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Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods

Diego Fabregat-Traver, Davor Davidovic, Markus Hohnerbach, Edoardo Di Napoli

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Tags: Computer science, CUDA, nVidia, Performance, Tesla K20, Tesla K40, Tesla K80

November 3, 2016 by hgpu

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

Xiang Li, Tao Qin, Jian Yang, Tie-Yan Liu

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Tags: Algorithms, Computer science, Deep learning, Neural networks, NLP, nVidia, RNN, Tesla K20

November 1, 2016 by hgpu

Performance Optimization of 3-D Lattice Boltzmann Flow Solver on a GPU

Nhat-Phuong Tran, Myungho Lee, Sugwon Hong

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Tags: Computer science, CUDA, Fluid dynamics, Lattice Boltzmann model, Numerical simulation, nVidia, Performance, Tesla K20

November 1, 2016 by hgpu

GOTHIC: Gravitational oct-tree code accelerated by hierarchical time step controlling

Yohei Miki, Masayuki Umemura

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Tags: Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, nVidia GeForce GTX Titan X, Tesla K20, Tesla M2090

October 29, 2016 by hgpu

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

Pilar Cossio, David Rohr, Fabio Baruffa, Markus Rampp, Volker Lindenstruth, Gerhard Hummer

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Tags: Bayesian, Biology, Biomolecules, CUDA, Electron microscopy, Heterogeneous systems, Medicine, Microscopy, MPI, nVidia, nVidia GeForce GTX Titan, OpenMP, Package, Tesla K20, Tomography

October 8, 2016 by hgpu

Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

Jiaquan Gao, Panpan Qi, Guixia He

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla C2050, Tesla K20

October 4, 2016 by hgpu

Bridging the Semantic Gaps of GPU Acceleration for Scaleout CNN-based Big Data Processing: Think Big, See Small

Mingcong Song, Yang Hu, Yunlong Xu, Chao Li, Huixiang Chen, Jingling Yuan, Tao Li

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Tags: Analytical model, big data, Computer science, CUDA, Deep learning, Distributed computing, Neural networks, nVidia, Tesla K20

September 22, 2016 by hgpu

MCS 572: Introduction to Supercomputing

Jan Verschelde

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Tags: Algorithms, Computer science, CUDA, MPI, Numerical Analysis, nVidia, nVidia GeForce 9400 M, OpenMP, Package, Tesla C2050, Tesla C2070, Tesla K20, Tutorial

September 22, 2016 by hgpu

System Design Principles for Heterogeneous Resource Management and Scheduling in Accelerator-Based Systems

Dipanjan Sengupta

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Tags: Algorithms, Cloud, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia Quadro 2000, nVidia Quadro 4000, Task scheduling, Tesla C2050, Tesla C2070, Tesla K20, Thesis

September 10, 2016 by hgpu

A Lightweight Approach to Performance Portability with targetDP

Alan Gray, Kevin Stratford

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Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, performance portability, QCD, Tesla K20, Tesla K40

September 8, 2016 by hgpu

Understanding the Impact of Hybrid Programming on Software Energy Efficiency

Donna LaKomski

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Tags: Computer science, CUDA, Heterogeneous systems, Hybrid computing, Intel Xeon Phi, nVidia, OpenCL, Tesla K20, Thesis

September 3, 2016 by hgpu

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Yue Hu

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Tags: Code generation, Computer science, CUDA, DSL, Fluid dynamics, Intel Xeon Phi, nVidia, OpenCL, Package, PDEs, PTX, Stencil computation, Tesla K20, Thesis

August 31, 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

Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods

LightRNN: Memory and Computation-Efficient Recurrent Neural Networks

Performance Optimization of 3-D Lattice Boltzmann Flow Solver on a GPU

GOTHIC: Gravitational oct-tree code accelerated by hierarchical time step controlling

BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

Efficient CSR-Based Sparse Matrix-Vector Multiplication on GPU

Bridging the Semantic Gaps of GPU Acceleration for Scaleout CNN-based Big Data Processing: Think Big, See Small

MCS 572: Introduction to Supercomputing

A Lightweight Approach to Performance Portability with targetDP

Understanding the Impact of Hybrid Programming on Software Energy Efficiency

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

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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