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Parallelism in Database Operations

Kalle Karkkainen

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Tags: Computer science, CUDA, Databases, nVidia, Review

December 2, 2012 by hgpu

Efficient Hybrid Execution of C++ Applications using Intel(R) Xeon Phi(TM) Coprocessor

Jiri Dokulil, Enes Bajrovic, Siegfried Benkner, Sabri Pllana, Martin Sandrieser, Beverly Bachmayer

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Tags: Computer science, Intel, Intel Phi, Software Engineering

December 2, 2012 by hgpu

FIELA: A Fast Image Encryption with Lorenz Attractor using Hybrid Computing

P Kranthi Kumar, B V Nagendra Prasad, Gelli MBSS Kumar, V. Chandrasekaran, P.K.Baruah

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Tags: Computer science, CUDA, Hybrid computing, nVidia, Security, Tesla M2070

December 2, 2012 by hgpu

CPUless PCs inside networked control systems

Peter Fodrek, Tomas Murgas, Michal Blaho

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Tags: Algorithms, Computer science, nVidia, OpenCL, Operating systems

December 1, 2012 by hgpu

Multidimensional Dataflow Graph Modeling and Mapping for Efficient GPU Implementation

Lai-Huei Wang, Chung-Ching Shen, Gunasekaran Seetharaman, Kannappan Palaniappan, Shuvra S. Bhattacharyya

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Tags: Computer science, CUDA, Design space exploration, DSP, nVidia, nVidia GeForce GTX 260

December 1, 2012 by hgpu

Parallel Nonbinary LDPC Decoding on GPU

Guohui Wang, Hao Shen, Bei Yin, Michael Wu, Yang Sun, Joseph R. Cavallaro

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 470, Parity-check codes

November 29, 2012 by hgpu

clOpenCL – Supporting Distributed Heterogeneous Computing in HPC Clusters

Albano Alves, Jose Rufino, Antonio Pina, Luis Paulo Santos

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Tags: Cluster computing, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX 460, OpenCL

November 29, 2012 by hgpu

GPU Ray Tracing – Comparative Study of Ray-Triangle Intersection Algorithms

Vladimir Shumskiy, Alexandre Parshin

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Tags: ATI, ATI Radeon HD 6870, ATI Radeon HD 7850, Computer science, DirectX, nVidia, nVidia GeForce GT 240 M, nVidia GeForce GTX 560, Raytracing, Rendering

November 29, 2012 by hgpu

Formal Semantics of Heterogeneous CUDA-C: A Modular Approach with Applications

Chris Hathhorn, Michela Becchi, William L. Harrison, Adam Procter

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, Package, Programming Languages

November 28, 2012 by hgpu

Softshell: Dynamic Scheduling on GPUs

Markus Steinberger, Bernhard Kainz, Bernhard Kerbl, Stefan Hauswiesner, Michael Kenzel, Dieter Schmalstieg

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Quadro FX 6000, Package, Task scheduling

November 27, 2012 by hgpu

From Parallel Programs to Customized Parallel Processors

Pekka Jaaskelainen

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Tags: Algorithms, Compilers, Computer science, FPGA, High-level Languages, nVidia, nVidia GeForce 9400, OpenCL, Thesis

November 27, 2012 by hgpu

Optimizing 3D Convolutions for Wavelet Transforms on CPUs with SSE Units and GPUs

Brice Videau, Vania Marangozova-Martin, Luigi Genovese, Thierry Deutsch

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Tags: Algorithms, ATI, ATI Radeon HD 6970, Computer science, Heterogeneous systems, nVidia, OpenCL, Optimization, Tesla C2070, Wavelet

November 27, 2012 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

Parallelism in Database Operations

Efficient Hybrid Execution of C++ Applications using Intel(R) Xeon Phi(TM) Coprocessor

FIELA: A Fast Image Encryption with Lorenz Attractor using Hybrid Computing

CPUless PCs inside networked control systems

Multidimensional Dataflow Graph Modeling and Mapping for Efficient GPU Implementation

Parallel Nonbinary LDPC Decoding on GPU

clOpenCL – Supporting Distributed Heterogeneous Computing in HPC Clusters

GPU Ray Tracing – Comparative Study of Ray-Triangle Intersection Algorithms

Formal Semantics of Heterogeneous CUDA-C: A Modular Approach with Applications

Softshell: Dynamic Scheduling on GPUs

From Parallel Programs to Customized Parallel Processors

Optimizing 3D Convolutions for Wavelet Transforms on CPUs with SSE Units and 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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