high performance computing on graphics processing units: hgpu.org

hgpu.org » Benchmarking

Resource-Aware Compiler Prefetching for Fine-Grained Many-Cores

George C. Caragea, Alexandros Tzannes, Fuat Keceli, Rajeev Barua, Uzi Vishkin

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Tags: Algorithms, ASIC, Benchmarking, Computer science, Memory level parallelism, nVidia, Package, Prefetch

December 29, 2011 by hgpu

Acceleration of PIC Simulation with GPU

Junya Suzuki, Hironori Shimazu, Keiichiro Fukazawa, Mitsue Den

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Tags: Benchmarking, CUDA, nVidia, Particle-in-cell methods, Physics, Plasma physics, Tesla C1060

December 29, 2011 by hgpu

Visualization assisted by parallel processing

B. Lange, W. Puech, and N. Rodriguez, H. Rey, X. Vasques

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Tags: Algorithms, Benchmarking, Computer science, nVidia, nVidia GeForce GT 330, OpenCL, Rendering, Visualization, Voronoi diagram

December 29, 2011 by hgpu

Enabling New Uses for GPUs

Matthew D. Sinclair

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro FX 580, Tesla C1060, Thesis

December 27, 2011 by hgpu

Fast Parallel Machine Learning Algorithms for Large Datasets Using Graphic Processing Unit

Qi Li

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, Optimization, Sorting, Tesla C1060, Tesla C2050, Tesla C2070, Thesis

December 26, 2011 by hgpu

Survey on Benchmarks for a GPU Based Multi Camera Stereo Matching Algorithm

Klaus Denker, Georg Umlauf

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Tags: Algorithms, Benchmarking, Computer science, Computer vision, GLSL, OpenGL

December 23, 2011 by hgpu

GPU-based parallel collision detection for real-time motion planning

Jia Pan, Dinesh Manocha

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Tags: Algorithms, Benchmarking, Clustering, Collision detection, Computer science, CUDA, nVidia, nVidia GeForce GTX 480

December 22, 2011 by hgpu

Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU

Jonathan Passerat-Palmbach, Jonathan Caux, Pridi Siregar, Claude Mazel, D.R.C.Hill

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Tags: Benchmarking, Computer science, CUDA, nVidia, Stochastic simulation, Tesla C2050, Warp level parallelism

December 21, 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

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

A Translation Framework for Executing the Sequential Binary Code on CPU/GPU Based Architectures

Erzhou Zhu, Haibing Guan, Guoxing Dong, Yindong Yang, Hongbo Yang

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Tags: Benchmarking, Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, PTX

December 18, 2011 by hgpu

A Predictive Model for Solving Small Linear Algebra Problems in GPU Registers

Michael J. Anderson, David Sheffield, Kurt Keutzer

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Tags: Benchmarking, Computer science, CUDA, Factorization, Linear Algebra, nVidia, nVidia Quadro FX 6000, Package, Signal processing

December 16, 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

Resource-Aware Compiler Prefetching for Fine-Grained Many-Cores

Acceleration of PIC Simulation with GPU

Visualization assisted by parallel processing

Enabling New Uses for GPUs

Fast Parallel Machine Learning Algorithms for Large Datasets Using Graphic Processing Unit

Survey on Benchmarks for a GPU Based Multi Camera Stereo Matching Algorithm

GPU-based parallel collision detection for real-time motion planning

Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU

Analysis of GPGPU Platforms Efficiency in General-Purpose Computations

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

A Translation Framework for Executing the Sequential Binary Code on CPU/GPU Based Architectures

A Predictive Model for Solving Small Linear Algebra Problems in GPU Registers

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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