high performance computing on graphics processing units: hgpu.org

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hgpu.org » Applications » Computer science

Performance and Quality of Random Number Generators

V. du Preez, M.G.B. Johnson, A. Leist, K.A. Hawick

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Tags: Algorithms, Computer science, CUDA, Monte Carlo simulation, Numerical simulation, nVidia, nVidia GeForce GTX 580, Pseudo-random number generators, Review

December 20, 2011 by hgpu

A Framework for Genetic Algorithms in Parallel Environments

Tomoyuki Hiroyasu, Ryosuke Yamanaka, Masato Yoshimi, Mitsunori Miki

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Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, nVidia GeForce GTX 250

December 20, 2011 by hgpu

Analysis of GPGPU Platforms Efficiency in General-Purpose Computations

Pavel Kartashev, Vladislavs Nazaruk

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Tags: ATI, ATI Radeon HD 5750, Benchmarking, Computer science, nVidia, nVidia GeForce GT 240, OpenCL, Performance

December 20, 2011 by hgpu

Can CUDA be exposed through web services?

Massimo Becker

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Tags: Computer science, CUDA, Networks, nVidia, OpenCL, Overview, Thesis

December 20, 2011 by hgpu

Parsing in Parallel on Multiple Cores and GPUs

Mark Johnson

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Tags: Algorithms, Computer science, CUDA, nVidia, Tesla S2050

December 20, 2011 by hgpu

The Future in Mobile Multicore Computing

Blake Hurd, Chiu C. Tan, Jie Wu

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Tags: Computer science, Hardware Architecture, Performance

December 20, 2011 by hgpu

Floating-point Mixed-radix FFT Core Generation for FPGA and Comparison with GPU and CPU

Bo Duan, Wendi Wang, Xingjian Li, Chunming Zhang, Peiheng Zhang, Ninghui Sun

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Tags: Algorithms, Computer science, CUDA, FFT, FPGA, nVidia, nVidia GeForce GTX 480, Optimization, Performance

December 20, 2011 by hgpu

Fast Random Graph Generation

Sadegh Heyrani Nobari, Xuesong Lu, Panagiotis Karras, Stephane Bressan

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Tags: Algorithms, Computer science, CUDA, Databases, Graph theory, nVidia, nVidia GeForce 9800 GT, Package

December 19, 2011 by hgpu

GPU-Accelerated Preconditioned Iterative Linear Solvers

Ruipeng Li, Yousef Saad

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Tags: Computer science, Conjugate gradient solver, CUDA, Factorization, Linear Algebra, nVidia, Overview, Sparse matrix, Tesla C1060

December 19, 2011 by hgpu

Optimization of mapped functions sequences using fusions on GPU

Jan Fousek

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Tags: Algorithms, Computer science, CUDA, MPI, nVidia, nVidia GeForce GTX 480, Optimization, Thesis

December 19, 2011 by hgpu

Towards Automatic C Programs Optimization and Parallelization using the PIPS-PoCC Integration

Dounia Khaldi, Corinne Ancourt, Francois Irigoin

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, Heterogeneous systems, MPI, nVidia, OpenMP, Optimization, Package

December 19, 2011 by hgpu

Experiences Developing the OpenUH Compiler and Runtime Infrastructure

Barbara Chapman, Deepak Eachempati, Oscar Hernandez

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Tags: Code generation, Compilers, Computer science, nVidia, Package, PTX

December 19, 2011 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

Applications

Performance and Quality of Random Number Generators

A Framework for Genetic Algorithms in Parallel Environments

Analysis of GPGPU Platforms Efficiency in General-Purpose Computations

Can CUDA be exposed through web services?

Parsing in Parallel on Multiple Cores and GPUs

The Future in Mobile Multicore Computing

Floating-point Mixed-radix FFT Core Generation for FPGA and Comparison with GPU and CPU

Fast Random Graph Generation

GPU-Accelerated Preconditioned Iterative Linear Solvers

Optimization of mapped functions sequences using fusions on GPU

Towards Automatic C Programs Optimization and Parallelization using the PIPS-PoCC Integration

Experiences Developing the OpenUH Compiler and Runtime Infrastructure

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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