high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

Work Efficient Parallel Algorithms for Large Graph Exploration

Dip Sankar Banerjee, Shashank Sharma, Kishore Kothapalli

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Tags: Algorithms, Computer science, CUDA, Graph theory, Heterogeneous systems, nVidia, nVidia GeForce GTX 580

October 21, 2013 by hgpu

Architecture-and Workload-Aware Heterogeneous Algorithms for Sparse Matrix Vector Multiplication

Sivaramakrishna Bharadwaj Indarapu, Manoj Maramreddy, Kishore Kothapalli

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX 480, Sparse matrix

October 21, 2013 by hgpu

Multi-Scale Scheduling Techniques for Signal Processing Systems

Zheng Zhou

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Tags: Algorithms, CUDA, DSP, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, Particle swarm optimization, Signal processing, Thesis

October 19, 2013 by hgpu

Efficient fine grained shared buffer management for multiple OpenCL devices

Chang-qing Xun, Dong Chen, Qiang Lan, Chun-yuan Zhang

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Tags: Computer science, Heterogeneous systems, Memory model, nVidia, OpenCL, Tesla C2050

October 19, 2013 by hgpu

OpenACC-based Snow Simulation

Magnus Alvestad Mikalsen

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenACC, OpenCL, Performance, Tesla C2070, Tesla K20, Thesis

October 18, 2013 by hgpu

Heterogeneous Clustering with Homogeneous Code: Accelerate MPI Applications Without Code Surgery Using Intel Xeon Phi Coprocessors

Andrey Vladimirov and Vadim Karpusenko

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Tags: Heterogeneous systems, Intel Phi, Intel Xeon Phi, Monte Carlo simulation, MPI, Portability, Task scheduling

October 18, 2013 by Andrey Vladimirov

Towards Code Generation from Design Models for Embedded Systems on Heterogeneous CPU-GPU Platforms

Federico Ciccozzi

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, nVidia

October 18, 2013 by hgpu

Heterogeneous FTDT for Seismic Processing

Andreas Berg Skomedal

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Source codes

Tags: CUDA, Earth and Space Sciences, FDTD, Finite-difference time-domain, Geoscience, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Package, Seismology, Tesla K20, Thesis

October 18, 2013 by hgpu

Understanding and Modeling the Synchronization Cost in the GPU Architecture

James T. Letendre

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, Performance, Tesla C2075, Thesis

October 17, 2013 by hgpu

A Dynamic Resource Management System for Network-Attached Accelerator Clusters

Suraj Prabhakaran, Mohsin Iqbal, Sebastian Rinke, Felix Wolf

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, nVidia

October 17, 2013 by hgpu

Domain-Specific Languages for Heterogeneous Parallel Computing

HyoukJoong Lee, Kevin J. Brown, Arvind K. Sujeeth, Hassan Chafi, Tiark Rompf, Martin Odersky, Kunle Olukotun

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Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Tesla C2050

October 15, 2013 by hgpu

Characterizing the Challenges and Evaluating the Efficacy of a CUDA-to-OpenCL Translator

Mark Gardner, Paul Sathre, Wu-chun Feng, Gabriel Martinez

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, OpenCL

October 13, 2013 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

Work Efficient Parallel Algorithms for Large Graph Exploration

Architecture-and Workload-Aware Heterogeneous Algorithms for Sparse Matrix Vector Multiplication

Multi-Scale Scheduling Techniques for Signal Processing Systems

Efficient fine grained shared buffer management for multiple OpenCL devices

OpenACC-based Snow Simulation

Heterogeneous Clustering with Homogeneous Code: Accelerate MPI Applications Without Code Surgery Using Intel Xeon Phi Coprocessors

Towards Code Generation from Design Models for Embedded Systems on Heterogeneous CPU-GPU Platforms

Heterogeneous FTDT for Seismic Processing

Understanding and Modeling the Synchronization Cost in the GPU Architecture

A Dynamic Resource Management System for Network-Attached Accelerator Clusters

Domain-Specific Languages for Heterogeneous Parallel Computing

Characterizing the Challenges and Evaluating the Efficacy of a CUDA-to-OpenCL Translator

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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