high performance computing on graphics processing units: hgpu.org

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Deep In-GPU Experience Replay

Ben Parr

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Tags: Artificial intelligence, Computer science, nVidia, nVidia GeForce GTX Titan X

January 13, 2018 by hgpu

GPU Acceleration of a High-Order Discontinuous Galerkin Incompressible Flow Solver

Ali Karakus, Noel Chalmers, Kasia Swirydowicz, Timothy Warburton

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Tags: Computational Physics, CUDA, Fluid dynamics, Navier-Stokes equations, NSEs, Numerical Analysis, nVidia, Tesla P100

January 6, 2018 by hgpu

Rubus: A compiler for seamless and extensible parallelism

Muhammad Adnan, Faisal Aslam, Zubair Nawaz, Syed Mansoor Sarwar

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Tags: Aparapi, Benchmarking, Computer science, CUDA, Java, Matrix multiplication, nVidia, nVidia GeForce GT 630 M, OpenCL, OpenGL, Package

January 6, 2018 by hgpu

ThunderSVM: A Fast SVM Library on GPUs and CPUs

Zeyi Wen, Jiashuai Shi, Bingsheng He, Qinbin Li, Jian Chen

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Tags: Computer science, CUDA, Data mining, Machine learning, nVidia, OpenMP, Package, Tesla P100

January 6, 2018 by hgpu

Analysing the Performance of GPU Hash Tables for State Space Exploration

Nathan Cassee, Anton Wijs

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Tags: Computer science, CUDA, Design space exploration, Hashing, nVidia, nVidia GeForce GT 750 M, Performance

January 6, 2018 by hgpu

Design and Implementation of the Futhark Programming Language

Troels Henriksen

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Tags: AMD FirePro W8100, ATI, Computer science, Haskell, High-level Languages, nVidia, OpenCL, Package, Tesla K40, Thesis

December 28, 2017 by hgpu

A Generic Inverted Index Framework for Similarity Search on the GPU

Jingbo Zhou, Qi Guo, H. V. Jagadish, Lubos Krcal, Siyuan Liu, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

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Tags: Computer science, CUDA, Hashing, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, OpenMPI, Package

December 28, 2017 by hgpu

Protecting Real-Time GPU Applications on Integrated CPU-GPU SoC Platforms

Waqar Ali, Heechul Yun

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Tags: Computer science, CUDA, nVidia, nVidia Jetson TK1, Operating systems, Performance, Security, SoC

December 28, 2017 by hgpu

Extending OmpSs for OpenCL kernel co-execution in heterogeneous systems

B. Perez, E. Stafford, J. L. Bosque, R. Beivide, S. Mateo, X. Teruel, X. Martorell, E. Ayguade

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, OmpSs, OpenCL, OpenMP, Tesla K20

December 24, 2017 by hgpu

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

Ammar Ahmad Awan, Hari Subramoni, Dhabaleswar K. Panda

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Tags: Benchmarking, Caffe, Computer science, CUBLAS, CUDA, Deep learning, Intel Xeon Phi, Machine learning, nVidia, Tela K40, Tesla K80, Tesla P100

December 24, 2017 by hgpu

An MPI-Based Python Framework for Distributed Training with Keras

Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu

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Tags: Computer science, CUDA, Deep learning, Keras, Machine learning, MPI, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Python, Tesla K80

December 24, 2017 by hgpu

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

Hsi-Yu Schive, John A. ZuHone, Nathan J. Goldbaum, Matthew J. Turk, Massimo Gaspari, Chin-Yu Cheng

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Tags: ARM, Astrophysics, Chemistry, CUDA, Instrumentation and Methods for Astrophysics, Magnetohydrodynamics, MPI, nVidia, OpenMP, Package, Tesla K20, Tesla P100

December 24, 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

Deep In-GPU Experience Replay

GPU Acceleration of a High-Order Discontinuous Galerkin Incompressible Flow Solver

Rubus: A compiler for seamless and extensible parallelism

ThunderSVM: A Fast SVM Library on GPUs and CPUs

Analysing the Performance of GPU Hash Tables for State Space Exploration

Design and Implementation of the Futhark Programming Language

A Generic Inverted Index Framework for Similarity Search on the GPU

Protecting Real-Time GPU Applications on Integrated CPU-GPU SoC Platforms

Extending OmpSs for OpenCL kernel co-execution in heterogeneous systems

An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures

An MPI-Based Python Framework for Distributed Training with Keras

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

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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