high performance computing on graphics processing units: hgpu.org

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MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures

Yang You, Shuaiwen Leon Song, Haohuan Fu, Andres Marquez, Guangwen Yang, Kevin Barker, Kirk W. Cameron, Maryam Mehri Dehnavi, Amanda Peters Randles

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Tags: Computer science, CUDA, Intel Xeon Phi, Machine learning, nVidia, Tesla K20, Vector Machine

June 20, 2014 by hgpu

GPU Implementation of Gaussian Processes

Ying Deng, Edward Khon, Yuanruo Liang, Terence Lim, Jun Wei Ng, Lixiaonan Yin

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 570

June 11, 2014 by hgpu

Mapping parallel programs to heterogeneous multi-core systems

Dominik Grewe

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Tags: Algorithms, ATI, ATI Radeon HD 7990, Computer science, Heterogeneous systems, Machine learning, nVidia, nVidia GeForce GTX 580, OpenCL, Package, Thesis

June 5, 2014 by hgpu

Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers

J.D. Tyzack, H.Y. Mussa, M.J. Williamson,J. Kirchmair, R.C. Glen.

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Tags: Biochemistry, CUDA, Machine learning, nVidia, nVidia GeForce GT 640, nVidia Tesla C2075

May 31, 2014 by hgpu

SMCGen: Generating Reconfigurable Design for Sequential Monte Carlo Applications

Thomas C.P. Chau, Maciej Kurek, James S. Targett, Jake Humphrey, George Skouroupathis, Alison Eele, Jan Maciejowski, Benjamin Cope, Kathryn Cobden, Philip Leong, Peter Y.K. Cheung, Wayne Luk

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Tags: Algorithms, Computer science, CUDA, FPGA, Machine learning, nVidia, Package, Tesla C2070

May 29, 2014 by hgpu

Automatic Scheduling of Compute Kernels Across Heterogeneous Architectures

Robert F. Lyerly

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Tags: Compilers, Computer science, Heterogeneous systems, Intel Xeon Phi, Machine learning, nVidia, OpenCL, OpenMP, Task scheduling, Tesla C2075

May 9, 2014 by hgpu

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

Robert T. McGibbon, Bharath Ramsundar, Mohammad M. Sultan, Gert Kiss, Vijay S. Pande

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Tags: Algorithms, Biology, Biophysics, CUDA, Distributed computing, Machine learning, Molecular dynamics, nVidia, nVidia GeForce GTX Titan

May 9, 2014 by hgpu

One weird trick for parallelizing convolutional neural networks

Alex Krizhevsky

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Tags: Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, nVidia, Tesla K20

April 25, 2014 by hgpu

Measuring the evolving Internet ecosystem with exchange points

Mohammad Zubair Ahmad

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Tags: Computer science, CUDA, Machine learning, Networks, nVidia, nVidia GeForce 9400, nVidia GeForce GTX 260 M, Thesis

April 22, 2014 by hgpu

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Andrew L. Beam, Alison Motsinger-Reif, Jon Doyle

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Tags: Bayesian, Biology, CUDA, Machine learning, Neural networks, nVidia, Package, Python

April 17, 2014 by hgpu

Exploring the power of GPU’s for training Deep Belief Networks

Vivek Kulkarni

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Tags: Computer science, CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 570

April 9, 2014 by hgpu

Parallel Support Vector Machines in Practice

Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Tesla C2075

April 6, 2014 by hgpu

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

Unified schemes for directive-based GPU offloading

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

See all packages

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high performance computing on graphics processing units: hgpu.org

MIC-SVM: Designing A Highly Efficient Support Vector Machine For Advanced Modern Multi-Core and Many-Core Architectures

GPU Implementation of Gaussian Processes

Mapping parallel programs to heterogeneous multi-core systems

Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers

SMCGen: Generating Reconfigurable Design for Sequential Monte Carlo Applications

Automatic Scheduling of Compute Kernels Across Heterogeneous Architectures

Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models

One weird trick for parallelizing convolutional neural networks

Measuring the evolving Internet ecosystem with exchange points

Bayesian Neural Networks for Genetic Association Studies of Complex Disease

Exploring the power of GPU’s for training Deep Belief Networks

Parallel Support Vector Machines in Practice

Recent source codes

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

tdg-benchs: benchmarks used to test the performance of taskgraph

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

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