high performance computing on graphics processing units: hgpu.org

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Achieving High Throughput Sequencing with Graphics Processing Units

Su Chen, Chaochao Zhang, Feng Shen, Ling Bai, Hai Jiang, Damir Herman

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Tags: Algorithms, Bioinformatics, Computer science, CUDA, nVidia, Sequence alignment, Tesla C2050

December 10, 2011 by hgpu

Fast and Memory-Efficient Minimum Spanning Tree on the GPU

Scott Rostrup, Shweta Srivastava, Kishore Singhal

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Tags: Algorithms, Computer science, Graph theory, nVidia, Performance, Tesla S1070, Tesla T10

December 10, 2011 by hgpu

Playdoh: A lightweight Python library for distributed computing and optimisation

Cyrille Rossant, Bertrand Fontaine, Dan F.M. Goodman

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Source codes

Tags: Computer science, CUDA, Differential equations, Distributed computing, Monte Carlo simulation, nVidia, nVidia GeForce GTX 295, Package, Partial differential equations, PDEs, Python

December 10, 2011 by hgpu

Load Balancing Utilizing Data Redundancy in Distributed Volume Rendering

S. Frey, T. Ertl

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Rendering

December 9, 2011 by hgpu

A design tool for efficient mapping of multimedia applications onto heterogeneous platforms

Chung-Ching Shen, Hsiang-Huang Wu, Nimish Sane, William Plishker, Shuvra S. Bhattacharyya

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Tags: Computer science, CUDA, Flow analysis, Heterogeneous systems, nVidia, nVidia GeForce GTX 260

December 9, 2011 by hgpu

DRAM Scheduling Policy for GPGPU Architectures Based on a Potential Function

Nagesh B. Lakshminarayana, Jaekyu Lee, Hyesoon Kim, Jinwoo Shin

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Tags: Computer science, Hardware Architecture, Memory

December 9, 2011 by hgpu

GPU implementations of scheduling heuristics for heterogeneous computing environments

Sergio Nesmachnow, Mauro Canabe

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Task scheduling, Tesla C1060

December 9, 2011 by hgpu

Objective-Driven Workload Allocation in Heterogeneous Computing Systems

Qiang Liu, Wayne Luk

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Tags: Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Performance, Tesla C2070

December 9, 2011 by hgpu

Enhanced Parallel ILU (p)-based Preconditioners for Multi-core CPUs and GPUs-The Power (g)-pattern Method

Vincent Heuveline, Dimitar Lukarski, Jan-Philipp Weiss

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Tags: Computer science, CUDA, Factorization, Numerical simulation, nVidia, Tesla S1070

December 9, 2011 by hgpu

Contributions to Parallel Simulation of Equation-Based Models on Graphics Processing Units

Kristian Stavaker

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce 8600, OpenCL, Tesla C1060, Tesla M2050, Thesis

December 9, 2011 by hgpu

Parallel Computation of 2D Morse-Smale Complexes

Nithin Shivashankar, Senthilnathan M, Vijay Natarajan

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 260, OpenCL, Visualization

December 9, 2011 by hgpu

Exploring the Optimization Space of Multi-Core Architectures with OpenCL Benchmarks

Deepak Mathews Panickal

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Tags: ATI, ATI Radeon HD 5470, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTX, OpenCL, Optimization, Tesla C2070, Thesis

December 9, 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

Achieving High Throughput Sequencing with Graphics Processing Units

Fast and Memory-Efficient Minimum Spanning Tree on the GPU

Playdoh: A lightweight Python library for distributed computing and optimisation

Load Balancing Utilizing Data Redundancy in Distributed Volume Rendering

A design tool for efficient mapping of multimedia applications onto heterogeneous platforms

DRAM Scheduling Policy for GPGPU Architectures Based on a Potential Function

GPU implementations of scheduling heuristics for heterogeneous computing environments

Objective-Driven Workload Allocation in Heterogeneous Computing Systems

Enhanced Parallel ILU (p)-based Preconditioners for Multi-core CPUs and GPUs-The Power (g)-pattern Method

Contributions to Parallel Simulation of Equation-Based Models on Graphics Processing Units

Parallel Computation of 2D Morse-Smale Complexes

Exploring the Optimization Space of Multi-Core Architectures with OpenCL Benchmarks

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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