high performance computing on graphics processing units: hgpu.org

Programming

hgpu.org » Programming

TTC: A Tensor Transposition Compiler for Multiple Architectures

Paul Springer, Aravind Sankaran, Paolo Bientinesi

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Tags: BLAS, Compilers, Computer science, CUDA, Intel Xeon Phi, Linear Algebra, Mathematical Software, nVidia, nVidia GeForce 840 M, Package, Performance, Tesla K40

July 8, 2016 by hgpu

GPU Based Detection of Topological Changes in Voronoi Diagrams

Massimo Bernaschi, Matteo Lulli, Mauro Sbragaglia

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Tags: Computational Physics, Condensed matter, CUDA, Lattice Boltzmann model, nVidia, Physics, Soft Condensed Matter, Voronoi diagram

July 8, 2016 by hgpu

Matrix Multiplication Beyond Auto-Tuning: Rewrite-based GPU Code Generation

Michel Steuwer, Toomas Remmelg, Christophe Dubach

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Tags: ARM, ATI, ATI Radeon HD 7970, BLAS, Code generation, Computer science, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX Titan Black, OpenCL, performance portability

July 8, 2016 by hgpu

Accelerated cryo-EM structure determination with parallelisation using GPUs in relion-2

Dari Kimanius, Bjorn O Forsberg, Sjors HW Scheres, Erik Lindahl

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Tags: Biology, Cryo-EM, CUDA, Image processing, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980 Ti

July 5, 2016 by hgpu

An Adaptive Multi-Spline Refinement Algorithm in Simulation Based Sailboat Trajectory Optimization Using Onboard Multi-Core Computer Systems

Roman Debski

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Tags: Algorithms, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GT 650 M, OpenCL

July 5, 2016 by hgpu

A Test Drive of the NVIDIA Jetson TX1 Developer Kit for Deep Learning and Computer Vision Applications

BDTI staff

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Tags: Algorithms, Caffe, Computer science, Computer vision, CUDA, Deep learning, nVidia, nVidia Jetson TK1, nVidia Tegra TX1, OpenCV

July 5, 2016 by hgpu

OpenCL Implementation of a Parallel Universal Kriging Algorithm for Massive Spatial Data Interpolation on Heterogeneous Systems

Fang Huang, Shuanshuan Bu, Jian Tao, Xicheng Tan

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Tags: Algorithms, Earth and Space Sciences, Geoscience, Heterogeneous systems, Intel Xeon Phi, nVidia, OpenCL, Tesla K20

July 5, 2016 by hgpu

Time Predictability of GPU Kernel on an HSA Compliant Platform

Marcus Larsson, Nandinbaatar Tsog

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Tags: ATI, Computer science, Heterogeneous systems, HSA, OpenCL, Thesis

July 5, 2016 by hgpu

Compiler-Assisted Workload Consolidation For Efficient Dynamic Parallelism on GPU

Hancheng Wu, Da Li, Michela Becchi

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Tags: Algorithms, Computer science, CUDA, nVidia, Performance, Tesla K20

June 30, 2016 by hgpu

Modified Levels of Parallel Odd-Even Transposition Sorting Network (OETSN) with GPU Computing using CUDA

Neetu Faujdar, SP Ghrera

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Sorting

June 30, 2016 by hgpu

DeepBE: Learning Deep Binary Encoding for Multi-Label Classification

Chenghua Li, Qi Kang, Guojing Ge, Qiang Song, Hanqing Lu, Jian Cheng

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Tags: Computer science, Computer vision, CUDA, Deep learning, Image recognition, Neural networks, nVidia, nVidia GeForce GTX Titan Black, nVidia GeForce GTX Titan X

June 30, 2016 by hgpu

Persistent RNNs: Stashing Recurrent Weights On-Chip

Gregory Diamos, Shubho Sengupta, Bryan Catanzaro, Mike Chrzanowski, Adam Coates, Erich Elsen, Jesse Engel, Awni Hannun, Sanjeev Satheesh

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Tags: Computer science, CUDA, Deep learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX Titan X, Package, RNN, Speech recognition

June 30, 2016 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

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

See all packages

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high performance computing on graphics processing units: hgpu.org

Programming

TTC: A Tensor Transposition Compiler for Multiple Architectures

GPU Based Detection of Topological Changes in Voronoi Diagrams

Matrix Multiplication Beyond Auto-Tuning: Rewrite-based GPU Code Generation

Accelerated cryo-EM structure determination with parallelisation using GPUs in relion-2

An Adaptive Multi-Spline Refinement Algorithm in Simulation Based Sailboat Trajectory Optimization Using Onboard Multi-Core Computer Systems

A Test Drive of the NVIDIA Jetson TX1 Developer Kit for Deep Learning and Computer Vision Applications

OpenCL Implementation of a Parallel Universal Kriging Algorithm for Massive Spatial Data Interpolation on Heterogeneous Systems

Time Predictability of GPU Kernel on an HSA Compliant Platform

Compiler-Assisted Workload Consolidation For Efficient Dynamic Parallelism on GPU

Modified Levels of Parallel Odd-Even Transposition Sorting Network (OETSN) with GPU Computing using CUDA

DeepBE: Learning Deep Binary Encoding for Multi-Label Classification

Persistent RNNs: Stashing Recurrent Weights On-Chip

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

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

Most viewed papers (last 30 days)