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GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Bartosz D. Wozniak

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Tags: CUBLAS, CUDA, Fluid dynamics, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

July 7, 2014 by hgpu

Mixed-precision Orthogonalization Scheme and Adaptive Step Size for CA-GMRES on GPUs

Ichitaro Yamazaki, Stanimire Tomov, Tingxing Dong, Jack Dongarra

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Tags: Computer science, Linear Algebra, nVidia, Performance, Tesla K20, Tesla K40, Tesla M2090

July 1, 2014 by hgpu

A Fast Batched Cholesky Factorization on a GPU

Tingxing Dong, Azzam Haidar, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Package, Tesla K40

June 12, 2014 by hgpu

targetDP: an Abstraction of Lattice Based Parallelism with Portable Performance

Alan Gray, Kevin Stratford

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Tags: CUDA, Data parallelism, Fluid dynamics, Intel Xeon Phi, Lattice Boltzmann model, nVidia, Tesla K40

May 20, 2014 by hgpu

Exercising high-level parallel programming on streams: a systems biology use case

Marco Aldinucci, Maurizio Drocco, Guilherme Peretti Pezzi, Claudia Misale, Fabio Tordini, Massimo Torquati

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Tags: Bioinformatics, Biology, Cloud, CUDA, Monte Carlo simulation, nVidia, Tesla K40

April 30, 2014 by hgpu

Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures

Hartwig Anzt, Dimitar Lukarski, Stanimire Tomov, Jack Dongarra

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, nVidia, OpenCL, Tesla K40

April 27, 2014 by hgpu

Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-sigma formats on NVIDIA GPUs

Hartwig Anzt, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sorting, Sparse matrix, Tesla K40

April 7, 2014 by hgpu

Multi-Elimination ILU Preconditioners on GPUs

Dimitar Lukarski, Hartwig Anzt, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, nVidia, Tesla K40

February 23, 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

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high performance computing on graphics processing units: hgpu.org

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Mixed-precision Orthogonalization Scheme and Adaptive Step Size for CA-GMRES on GPUs

A Fast Batched Cholesky Factorization on a GPU

targetDP: an Abstraction of Lattice Based Parallelism with Portable Performance

Self-Adaptive Multiprecision Preconditioners on Multicore and Manycore Architectures

Implementing a Sparse Matrix Vector Product for the SELL-C/SELL-C-sigma formats on NVIDIA GPUs

Multi-Elimination ILU Preconditioners on GPUs

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

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