high performance computing on graphics processing units: hgpu.org

Pier Stanislao Paolucci, Roberto Ammendola, Andrea Biagioni, Ottorino Frezza, Francesca Lo Cicero, Alessandro Lonardo, Michele Martinelli, Elena Pastorelli, Francesco Simula, Piero Vicini

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Tags: ARM, Computer science, MPI, Neural networks, Neurons and Cognition, nVidia, STDP

May 15, 2015 by hgpu

Improving CUDA DNA Analysis Software with Genetic Programming

William B. Langdon, Brian Yee Hong Lam, Justyna Petke, Mark Harman

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Tags: Algorithms, Artificial intelligence, Bioinformatics, Computer science, CUDA, Genetic programming, nVidia, Package, Tesla K20, Tesla K40

May 12, 2015 by hgpu

CVPI: A Computer Vision Library For Mobile and Embedded Platforms

Devin Homan

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Tags: Computer science, Computer vision, Image processing, OpenGL, Package, Rendering

May 12, 2015 by hgpu

Development of Parallel Architectures for Radar/Video Signal Processing Applications

Amin Jarrah

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Tags: Algorithms, CUDA, Discrete Wavelet Transform, FPGA, Heterogeneous systems, Image processing, nVidia, nVidia GeForce GTX 260, Signal processing, Thesis

May 12, 2015 by hgpu

Density Estimations for Approximate Query Processing on SIMD Architectures

Witold Andrzejewski, Artur Gramacki, Jaroslaw Gramacki

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package

May 12, 2015 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

Efficient Resource Scheduling for Big Data Processing on Accelerator-based Heterogeneous Systems

Using Butterfly-Patterned Partial Sums to Optimize GPU Memory Accesses for Drawing from Discrete Distributions

Performance Analysis and Efficient Execution on Systems with multi-core CPUs, GPUs and MICs

Speeding up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves

MRCUDA: MapReduce Acceleration Framework Based on GPU

The 3D Flow Field Around an Embedded Planet

Adaptive discrete cosine transform-based image compression method on a heterogeneous system platform using Open Computing Language

Power, Energy and Speed of Embedded and Server Multi-Cores applied to Distributed Simulation of Spiking Neural Networks: ARM in NVIDIA Tegra vs Intel Xeon quad-cores

Improving CUDA DNA Analysis Software with Genetic Programming

CVPI: A Computer Vision Library For Mobile and Embedded Platforms

Development of Parallel Architectures for Radar/Video Signal Processing Applications

Density Estimations for Approximate Query Processing on SIMD Architectures

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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