015 by hgpu

Exploiting Concurrency Patterns with Heterogeneous Task and Data Parallelism

A Geetha Venkatesh

View

Download (PDF)

Tags: Computer science, Data parallelism, Heterogeneous systems, Matrix multiplication, nVidia, OpenCL, Tesla C2070, Thesis

February 3, 2015 by hgpu

Locality-aware parallel block-sparse matrix-matrix multiplication using the Chunks and Tasks programming model

Emanuel H. Rubensson, Elias Rudberg

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Matrix multiplication, MPI, nVidia, Sparse matrix, Tesla K20

February 2, 2015 by hgpu

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat

View

Download (PDF)

Tags: Algorithms, ATI, ATI Radeon HD 7970, Computer science, Matrix multiplication, nVidia, OpenCL, Performance, Tesla K20

January 21, 2015 by hgpu

A New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems

Jonathan Wong, Ellen Kuhl, Eric Darve

View

Download (PDF)

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

January 5, 2015 by hgpu

Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

Michael Driscoll

View

Download (PDF)

Source codes

Tags: 3D Graphics and Realism, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sparse matrix

January 5, 2015 by hgpu

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

Jose M. Andion, Manuel Arenaz, Francois Bodin, Gabriel Rodriguez, Juan Tourino

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, Tesla S1070, Tesla S2050

December 14, 2014 by hgpu

Implementing Level-3 BLAS Routines in OpenCL on Different Processing Units

Kazuya Matsumoto, Naohito Nakasato, Stanislav Sedukhin

View

Download (PDF)

Tags: ATI, ATI Radeon HD 7970, BLAS, Computer science, Intel Xeon Phi, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX Titan, OpenCL

October 29, 2014 by hgpu

A Study of the Potential of Locality-Aware Thread Scheduling for GPUs

Cedric Nugteren, Gert-Jan van den Braak, Henk Corporaal

View

Download (PDF)

Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 580, OpenCL, Task scheduling

September 28, 2014 by hgpu

Optimization of Data-Parallel Scientific Applications on Highly Heterogeneous Modern HPC Platforms

Ziming Zhong

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, Numerical simulation, nVidia, Tesla S2050, Thesis

August 27, 2014 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

PTX2Kernel: Converting PTX Code into Compilable Kernels

Fast Sparse Matrix Multiplication on GPU

Fast Radix Sort for Sparse Linear Algebra on GPU

Exploiting Concurrency Patterns with Heterogeneous Task and Data Parallelism

Locality-aware parallel block-sparse matrix-matrix multiplication using the Chunks and Tasks programming model

Reproducible and Accurate Matrix Multiplication for GPU Accelerators

A New Sparse Matrix Vector Multiplication GPU Algorithm Designed for Finite Element Problems

Subdivision Surface Evaluation as Sparse Matrix-Vector Multiplication

Locality-Aware Automatic Parallelization for GPGPU with OpenHMPP Directives

Implementing Level-3 BLAS Routines in OpenCL on Different Processing Units

A Study of the Potential of Locality-Aware Thread Scheduling for GPUs

Optimization of Data-Parallel Scientific Applications on Highly Heterogeneous Modern HPC Platforms

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

Most viewed papers (last 30 days)