high performance computing on graphics processing units: hgpu.org

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PSCToolkit: solving sparse linear systems with a large number of GPUs

Pasqua D'Ambra, Fabio Durastante, Salvatore Filippone

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Tags: CUDA, Linear Algebra, Mathematical Software, Mathematics, Numerical Analysis, nVidia, nVidia A100, Package, Sparse

July 7, 2024 by hgpu

Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU

Mohammad Zubair, Christoph Bauinger

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Tags: Computer science, CUDA, Deep learning, Intel, Intel Data Center GPU Max 1550, Linear Algebra, Machine learning, Mathematical Software, Matrix multiplication, nVidia, nVidia V100, Sparse matrix, SYCL

November 5, 2023 by hgpu

ProtoX: A First Look

Het Mankad, Sanil Rao, Brian Van Straalen, Phillip Colella, Franz Franchetti

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Tags: Code generation, Computer science, Differential equations, Laplace and Poisson equation, Mathematical Software, Partial differential equations, PDEs, Poisson equation, Stencil computation

July 24, 2023 by hgpu

GPU Offloading in ExaHyPE Through C++ Standard Algorithms

Uzmar Gomez, Gonzalo Brito Gadeschi, Tobias Weinzierl

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Tags: Algorithms, Computer science, CUDA, Differential equations, Mathematical Software, nVidia, nVidia GeForce RTX 2080 Ti, OpenMP, Partial differential equations, PDEs

February 26, 2023 by hgpu

TorchOpt: An Efficient Library for Differentiable Optimization

Jie Ren, Xidong Feng, Bo Liu, Xuehai Pan, Yao Fu, Luo Mai, Yaodong Yang

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Tags: Computer science, CUDA, Deep learning, Mathematical Software, nVidia, OpenMP, Package, PyTorch

November 20, 2022 by hgpu

Least Squares on GPUs in Multiple Double Precision

Jan Verschelde

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Tags: Code generation, Computer science, CUDA, Mathematical Software, nVidia, Package, Tesla P100, Tesla V100

October 24, 2021 by hgpu

cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs

Yu-hsuan Shih, Garrett Wright, Joakim Andén, Johannes Blaschke, Alex H. Barnett

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Tags: CUDA, FFT, Image reconstruction, Mathematical Software, Numerical Analysis, nVidia, Package, Signal processing, Tesla V100

February 21, 2021 by hgpu

AMGCL – A C++ library for efficient solution of large sparse linear systems

Denis Demidov

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Tags: Computer science, CUDA, Mathematical Software, MPI, OpenCL, Package, Sparse, Sparse linear iterative solvers

November 8, 2020 by hgpu

Portable high-order finite element kernels I: Streaming Operations

Noel Chalmers, Tim Warburton

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Tags: AMD, AMD Radeon Instinct MI60, AMD Radeon VII, Computer science, CUDA, FEM, HIP, Mathematical Software, nVidia, nVidia GeForce GTX Titan V, OCCA, Portability, Tesla V100

October 18, 2020 by hgpu

Flexible Performant GEMM Kernels on GPUs

Thomas Faingnaert, Tim Besard, Bjorn De Sutter

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Tags: Computer science, CUBLAS, CUDA, Julia, Machine learning, Mathematical Software, Matrix multiplication, Mixed precision, nVidia, nVidia GeForce RTX 2080 Ti, Package, Performance

October 4, 2020 by hgpu

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

Hartwig Anzt, Terry Cojean, Goran Flegar, Fritz Göbel, Thomas Grützmacher, Pratik Nayak, Tobias Ribizel, Yuhsiang Mike Tsai, Enrique S. Quintana-Ortí

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Tags: Algorithms, AMD Radeon VII, ATI, Computer science, CUDA, HIP, Linear Algebra, Mathematical Software, nVidia, Package, Sparse, Sparse matrix, Tesla V100

July 5, 2020 by hgpu

Preparing Ginkgo for AMD GPUs – A Testimonial on Porting CUDA Code to HIP

Yuhsiang M. Tsai, Terry Cojean, Tobias Ribizel, Hartwig Anzt

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Tags: AMD Radeon VII, ATI, Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, Package

June 28, 2020 by hgpu

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

EnergyUCB-Bandit

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

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high performance computing on graphics processing units: hgpu.org

PSCToolkit: solving sparse linear systems with a large number of GPUs

Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU

ProtoX: A First Look

GPU Offloading in ExaHyPE Through C++ Standard Algorithms

TorchOpt: An Efficient Library for Differentiable Optimization

Least Squares on GPUs in Multiple Double Precision

cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs

AMGCL – A C++ library for efficient solution of large sparse linear systems

Portable high-order finite element kernels I: Streaming Operations

Flexible Performant GEMM Kernels on GPUs

Ginkgo: A Modern Linear Operator Algebra Framework for High Performance Computing

Preparing Ginkgo for AMD GPUs – A Testimonial on Porting CUDA Code to HIP

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Over-synchronization in GPU Programs

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

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

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

EnergyUCB-Bandit

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