high performance computing on graphics processing units: hgpu.org

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FC_ACCEL: Enabling Efficient, Low-Latency and Flexible Inference in DNN Fully Connected Layers, using Optimized Checkerboard Block matrix decomposition, fast scheduling, and a resource efficient 1D PE array with a custom HBM2 memory subsystem

Nick Iliev, Amit R Trivedi

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Tags: CNN, Computer science, Deep learning, FPGA, Matrix decomposition, Neural networks, nVidia, nVidia Jetson AGX Xavier, Performance

February 13, 2022 by hgpu

Using reconfigurable computing technology to accelerate matrix decomposition and applications

Xinying Wang

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Tags: Computer science, Data mining, Factorization, FPGA, Intel Xeon Phi, Matrix decomposition, Semantic indexing, Signal processing, Text mining, Thesis

December 28, 2017 by hgpu

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

Joao Paulo Tarasconi Ruschel

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, OpenCL, OpenMP, Package, Performance, Tesla K80, Thesis

January 26, 2017 by hgpu

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

Ichitaro Yamazaki, Stanimire Tomov, Jack Dongarra

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Tags: BLAS, Computer science, CUDA, Linear Algebra, Matrix decomposition, Mixed precision, Numerical Analysis, nVidia, Performance, Tesla K20

July 16, 2016 by hgpu

RSVDPACK: Subroutines for computing partial singular value decompositions via randomized sampling on single core, multi core, and GPU architectures

Sergey Voronin, Per-Gunnar Martinsson

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Tags: Algorithms, CUDA, CULA, Mathematical Software, Mathematics, Matrix decomposition, Numerical Analysis, nVidia, Package, Tesla M2070

February 22, 2015 by hgpu

Computation on GPU of Eigenvalues and Eigenvectors of a Large Number of Small Hermitian Matrices

Alain Cosnuau

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, Tesla M2070

June 27, 2014 by hgpu

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Gary Macindoe

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, Machine learning, Matrix decomposition, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla S1070, Thesis

January 29, 2014 by hgpu

Tiled QR Decomposition and Its Optimization on CPU and GPU Computing System

Dongjin Kim, Kyu-Ho Park

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Matrix decomposition, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680

November 8, 2013 by hgpu

GPU-Accelerated Scalable Solver for Banded Linear Systems

Hang Liu, Jung-Hee Seo, Rajat Mittal, H. Howie Huang

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Tags: Computer science, CUDA, Diffusion equation, Matrix decomposition, MPI, nVidia, OpenMPI, Poisson equation, Tesla M2050, Tesla M2090

August 16, 2013 by hgpu

Parallel perfusion imaging processing using GPGPU

Fan Zhu, David Rodriguez Gonzalez, Trevor Carpenter, Malcolm Atkinson, Joanna Wardlaw

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Tags: Algorithms, CUDA, Matrix decomposition, Medicine, nVidia, nVidia GeForce GTX 280

August 5, 2012 by hgpu

A GPU-based Approximate SVD Algorithm

Blake Foster, Sridhar Mahadevan, Rui Wang

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Tags: Algorithms, Computer science, CUDA, Matrix decomposition, nVidia, nVidia GeForce GTX 480, Package

December 15, 2011 by hgpu

Effective GPU Strategies for LU Decomposition

Dinesh Bandara, Nalin Ranasinghe

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Tags: Algorithms, Computer science, CUDA, Matrix decomposition, nVidia, Tesla C1060

December 2, 2011 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

FC_ACCEL: Enabling Efficient, Low-Latency and Flexible Inference in DNN Fully Connected Layers, using Optimized Checkerboard Block matrix decomposition, fast scheduling, and a resource efficient 1D PE array with a custom HBM2 memory subsystem

Using reconfigurable computing technology to accelerate matrix decomposition and applications

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

Stability and Performance of Various Singular Value QR Implementations on Multicore CPU with a GPU

RSVDPACK: Subroutines for computing partial singular value decompositions via randomized sampling on single core, multi core, and GPU architectures

Computation on GPU of Eigenvalues and Eigenvectors of a Large Number of Small Hermitian Matrices

Hybrid algorithms for efficient Cholesky decomposition and matrix inverse using multicore CPUs with GPU accelerators

Tiled QR Decomposition and Its Optimization on CPU and GPU Computing System

GPU-Accelerated Scalable Solver for Banded Linear Systems

Parallel perfusion imaging processing using GPGPU

A GPU-based Approximate SVD Algorithm

Effective GPU Strategies for LU Decomposition

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

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

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

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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