high performance computing on graphics processing units: hgpu.org

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GPU-Acceleration of In-Memory Data Analytics

Evangelia Sitaridi

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Tags: Algorithms, Computer science, CUDA, Databases, nVidia, String matching, Tesla C2070, Tesla K40, Thesis

August 18, 2016 by hgpu

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

Bo Yang, Hui Liu, Zhangxin Chen

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Tags: Computer science, CUDA, Linear Algebra, Mathematical Software, nVidia, Sparse linear iterative solvers, Sparse matrix, Tesla C2050, Tesla C2070

June 7, 2016 by hgpu

Bridging the Performance-Programmability Gap for FPGAs via OpenCL: A Case Study with OpenDwarfs

Konstantinos Krommydas, Ahmed E. Helal, Anshuman Verma, Wu-Chun Feng

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Tags: Benchmarking, Computer science, FPGA, Intel Xeon Phi, OpenCL, performance portability, Tesla C2070, Tesla K20

May 30, 2016 by hgpu

A Unified, Hardware-Fitted, Cross-GPU Performance Model

James Stevens, Andreas Klockner

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Tags: AMD Radeon R9 Fury, ATI, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenCL, Performance, Tesla C2070, Tesla K40

April 19, 2016 by hgpu

GPU Robot Motion Planning using Semi-Infinite Nonlinear Programming

Benjamin Chretien, Adrien Escande, Abderrahmane Kheddar

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Tags: Computer science, CUDA, Nonlinear optimization, nVidia, nVidia GeForce GT 650 M, nVidia GeForce GTX Titan Black, Robotics, Tesla C2070

February 25, 2016 by hgpu

GeNN: a code generation framework for accelerated brain simulations

Esin Yavuz, James Turner, Thomas Nowotny

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Tags: Biology, Code generation, CUDA, Neural networks, Neuroscience, Numerical simulation, nVidia, nVidia Quadro K 2000 M, Package, Tesla C2070, Tesla K40

January 29, 2016 by hgpu

Parallel Implementation of Vortex Element Method on CPUs and GPUs

Kseniia Kuzmina, Ilia Marchevsky, Victoriya Moreva

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Tags: CUDA, Fast multipole method, Fluid dynamics, MPI, nVidia, nVidia GeForce GTX 970, Tesla C2070

December 6, 2015 by hgpu

Efficient Resource Sharing Through GPU Virtualization on Accelerated High Performance Computing Systems

Teng Li, Vikram K. Narayana, Tarek El-Ghazawi

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Tags: Computer science, CUDA, nVidia, Tesla C2070, Virtualization

November 25, 2015 by hgpu

GPU acceleration of preconditioned solvers for ill-conditioned linear systems

Rohit Gupta

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Tags: Computer science, Conjugate gradient solver, MPI, nVidia, Tesla C2070, Thesis

October 11, 2015 by hgpu

Kinematic Modelling of Disc Galaxies using Graphics Processing Units

Georgios Bekiaris, Karl Glazebrook, Christopher J. Fluke, Roberto Abraham

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Tags: Algorithms, Astrophysics, CUDA, nVidia, Package, Tesla C2070, Tesla K40

October 8, 2015 by hgpu

Solving the Quadratic Assignment Problem on heterogeneous environment (CPUs and GPUs) with the application of Level 2 Reformulation and Linearization Technique

Alexandre Domingues Goncalves, Artur Alves Pessoa, Lucia Maria de Assumpcao Drummond, Cristiana Bentes, Ricardo Farias

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, Tesla C2070

October 8, 2015 by hgpu

Source-to-Source Automatic Program Transformations for GPU-like Hardware Accelerators

Mehdi Amini

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Tags: ATI, ATI Radeon HD 6970, Code generation, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 670, OpenCL, Tesla C1060, Tesla C2070, Thesis

August 24, 2015 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

GPU-Acceleration of In-Memory Data Analytics

Development of Krylov and AMG linear solvers for large-scale sparse matrices on GPUs

Bridging the Performance-Programmability Gap for FPGAs via OpenCL: A Case Study with OpenDwarfs

A Unified, Hardware-Fitted, Cross-GPU Performance Model

GPU Robot Motion Planning using Semi-Infinite Nonlinear Programming

GeNN: a code generation framework for accelerated brain simulations

Parallel Implementation of Vortex Element Method on CPUs and GPUs

Efficient Resource Sharing Through GPU Virtualization on Accelerated High Performance Computing Systems

GPU acceleration of preconditioned solvers for ill-conditioned linear systems

Kinematic Modelling of Disc Galaxies using Graphics Processing Units

Solving the Quadratic Assignment Problem on heterogeneous environment (CPUs and GPUs) with the application of Level 2 Reformulation and Linearization Technique

Source-to-Source Automatic Program Transformations for GPU-like Hardware Accelerators

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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