high performance computing on graphics processing units: hgpu.org

Jin-Hua Tao, Zi-Dong Du, Qi Guo, Hui-Ying Lan, Lei Zhang, Sheng-Yuan Zhou, Cong Liu, Hai-Feng Liu, Shan Tang, Allen Rush, Willian Chen, Shao-Li Liu, Yun-Ji Chen, Tian-Shi Chen

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Tags: ASIC, Benchmarking, Computer science, Deep learning, nVidia, nVidia GeForce GTX 950, nVidia Tegra TX1, Tesla K40

October 24, 2017 by hgpu

Parallel Matching and Clustering Algorithms on GPUs

Md. Naim

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, Tesla K40, Thesis

October 21, 2017 by hgpu

Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors

Satya P. Jammy, Christian T. Jacobs, David J. Lusher, Neil D. Sandham

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Finite difference, Intel Xeon Phi, nVidia, Tesla K40

October 3, 2017 by hgpu

Generating Custom Code for Efficient Query Execution on Heterogeneous Processors

Sebastian Bress, Bastian Kocher, Henning Funke, Tilmann Rabl, Volker Markl

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Tags: APU, Computer science, Databases, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Tesla K40

September 7, 2017 by hgpu

Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization

Viacheslav Khomenko, Oleg Shyshkov, Olga Radyvonenko, Kostiantyn Bokhan

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Tags: Algorithms, Computer science, Deep learning, LSTM, Neural networks, nVidia, Tesla K40, Theano

September 7, 2017 by hgpu

Integer sorting on multicores: some (experiments and) observations

Alexandros V. Gerbessiotis

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Tags: Algorithms, Computer science, CUDA, Intel Xeon Phi, nVidia, Performance, Sorting, Tesla K40

September 3, 2017 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

Hydra: a C++11 framework for data analysis in massively parallel platforms

Vectorized algorithm for multidimensional Monte Carlo integration on modern GPU, CPU and MIC architectures

Comparison of Parallelisation Approaches, Languages, and Compilers for Unstructured Mesh Algorithms on GPUs

A Dynamic Hash Table for the GPU

Automatic Scan Parallelization in OpenMP

GooFit 2.0

BENCHIP: Benchmarking Intelligence Processors

Parallel Matching and Clustering Algorithms on GPUs

Energy efficiency of finite difference algorithms on multicore CPUs, GPUs, and Intel Xeon Phi processors

Generating Custom Code for Efficient Query Execution on Heterogeneous Processors

Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization

Integer sorting on multicores: some (experiments and) observations

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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