high performance computing on graphics processing units: hgpu.org

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GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays

Muaaz Awan, Fahad Saeed

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Tags: Algorithms, big data, Computer science, CUDA, nVidia, Package, Sorting, Tesla K40

September 10, 2016 by hgpu

A Lightweight Approach to Performance Portability with targetDP

Alan Gray, Kevin Stratford

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Tags: Computer science, CUDA, Intel Xeon Phi, nVidia, Package, Performance, performance portability, QCD, Tesla K20, Tesla K40

September 8, 2016 by hgpu

QSL Squasher: A Fast Quasi-Separatrix Layer Map Calculator

Svetlin Tassev, Antonia Savcheva

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Tags: AMD FirePro W8100, Astrophysics, ATI, OpenCL, Package, Solar and Stellar Astrophysics

September 8, 2016 by hgpu

Matrix Computations and Optimization in Apache Spark

Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Sparks, Aaron Staple, Matei Zaharia

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Scala, Spark, Tesla M2050

September 3, 2016 by hgpu

Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects

Ling-Qi Yan, Soham Uday Mehta, Ravi Ramamoorthi, Fredo Durand

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, Filtering, nVidia, nVidia GeForce GTX Titan, Package, Raytracing, Rendering

September 3, 2016 by hgpu

DeepPy: Pythonic deep learning

Anders Boesen Lindbo Larsen

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Python

September 3, 2016 by hgpu

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

Alexey Kolesnichenko, Christopher M. Poskitt, Sebastian Nanz

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Tags: Algorithms, Code generation, Computer science, CUDA, nVidia, nVidia Quadro K 2000 M, Package

August 31, 2016 by hgpu

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Yue Hu

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Tags: Code generation, Computer science, CUDA, DSL, Fluid dynamics, Intel Xeon Phi, nVidia, OpenCL, Package, PDEs, PTX, Stencil computation, Tesla K20, Thesis

August 31, 2016 by hgpu

Benchmarking State-of-the-Art Deep Learning Software Tools

Shaohuai Shi, Qiang Wang, Pengfei Xu, Xiaowen Chu

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Tags: Benchmarking, CNN, Computer science, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, RNN, Tesla K80

August 28, 2016 by hgpu

Fast Multidimensional Image Processing with OpenCL

Daniel Oliveira Dantas, Helton Danilo Passos Leal, Davy Oliveira Barros Sousa

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Tags: AMD Radeon R9 270X, ATI, CUDA, GLSL, Image processing, OpenCL, Package

August 23, 2016 by hgpu

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

Ferdinando Fioretto, Enrico Pontelli, William Yeoh, Rina Dechter

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Tags: Algorithms, Artificial intelligence, Bayesian, Computer science, nVidia, nVidia GeForce GTX Titan, nVidia GeForce GTX Titan X, Optimization, Package, Tesla M2075

August 23, 2016 by hgpu

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Kai Zhang, Wangmeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang

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Tags: CNN, CUDA, Deep learning, Image processing, Matlab, nVidia, nVidia GeForce GTX Titan X, Package, Signal denoising

August 18, 2016 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

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high performance computing on graphics processing units: hgpu.org

GPU-ArraySort: A parallel, in-place algorithm for sorting large number of arrays

A Lightweight Approach to Performance Portability with targetDP

QSL Squasher: A Fast Quasi-Separatrix Layer Map Calculator

Matrix Computations and Optimization in Apache Spark

Fast 4D Sheared Filtering for Interactive Rendering of Distribution Effects

DeepPy: Pythonic deep learning

SafeGPU: Contract- and Library-Based GPGPU for Object-Oriented Languages

A Performance Model and Optimization Strategies for Automatic GPU Code Generation of PDE Systems Described by a Domain-Specific Language

Benchmarking State-of-the-Art Deep Learning Software Tools

Fast Multidimensional Image Processing with OpenCL

Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

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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