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Parallel mutual information estimation for inferring gene regulatory networks on GPUs

Haixiang Shi, Bertil Schmidt, Weiguo Liu, Wolfgang Muller-Wittig

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Tags: Algorithms, Biology, Computational biology, CUDA, Image processing, Machine learning, nVidia, Package, Tesla C2050

January 13, 2012 by hgpu

Scientific Computing with Python on GPUs

Stefan Reiterer

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Tags: Algorithms, Computer science, CUDA, nVidia, Python, Tutorial

January 13, 2012 by hgpu

An FPGA-based processing pipeline for high definition stereo video

Pierre Greisen, Simon Heinzle, Markus Gross, Andreas Peter Burg

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Tags: Algorithms, CUDA, FPGA, Image processing, nVidia, nVidia GeForce GTX 480, Stereo vision

January 13, 2012 by hgpu

Mesh deformations in X3D via CUDA with freeform deformation lattices

Yvonne Jung, Holger Graf, Johannes Behr, Arjan Kuijper

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Tags: Algorithms, Computer science, CUDA, Interactive application, nVidia, nVidia GeForce GTX 295

January 13, 2012 by hgpu

Enhancing Data Parallelism for Ant Colony Optimisation on GPUs

Jose M. Cecilia, Jose M. Garcia, Andy Nisbet, Martyn Amos, Manuel Ujaldon

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Metaheuristics, nVidia, Tesla C2050

January 12, 2012 by hgpu

Incomplete-LU and Cholesky Preconditioned Iterative Methods Using CUSPARSE and CUBLAS

Maxim Naumov

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, Linear Algebra, nVidia, Tesla C2050

January 12, 2012 by hgpu

Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

Hartwig Anzt, Maribel Castillo, Juan C. Fernandez, Vincent Heuveline, Francisco D. Igual, Rafael Mayo and Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Mixed precision, nVidia, Tesla C1060

January 11, 2012 by hgpu

A parallel Genetic Programming algorithm for classification

Alberto Cano, Amelia Zafra, Sebastian Ventura

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 480

January 11, 2012 by hgpu

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms

Hartwig Anzt, Vincent Heuveline, Jose I. Aliaga, Maribel Castillo, Juan C. Fernandez, Rafael Mayo, Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, nVidia, Optimization, Sparse linear iterative solvers, Tesla C1060

January 11, 2012 by hgpu

Exponential integrators on graphic processing units

Lukas Einkemmer

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 9800 GTX, Tesla C1060, Thesis

January 11, 2012 by hgpu

Implementing Genetic Algorithms to CUDA Environment Using Data Parallelization

Masashi Oiso, Yoshiyuki Matsumura, Toshiyuki Yasuda, Kazuhiro Ohkura

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 280

January 11, 2012 by hgpu

Parallel LDPC decoding using CUDA and OpenMP

Joo-Yul Park, Ki-Seok Chung

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, Parity-check codes

January 11, 2012 by hgpu

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

Iris: First-Class Multi-GPU Programming Experience in Triton

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

Parallel mutual information estimation for inferring gene regulatory networks on GPUs

Scientific Computing with Python on GPUs

An FPGA-based processing pipeline for high definition stereo video

Mesh deformations in X3D via CUDA with freeform deformation lattices

Enhancing Data Parallelism for Ant Colony Optimisation on GPUs

Incomplete-LU and Cholesky Preconditioned Iterative Methods Using CUSPARSE and CUBLAS

Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

A parallel Genetic Programming algorithm for classification

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms

Exponential integrators on graphic processing units

Implementing Genetic Algorithms to CUDA Environment Using Data Parallelization

Parallel LDPC decoding using CUDA and OpenMP

Recent source codes

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

NVIDIA Nemotron Parse 1.1

ThunderKittens: Tile primitives for speedy kernels

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

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