high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Computer science

Intel’s Array Building Blocks: A retargetable, dynamic compiler and embedded language

Chris J. Newburn, Byoungro So, Zhenying Liu, Michael McCool, Anwar Ghuloum, Stefanus Du Toit, Zhi Gang Wang, Zhao Hui Du, Yongjian Chen, Gansha Wu, Peng Guo, Zhanglin Liu and Dan Zhang

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Tags: Benchmarking, Code generation, Computer science, Heterogeneous systems, Optimization

October 7, 2011 by hgpu

A Framework for Automatic OpenMP Code Generation

A. Raghesh

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Tags: Code generation, Computer science, OpenCL, Optimization, Programming techniques, Thesis

October 7, 2011 by hgpu

Run-time Reconfigurable Multiprocessors

Madhura Purnaprajna

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Tags: ATI, Computer science, FPGA, Hardware Architecture, nVidia, Performance, Thesis

October 7, 2011 by hgpu

Divergence Analysis and Optimizations

Bruno Coutinho, Diogo Sampaio, Fernando Magno Quintao Pereira, Wagner Meira Jr.

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Tags: Algorithms, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, Optimization, Package, PTX

October 6, 2011 by hgpu

CUDA performance analyzer

Aniruddha Dasgupta

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Tags: Analytical model, Computer science, CUDA, Fast multipole method, nVidia, Optimization, Performance, Tesla C1060, Thesis

October 6, 2011 by hgpu

Offloading Java to Graphics Processors

Peter Calvert

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

October 6, 2011 by hgpu

Parallelisation of Java for Graphics Processors

Peter Calvert

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Tags: Algorithms, Benchmarking, Code generation, Computer science, CUDA, Java, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTX 260, Package, Thesis

October 6, 2011 by hgpu

Multi-core programming with OpenCL: performance and portability: OpenCL in a memory bound scenario

Olav Aanes Fagerlund

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Tags: Computer science, CUDA, FEM, Finite element method, nVidia, nVidia GeForce GTX 280, OpenCL, Thesis

October 6, 2011 by hgpu

Static GPU threads and an improved scan algorithm

Jens Breitbart

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 280, OpenCL, Performance, Presentation, Programming techniques

October 6, 2011 by hgpu

Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms

Andrew Nere, Atif Hashmi, Mikko Lipasti

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Image recognition, Neural networks, nVidia, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, Optimization, Tesla C2050

October 5, 2011 by hgpu

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Mark Silberstein, Assaf Schuster, John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Optimization, Package, Performance, Probability, Task scheduling, Tesla C2050

October 5, 2011 by hgpu

Parallel Direct Simulation Monte Carlo Computation Using CUDA on GPUs

C.-C. Su, C.-W. Hsieh, M. R. Smith, M. C. Jermy, and J.-S. Wua

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Tags: Benchmarking, Computer science, CUDA, Monte Carlo simulation, nVidia, Tesla C1060

October 5, 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

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high performance computing on graphics processing units: hgpu.org

Applications

Intel’s Array Building Blocks: A retargetable, dynamic compiler and embedded language

A Framework for Automatic OpenMP Code Generation

Run-time Reconfigurable Multiprocessors

Divergence Analysis and Optimizations

CUDA performance analyzer

Offloading Java to Graphics Processors

Parallelisation of Java for Graphics Processors

Multi-core programming with OpenCL: performance and portability: OpenCL in a memory bound scenario

Static GPU threads and an improved scan algorithm

Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Parallel Direct Simulation Monte Carlo Computation Using CUDA on GPUs

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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