high performance computing on graphics processing units: hgpu.org

Gallagher Pryor, Brett Lucey, Sandeep Maddipatla, Chris McClanahan, John Melonakos, Vishwanath Venugopalakrishnan, Krunal Patel, Pavan Yalamanchili, James Malcolm

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Tags: Computer science, CUDA, FFT, Image processing, Linear Algebra, nVidia, nVidia GeForce 9600, nVidia GeForce GTX 480, nVidia GeForce GTX 580, OpenGL, Package, PTX, Tesla C1060, Tesla C2070

September 26, 2011 by hgpu

From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming

Peng Du, Rick Weber, Piotr Luszczek, Stanimire Tomov, Gregory Peterson, Jack Dongarra

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Optimization, Performance, Tesla C2050

September 26, 2011 by hgpu

Identifying scalar behavior in CUDA kernels

Sylvain Collange

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Tags: Benchmarking, Code generation, Compilers, Computer science, CUDA, nVidia, PTX

September 26, 2011 by hgpu

Putting Automatic Polyhedral Compilation for GPGPU to Work

Soufiane Baghdadi, Armin Grosslinger, Albert Cohen

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Tags: Algorithms, Code generation, Compilers, Computer science, CUDA, nVidia, Package

September 26, 2011 by hgpu

Optimizing OpenCL Kernels for Iterative Statistical Applications on GPUs

Thilina Gunarathne, Bimalee Salpitikorala, Arun Chauhan, Geoffrey Fox

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Tags: Algorithms, Code generation, Computer science, nVidia, OpenCL, Optimization, Statistics, Tesla C1060

September 25, 2011 by hgpu

Harnessing the Power of GPUs without Losing Abstractions in SaC and ArrayOL: A Comparative Study

Jing Guo, Wendell Rodrigues, Jeyarajan Thiyagalingamz, Frederic Guyomarch, Pierre Boulet, Sven-Bodo Scholz

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Video encoding

September 25, 2011 by hgpu

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high performance computing on graphics processing units: hgpu.org

Applications

Fast Frequent Itemset Mining from Uncertain Databases using GPGPU

A framework to implement a multifrontal scheme on GPU architectures with OpenCL

PIPS Is not (just) Polyhedral Software

PGEM: Preemptive GPGPU Execution Model for Runtime Engines

LLVM to PTX Backend

A Uniform Platform to Support Multigenerational GPUs for High Performance Stream-based Computing

High-level GPU computing with jacket for MATLAB and C/C++

From CUDA to OpenCL: Towards a Performance-portable Solution for Multi-platform GPU Programming

Identifying scalar behavior in CUDA kernels

Putting Automatic Polyhedral Compilation for GPGPU to Work

Optimizing OpenCL Kernels for Iterative Statistical Applications on GPUs

Harnessing the Power of GPUs without Losing Abstractions in SaC and ArrayOL: A Comparative Study

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

Most viewed papers (last 30 days)