high performance computing on graphics processing units: hgpu.org

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SpMV: A Memory-Bound Application on the GPU Stuck Between a Rock and a Hard Place

John D. Davis, Eric S. Chung

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680, Sparse matrix

September 22, 2012 by hgpu

Accelerating Sparse Matrix Kernels on Graphics Processing Units

Kiran Kumar Matam

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla C1060, Tesla C2050, Thesis

September 14, 2012 by hgpu

Multi-GPU Implementation of the Uniformization Method for Solving Markov Models

Marek Karwacki, Beata Bylina, Jaroslaw Bylina

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Tags: Computer science, CUDA, nVidia, Sparse matrix, Tesla M2050

August 31, 2012 by hgpu

Large Scale Finite Element Analysis Using GPU Parallel Computing

Ashkan Akbariyeh

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Tags: Algorithms, Computer science, CUDA, Finite element method, nVidia, nVidia Quadro FX 5000, Sparse matrix, Thesis

August 8, 2012 by hgpu

Improving GPU Sparse Matrix-Vector Multiplication for Probabilistic Model Checking

Anton J. Wijs, Dragan Bosnacki

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Probabilistic model checking, Sparse matrix

August 6, 2012 by hgpu

Analysis and performance estimation of the conjugate gradient method on multiple GPUs

Mickeal Verschoor, Andrei C. Jalba

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Tags: Computer science, Conjugate gradient solver, CUDA, GPU cluster, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 570, Performance, Sparse matrix

August 4, 2012 by hgpu

New Sparse Matrix Storage Format to Improve The Performance of Total SPMV Time

Neelima Reddy, Raghavendra Prakash, Ram Mohana Reddy

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 470, Sparse matrix

August 1, 2012 by hgpu

Sparse Matrix-Vector Multiplication on NVIDIA GPU

Hui Liu, Song Yu, Zhangxin Chen, Ben Hsieh, Lei Shao

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Tags: Computer science, CUDA, nVidia, Sparse matrix, Tesla C2050, Tesla C2070

July 16, 2012 by hgpu

Sparse Matrix Matrix Multiplication on Hybrid CPU+GPU Platforms

Kiran Kumar Matam, Siva Rama Krishna Bharadwaj, Kishore Kothapalli

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Optimization, Sparse matrix, Tesla C2050

July 16, 2012 by hgpu

Stencil-Aware GPU Optimization of Iterative Solvers

Chekuri Choudary, Jeswin Godwin, Justin Holewinski, Deepan Karthik, Daniel Lowell, Azamat Mametjanov, Boyana Norris, Gerald Sabin, P. Sadayappan

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Tags: Computer science, CUDA, Differential equations, nVidia, Optimization, Partial differential equations, PDEs, Sparse matrix, Tesla C2050, Tesla M2070

July 11, 2012 by hgpu

Elastically Deformable Models based on the Finite Element Method Accelerated on Graphics Hardware using CUDA

Mickeal Verschoor, Andrei C. Jalba

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, nVidia GeForce GTX 570, Sparse matrix

July 9, 2012 by hgpu

Autotuning Stencil-Based Computations on GPUs

Azamat Mametjanov, Daniel Lowell, Ching-Chen May, Boyana Norris

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Tags: Computer science, CUDA, Differential equations, nVidia, Package, Partial differential equations, PDEs, Sparse matrix, Tesla C1060, Tesla C2070

June 9, 2012 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

SpMV: A Memory-Bound Application on the GPU Stuck Between a Rock and a Hard Place

Accelerating Sparse Matrix Kernels on Graphics Processing Units

Multi-GPU Implementation of the Uniformization Method for Solving Markov Models

Large Scale Finite Element Analysis Using GPU Parallel Computing

Improving GPU Sparse Matrix-Vector Multiplication for Probabilistic Model Checking

Analysis and performance estimation of the conjugate gradient method on multiple GPUs

New Sparse Matrix Storage Format to Improve The Performance of Total SPMV Time

Sparse Matrix-Vector Multiplication on NVIDIA GPU

Sparse Matrix Matrix Multiplication on Hybrid CPU+GPU Platforms

Stencil-Aware GPU Optimization of Iterative Solvers

Elastically Deformable Models based on the Finite Element Method Accelerated on Graphics Hardware using CUDA

Autotuning Stencil-Based Computations 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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