high performance computing on graphics processing units: hgpu.org

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Parallel programming with NVIDIA CUDA

Alejandro Segovia

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8600 GT, Package, Tutorial

September 11, 2011 by hgpu

TimeGraph: GPU scheduling for real-time multi-tasking environments

Shinpei Kato, Karthik Lakshmanan, Ragunathan Rajkumar, Yutaka Ishikawa

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Tags: Benchmarking, Computer science, nVidia, nVidia GeForce 9800 GT, nVidia GeForce GTX 285, nVidia GeForce GTX 480, OpenGL, Operating systems, Package, Real-time graphics, Task scheduling

September 11, 2011 by hgpu

Challenges of medical image processing

Ingrid Scholl, Til Aach, Thomas M. Deserno, Torsten Kuhlen

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Tags: Algorithms, Data mining, Grid, Image processing, Medicine, Review, Virtual reality, Visualization

September 11, 2011 by hgpu

A data parallel view on polyhedral process networks

Ana Balevic, Bart Kienhuis

Tags: Computer science, Data parallelism, Heterogeneous systems

September 11, 2011 by hgpu

High-performance SIMT code generation in an active visual effects library

Jay L.T. Cornwall, Lee Howes, Paul H.J. Kelly, Phil Parsonage, Bruno Nicoletti

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Tags: Algorithms, Code generation, CUDA, Image processing, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 260, Performance, Software Engineering, Visualization

September 11, 2011 by hgpu

Software-based branch predication for AMD GPUs

Ryan Taylor, Xiaoming Li

Tags: ATI, Benchmarking, Computer science, Performance, Programming techniques

September 11, 2011 by hgpu

Solving diffractive optics problems using graphics processing units

D. L. Golovashkin, N. L. Kasanskiy

Tags: Algorithms, ATI, ATI FireStream, Brook, Cg, CUDA, FDTD, Finite-difference time-domain, nVidia, OpenGL, Optics, Overview, Physics, Raytracing, Review

September 11, 2011 by hgpu

Enabling multiple accelerator acceleration for Java/OpenMP

Ronald Veldema, Thorsten Blass, Michael Philippsen

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Tags: Benchmarking, Computer science, CUDA, Java, nVidia, OpenCL, OpenMP, OpenMPI, Tesla M1060

September 9, 2011 by hgpu

Heterogeneous multicore parallel programming for graphics processing units

Francois Bodin, Stephane Bihan

Tags: Compilers, Computer science, Heterogeneous systems, nVidia

September 9, 2011 by hgpu

Beyond programmable shading (parts I and II)

Aaron Lefohn, Mike Houston, Johan Andersson, Ulf Assarsson, Cass Everitt, Kayvon Fatahalian, Tim Foley, Justin Hensley, Paul Lalonde, David Luebke

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Tags: 3D Graphics and Realism, Algorithms, Computer science, DirectX, Image processing, OpenGL, Presentation, Programming techniques, Rasterization, Raytracing, Rendering

September 9, 2011 by hgpu

Data classification for artificial intelligence construct training to aid in network incident identification using network telescope data

Bradley Cowie, Barry Irwin

Tags: Artificial intelligence, Astrophysics, Computer science, Security

September 9, 2011 by hgpu

A stream-computing extension to OpenMP

Antoniu Pop, Albert Cohen

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Tags: Compilers, Computer science, OpenMP, Optimization

September 9, 2011 by hgpu

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

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

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high performance computing on graphics processing units: hgpu.org

Applications

Parallel programming with NVIDIA CUDA

TimeGraph: GPU scheduling for real-time multi-tasking environments

Challenges of medical image processing

A data parallel view on polyhedral process networks

High-performance SIMT code generation in an active visual effects library

Software-based branch predication for AMD GPUs

Solving diffractive optics problems using graphics processing units

Enabling multiple accelerator acceleration for Java/OpenMP

Heterogeneous multicore parallel programming for graphics processing units

Beyond programmable shading (parts I and II)

Data classification for artificial intelligence construct training to aid in network incident identification using network telescope data

A stream-computing extension to OpenMP

Recent source codes

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

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

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