high performance computing on graphics processing units: hgpu.org

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Raising the Bar for Using GPUs in Software Packet Processing

Anuj Kalia, Dong Zhou, Michael Kaminsky, David G. Andersen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 650 Ti, nVidia GeForce GTX 980, Performance, Tesla K20

March 22, 2015 by hgpu

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

Weifeng Liu, Brian Vinter

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Tags: Algorithms, AMD Radeon R9 290X, Benchmarking, Computer science, CUDA, Intel Xeon Phi, Mathematical Software, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Performance, Sparse matrix

March 20, 2015 by hgpu

maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs

Andrew Lavin

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

January 28, 2015 by hgpu

Performance Analysis of Join Algorithms on GPUs

Ran Rui, Hao Li, Yi-Cheng Tu

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Tags: Algorithms, Computer science, CUDA, Databases, Join, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, Performance

January 26, 2015 by hgpu

Dynamic Feature-Adaptive Subdivision

H. Schafer, J. Raab, B. Keinert, M. Meyer, M. Stamminger, M. Niessner

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Tags: Computational Geometry, Computer science, DirectX, Interactive application, nVidia, nVidia GeForce GTX 980, Rendering

January 10, 2015 by hgpu

Code Optimization on Kepler GPUs and Xeon Phi

Yong-Chull Jang, Hwancheol Jeong, Jangho Kim, Weonjong Lee, Jeonghwan Pak, Yuree Chung (SWME Collaboration)

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Tags: CUDA, High Energy Physics - Lattice, Intel Xeon Phi, Maxwell's equations, MPI, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, OpenMP, Performance, Physics, QCD, Tesla K40

November 12, 2014 by hgpu

cuDNN: Efficient Primitives for Deep Learning

Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer

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Tags: Computer science, CUDA, Machine learning, Mathematical Software, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 980, Tesla K40

October 8, 2014 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

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high performance computing on graphics processing units: hgpu.org

Raising the Bar for Using GPUs in Software Packet Processing

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

maxDNN: An Efficient Convolution Kernel for Deep Learning with Maxwell GPUs

Performance Analysis of Join Algorithms on GPUs

Dynamic Feature-Adaptive Subdivision

Code Optimization on Kepler GPUs and Xeon Phi

cuDNN: Efficient Primitives for Deep Learning

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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