high performance computing on graphics processing units: hgpu.org

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A PTX Code Generator for LLVM

Helge Rhodin

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Tags: Code generation, Compilers, Computer science, CUDA, nVidia, Optimization, PTX, Thesis, Virtualization

September 30, 2011 by hgpu

CBench: Analyzing Compute Performance for Modern NVIDIA and AMD GPUs

Varun Sampath

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Tags: Algorithms, ATI, ATI Radeon HD 6870, Benchmarking, Computer science, CUDA, nVidia, OpenCL, Package, Tesla C2070

September 30, 2011 by hgpu

FATSEA-An Architectural Simulator for General Purpose Computing on GPUs

K.E. Ostby, J.L. Aragon, J.M. Garcia, M. Ujaldon

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Tags: Compilers, Computer science, CUDA, nVidia, Performance, PTX, Tesla C870

September 30, 2011 by hgpu

Translating GPU binaries to tiered SIMD architectures with Ocelot

Gregory Diamos, Andrew Kerr, Mukil Kesavan

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Tags: Algorithms, Cell processor, Compilers, Computer science, nVidia, PTX

September 30, 2011 by hgpu

Accelerating Foreign-Key Joins using Asymmetric Memory Channels

Holger Pirk, Stefan Manegold, Martin Kersten

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Tags: ATI, ATI Radeon HD 5850, Computer science, Databases, nVidia, nVidia GeForce GTX 480, OpenCL

September 30, 2011 by hgpu

Accelerating data mining workloads: current approaches and future challenges in system architecture design

Alok N. Choudhary, Daniel Honbo, Prabhat Kumar, Berkin Ozisikyilmaz, Sanchit Misra, Gokhan Memik

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Tags: Computer science, CUDA, Data mining, FPGA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GT

September 30, 2011 by hgpu

Adding special-purpose processor support to the Erlang VM

Christofer Ferm

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Tags: Computer science, DSP, nVidia, nVidia GeForce 9400 M, OpenCL, Performance, Thesis, Virtualization

September 30, 2011 by hgpu

Many-threaded implementation of differential evolution for the CUDA platform

Pavel Kromer, Vaclav Snasel, Jan Platos, Ajith Abraham

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Differential evolution, Genetic programming, nVidia, Optimization, Tesla C2050

September 29, 2011 by hgpu

Active thread compaction for GPU path tracing

Ingo Wald

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Presentation, Raytracing

September 29, 2011 by hgpu

Evolving CUDA PTX programs by quantum inspired linear genetic programming

Leandro F. Cupertino, Cleomar P. Silva, Douglas M. Dias, Marco Aurelio C. Pacheco, Cristiana Bentes

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Tags: Computer science, CUDA, Databases, Genetic programming, nVidia, Performance, PTX, Tesla C1060

September 29, 2011 by hgpu

Highly Scalable Multi Objective Test Suite Minimisation Using Graphics Cards

Shin Yoo, Mark Harman, Shmuel Ur

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Tags: Algorithms, ATI, ATI Radeon HD 4850, Benchmarking, Computer science, OpenCL, Optimization, Software Engineering

September 28, 2011 by hgpu

Design space exploration towards a realtime and energy-aware GPGPU-based analysis of biosensor data

Constantin Timm, Frank Weichert, Peter Marwedel, Heinrich Muller

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Tags: Computer science, Design space exploration, Energy-efficient computing, Image processing, nVidia, nVidia GeForce 9600 GT, nVidia GeForce GTS 250, nVidia ION, OpenCL, Sensing

September 28, 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

A PTX Code Generator for LLVM

CBench: Analyzing Compute Performance for Modern NVIDIA and AMD GPUs

FATSEA-An Architectural Simulator for General Purpose Computing on GPUs

Translating GPU binaries to tiered SIMD architectures with Ocelot

Accelerating Foreign-Key Joins using Asymmetric Memory Channels

Accelerating data mining workloads: current approaches and future challenges in system architecture design

Adding special-purpose processor support to the Erlang VM

Many-threaded implementation of differential evolution for the CUDA platform

Active thread compaction for GPU path tracing

Evolving CUDA PTX programs by quantum inspired linear genetic programming

Highly Scalable Multi Objective Test Suite Minimisation Using Graphics Cards

Design space exploration towards a realtime and energy-aware GPGPU-based analysis of biosensor data

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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