high performance computing on graphics processing units: hgpu.org

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Join Execution Using Fragmented Columnar Indices on GPU and MIC

Elena V. Ivanova, Stepan O. Prikazchikov, Leonid B. Sokolinsky

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Tags: Computer science, CUDA, Databases, Intel Xeon Phi, nVidia, Tesla K40

December 10, 2015 by hgpu

A Parallel Solver for Markov Decision Process in Crowd Simulations

Sergio Ruiz, Benjamin Hernandez

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Tags: ARM, Computer science, Crowd simulation, CUDA, nVidia, nVidia GeForce GTX Titan, nVidia Jetson TK1, Path problems, Tesla K40, Thrust

December 9, 2015 by hgpu

Using Data Compression for Increasing Efficiency of Data Transfer Between Main Memory and Intel Xeon Phi Coprocessor or NVidia GPU in Parallel DBMS

Konstantin Y. Besedin, Pavel S. Kostenetskiy, Stepan O. Prikazchikov

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Tags: Algorithms, Compression, Computer science, CUDA, Databases, Intel Xeon Phi, nVidia, OpenMP, Tesla K40

December 6, 2015 by hgpu

Parallelization Methods of the Template Matching Method on Graphics Accelerators

Gabor Kertesz, Sandor Szenasi, Zoltan Vamossy

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Tags: Algorithms, Computer science, CUDA, Image processing, nVidia, Tesla K40

December 6, 2015 by hgpu

Neural GPUs Learn Algorithms

Lukasz Kaiser, Ilya Sutskever

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K40

December 1, 2015 by hgpu

Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

Liang-Chieh Chen

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Source codes

Tags: Caffe, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX 650 M, OpenCV, Tesla K40, Thesis

November 24, 2015 by hgpu

Large Scale Artificial Neural Network Training Using Multi-GPUs

Linnan Wang, Wei Wu, Jianxiong Xiao, Yang Yi

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan X, Tesla K40

November 20, 2015 by hgpu

A polyphase filter for many-core architectures

Karel Adamek, Jan Novotny, Wes Armour

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Tags: Algorithms, CUDA, Intel Xeon Phi, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 780 Ti, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Signal processing, Tesla K40, Tesla K80, Tesla M2090

November 12, 2015 by hgpu

Free Launch: Optimizing GPU Dynamic Kernel Launches through Thread Reuse

Guoyang Chen, Xipeng Shen

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Tags: Compilers, Computer science, CUDA, nVidia, Tesla K20, Tesla K40

November 8, 2015 by hgpu

Data Stream Classification using Random Feature Functions and Novel Method Combinations

Diego Marron, Jesse Read, Albert Bifet, Nacho Navarro

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Tags: Computer science, CUDA, Deep learning, Nearest neighbour, Neural networks, nVidia, Tesla K40

November 8, 2015 by hgpu

Exact diagonalization of quantum lattice models on coprocessors

Topi Siro, Ari Harju

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Tags: Condensed matter, CUDA, Intel Xeon Phi, Mathematical Software, nVidia, OpenMP, Physics, Tesla K40

November 4, 2015 by hgpu

On the programmability of multi-GPU computing systems

Javier Cabezas Rodriguez

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Tags: Computer science, CUDA, MPI, nVidia, Performance, Tesla C1060, Tesla K20, Tesla K40, Thesis

November 3, 2015 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

See all packages

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high performance computing on graphics processing units: hgpu.org

Join Execution Using Fragmented Columnar Indices on GPU and MIC

A Parallel Solver for Markov Decision Process in Crowd Simulations

Using Data Compression for Increasing Efficiency of Data Transfer Between Main Memory and Intel Xeon Phi Coprocessor or NVidia GPU in Parallel DBMS

Parallelization Methods of the Template Matching Method on Graphics Accelerators

Neural GPUs Learn Algorithms

Learning Representation for Scene Understanding: Epitomes, CRFs, and CNNs

Large Scale Artificial Neural Network Training Using Multi-GPUs

A polyphase filter for many-core architectures

Free Launch: Optimizing GPU Dynamic Kernel Launches through Thread Reuse

Data Stream Classification using Random Feature Functions and Novel Method Combinations

Exact diagonalization of quantum lattice models on coprocessors

On the programmability of multi-GPU computing systems

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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