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OpenCL Parallel Programming Development Cookbook

Raymond Tay

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Tags: Book, Computer science, Heterogeneous systems, OpenCL, Tutorial

September 27, 2013 by hgpu

Java with Auto-Parallelization on Graphics Coprocessing Architecture

Guodong Han, Chenggang Zhang, King Tin Lam, Cho-Li Wang

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Java, nVidia, Performance, Tesla M2050

September 27, 2013 by hgpu

Trellis: Portability Across Architectures with a High-level Framework

Lukasz G. Szafaryn, Todd Gamblin, Bronis R. de Supinski, Kevin Skadron

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, Tesla C2050

September 27, 2013 by hgpu

OpenCL Task Partitioning in the Presence of GPU Contention

Dominik Grewe, Zheng Wang, and Michael F.P. O'Boyle

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Tags: Computer science, Heterogeneous systems, Intel, Intel HD Graphics 4000, Machine learning, OpenCL, Task scheduling

September 25, 2013 by hgpu

Automatic Software Synthesis from High-Level ForSyDe Models Targeting Massively Parallel Processors

George Ungureanu

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Tags: Algorithms, Computer science, CUDA, Data parallelism, Heterogeneous systems, Image processing, nVidia, Thesis

September 25, 2013 by hgpu

Improving Resource Utilization in Heterogeneous CPU-GPU Systems

Michael Boyer

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Tags: Algorithms, ATI, ATI Radeon HD 5870, ATI Radeon HD 7970, Biology, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 280, OpenCL, Package, Software Engineering, Thesis

September 23, 2013 by hgpu

Algorithmic GPGPU Memory Optimization

Byunghyun Jang, Minsu Choi, Kyung Ki Kim

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Tags: Algorithms, Computer science, Heterogeneous systems, Memory model, nVidia, nVidia GeForce GTX 285, OpenCL, Performance

September 15, 2013 by hgpu

Porting to the Intel Xeon Phi: Opportunities and Challenges

C. Rosales

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Intel Phi, MPI, nVidia, Optimization, Performance

September 15, 2013 by hgpu

Evaluating the Viability of Application-Driven Cooperative CPU/GPU Fault Detection

Dong Li, Seyong Lee, Jeffrey S. Vetter

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Tags: Computer science, CUDA, Fault tolerance, Heterogeneous systems, nVidia

September 11, 2013 by hgpu

A Bi-objective Optimization Framework for Query Plans

P. Przymus, K. Kaczmarski, K. Stencel

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Tags: Heterogeneous systems, Performance, Programming techniques

September 7, 2013 by hgpu

Phase Aware Memory Scheduling

C. Sangani, M. Venkatesan, R. Ramesh

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Tags: Heterogeneous systems, Memory, Memory model

September 6, 2013 by hgpu

Work in Progress: Vortex Detection and Visualization for Design of Micro Air Vehicles and Turbomachinery

R.J. Vickery, H. Thornburg, T. Wischgoll, C. Koehler, H. Dong, M. Pickett, N. Eikenberry, L. Harris, R. Snyder, D. Sanders, R. Hand

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Tags: CUDA, Fluid dynamics, Heterogeneous systems, nVidia

September 5, 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

OpenCL Parallel Programming Development Cookbook

Java with Auto-Parallelization on Graphics Coprocessing Architecture

Trellis: Portability Across Architectures with a High-level Framework

OpenCL Task Partitioning in the Presence of GPU Contention

Automatic Software Synthesis from High-Level ForSyDe Models Targeting Massively Parallel Processors

Improving Resource Utilization in Heterogeneous CPU-GPU Systems

Algorithmic GPGPU Memory Optimization

Porting to the Intel Xeon Phi: Opportunities and Challenges

Evaluating the Viability of Application-Driven Cooperative CPU/GPU Fault Detection

A Bi-objective Optimization Framework for Query Plans

Phase Aware Memory Scheduling

Work in Progress: Vortex Detection and Visualization for Design of Micro Air Vehicles and Turbomachinery

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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