high performance computing on graphics processing units: hgpu.org

Georgios Smaragdos, Georgios Chatzikonstantis, Rahul Kukreja, Harrys Sidiropoulos, Dimitrios Rodopoulos, Ioannis Sourdis, Zaid Al-Ars, Christoforos Kachris, Dimitrios Soudris, Chris I. De Zeeuw, Christos Strydis

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Tags: Biophysics, Computer science, Heterogeneous systems, Intel Xeon Phi, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX Titan X, OpenMP

December 10, 2016 by hgpu

Learning Structured Sparsity in Deep Neural Networks

Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia Quadro K600, Tesla K40

August 16, 2016 by hgpu

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

Patrick Doetsch, Albert Zeyer, Paul Voigtlaender, Ilya Kulikov, Ralf Schluter, Hermann Ney

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Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Package, RNN, Speech recognition

August 4, 2016 by hgpu

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi

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Tags: Computer science, CUDA, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan Z

May 23, 2016 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

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high performance computing on graphics processing units: hgpu.org

GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation

EvoTorch: Scalable Evolutionary Computation in Python

Understanding the Power of Evolutionary Computation for GPU Code Optimization

Performance Analysis and Improvement of Parallel Differential Evolution

An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms

GEVO: GPU Code Optimization using Evolutionary Computation

BrainSlug: Transparent Acceleration of Deep Learning Through Depth-First Parallelism

A hybrid CPU-GPU parallelization scheme of variable neighborhood search for inventory optimization problems

BrainFrame: A heterogeneous accelerator platform for neuron simulations

Learning Structured Sparsity in Deep Neural Networks

RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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