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hgpu.org » Applications » Computer science

Evaluation of Standardized Password-based Key Derivation against Parallel Processing Platforms

Markus Durmuth, Tim Guneysu, Markus Kasper, Christof Paar, Tolga Yalcin, Ralf Zimmermann

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Tags: Computer science, CUDA, FPGA, GPU cluster, nVidia, Review, Security, Tesla C2070

February 14, 2013 by hgpu

Real-Time 3D Face Identification from a Depth Camera

Rui Min, Jongmoo Choi, Gerard Medioni, Jean-Luc Dugelay

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Tags: Computer science, Computer vision, CUDA, nVidia, Tesla C1060

February 14, 2013 by hgpu

A Networked Dataflow Simulation Environment for Signal Processing and Data Mining Applications

Stephen Won

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Tags: Computer vision, CUDA, Data mining, Heterogeneous systems, nVidia, nVidia GeForce GTX 260, SIFT, Signal processing, Thesis

February 12, 2013 by hgpu

Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks

Alessandro Giusti, Dan C. Ciresan, Jonathan Masci, Luca M. Gambardella, Jurgen Schmidhuber

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Tags: Artificial intelligence, Computational Complexity, Computer science, Computer vision, CUDA, Neural networks, nVidia, nVidia GeForce GTX 580

February 12, 2013 by hgpu

Implementing an architecture for efficient network traffic processing on modern graphics hardware

Lazaros Koromilas

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Tags: Computer science, CUDA, Network communications, nVidia, nVidia GeForce GTX 480

February 12, 2013 by hgpu

Accelerated Wide Baseline Matching using OpenCL

Jian Cao, Jie Liang, Xiao-fang Xie, Xun-qiang Hu

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Tags: Computer science, Computer vision, Image registration, nVidia, nVidia GeForce 8800 GT, OpenCL

February 12, 2013 by hgpu

Adaptation of the MapReduce programming framework to compute-intensive data-analytics kernels

Reza Farivar

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Tags: Computer science, CUDA, Heterogeneous systems, MapReduce, nVidia, nVidia GeForce GTX 275, Thesis

February 9, 2013 by hgpu

A multi-lane traffic simulation model via continuous cellular automata

Emanuele Rodaro, Oznur Yeldan

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Source codes

Tags: Cellular automata, Computer science, CUDA, nVidia, nVidia GeForce GT 555 M, Package, Python

February 9, 2013 by hgpu

Enabling Inter-Machine Parallelism in High-Level Languages with SEJITS and MapReduce

Michael Driscoll, Evangelos Georganas, Penporn Koanantakool

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Tags: Computer science, CUDA, MapReduce, nVidia, Programming techniques, Python, Tesla M2050

February 9, 2013 by hgpu

Fast 3D Wavelet Transform on Multicore and Manycore Computing Platforms

V. Galiano, O. Lopez-Granado, M.P. Malumbres, H. Migallon

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Tags: Algorithms, Computer science, CUDA, Image processing, nVidia, nVidia GeForce GTX 280, Optimization, Wavelet

February 9, 2013 by hgpu

Document Stream Clustering using GPUs

Michael J. Szaszy, Hanan Samet

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Tags: Algorithms, Clustering, Computer science, CUDA, Information Retrieval, nVidia, nVidia GeForce GTX 480, Performance

February 9, 2013 by hgpu

Effectiveness of program transformations and compilers for directive-based GPU programming models

Swapnil Avinash Ghike

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, Optimization, Performance, Tesla X2090, Thesis

February 9, 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

Applications

Evaluation of Standardized Password-based Key Derivation against Parallel Processing Platforms

Real-Time 3D Face Identification from a Depth Camera

A Networked Dataflow Simulation Environment for Signal Processing and Data Mining Applications

Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks

Implementing an architecture for efficient network traffic processing on modern graphics hardware

Accelerated Wide Baseline Matching using OpenCL

Adaptation of the MapReduce programming framework to compute-intensive data-analytics kernels

A multi-lane traffic simulation model via continuous cellular automata

Enabling Inter-Machine Parallelism in High-Level Languages with SEJITS and MapReduce

Fast 3D Wavelet Transform on Multicore and Manycore Computing Platforms

Document Stream Clustering using GPUs

Effectiveness of program transformations and compilers for directive-based GPU programming models

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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