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iNFAnt: NFA pattern matching on GPGPU devices

Niccolo' Cascarano, Pierluigi Rolando, Fulvio Risso, Riccardo Sisto

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Tags: Computer science, CUDA, Networks, nVidia, nVidia GeForce GTX 260, String matching

January 11, 2011 by hgpu

Accelerating linpack with CUDA on heterogenous clusters

Massimiliano Fatica

Tags: Computer science, CUDA, GPU cluster, Linear Algebra, nVidia

January 11, 2011 by hgpu

FCUDA: Enabling Efficient Compilation of CUDA Kernels onto FPGAs

Ros Papakonstantinou, Karthik Gururaj, John A. Stratton, Deming Chen, Jason Cong, Wen-mei W. Hwu

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Tags: Compilers, Computer science, CUDA, FPGA, High-level Languages

January 11, 2011 by hgpu

High-performance CUDA kernel execution on FPGAs

Alexandros Papakonstantinou, Karthik Gururaj, John A. Stratton, Deming Chen, Jason Cong, Wen Mei

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Tags: Computer science, CUDA, FPGA, Performance, Programming techniques

January 11, 2011 by hgpu

High-speed volume ray casting with CUDA

L. Marsalek, A. Hauber, P. Slusallek

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Tags: Computer science, CUDA, nVidia, Poster, Real-time graphics

January 10, 2011 by hgpu

Canny edge detection on NVIDIA CUDA

Yuancheng Luo, Ramani Duraiswami

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Tags: Computer vision, CUDA, Image processing, nVidia, nVidia GeForce 8800 GTX, Package

January 10, 2011 by hgpu

Comparing Hardware Accelerators in Scientific Applications: A Case Study

Rick Weber, Akila Gothandaraman, Robert J. Hinde, Gregory D. Peterson

Tags: ATI, Brook, Computer science, CUDA, FPGA, nVidia, OpenCL, Performance

January 10, 2011 by hgpu

Neville elimination on multi- and many-core systems: OpenMP, MPI and CUDA

P. Alonso, R. Cortina, F. Martinez-Zaldivar, J. Ranilla

Tags: Computer science, CUBLAS, CUDA, MPI, nVidia, OpenMP

January 10, 2011 by hgpu

Importance sampling algorithms for first passage time probabilities in the infinite server queue

Ad Ridder

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Tags: Algorithms, Computer science, Monte Carlo simulation

January 10, 2011 by hgpu

Dense optical flow by iterative local window registration

G. Le Besnerais, F. Champagnat

Tags: Computer science, CUDA, nVidia, Optical flow

January 10, 2011 by hgpu

Connected component labeling on a 2D grid using CUDA

Oleksandr Kalentev, Abha Rai, Stefan Kemnitz, Ralf Schneider

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Tags: Algorithms, Computer science, CUDA, nVidia

January 10, 2011 by hgpu

Parallel Prefix Sum (Scan) with CUDA

Mark Harris, Shubhabrata Sengupta, John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, Visualization

January 9, 2011 by hgpu

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

ThunderKittens: Tile primitives for speedy kernels

ParallelKittens: Systematic and Practical Simplification of Multi-GPU AI Kernels

NVIDIA Nemotron Parse 1.1

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

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

See all packages

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high performance computing on graphics processing units: hgpu.org

Applications

iNFAnt: NFA pattern matching on GPGPU devices

Accelerating linpack with CUDA on heterogenous clusters

FCUDA: Enabling Efficient Compilation of CUDA Kernels onto FPGAs

High-performance CUDA kernel execution on FPGAs

High-speed volume ray casting with CUDA

Canny edge detection on NVIDIA CUDA

Comparing Hardware Accelerators in Scientific Applications: A Case Study

Neville elimination on multi- and many-core systems: OpenMP, MPI and CUDA

Importance sampling algorithms for first passage time probabilities in the infinite server queue

Dense optical flow by iterative local window registration

Connected component labeling on a 2D grid using CUDA

Parallel Prefix Sum (Scan) with CUDA

Recent source codes

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

ThunderKittens: Tile primitives for speedy kernels

NVIDIA Nemotron Parse 1.1

Iris: AMD RAD's multi-GPU Triton-based framework for seamless multi-GPU programming

HipKittens: Fast and Furious AMD Kernels

Fortran xDSL dialects

mt4g: Memory Topology 4 GPUs

Falcon: GPU-Based Floating-point Adaptive Lossless Compression

CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization

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