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A Framework for Composing High-Performance OpenCL from Python Descriptions

Michael Anderson

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Tags: Computer science, Computer vision, nVidia, nVidia GeForce GTX 480, OpenCL, Optical flow, Python, Thesis

November 29, 2014 by hgpu

A GPU-based framework for efficient image processing

Per Karlsson

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Tags: CUDA, GLSL, Image processing, nVidia, nVidia GeForce GTX 760, OpenCL, OpenGL, Python, Thesis

November 20, 2014 by hgpu

CUDArray: CUDA-based NumPy

Anders Boesen Lindbo Larsen

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

November 16, 2014 by hgpu

Deep Dynamic Neural Networks for Gesture Segmentation and Recognition

Di Wu, Ling Shao

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Tags: CNN, Computer science, CUDA, Neural networks, nVidia, Package, Python, Rendering

October 4, 2014 by hgpu

Caffe: Convolutional Architecture for Fast Feature Embedding

Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell

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Tags: Cloud, Computer science, CUDA, Neural networks, nVidia, Package, Pattern recognition, Python

August 23, 2014 by hgpu

A Domain Specific Approach to Heterogeneous Computing: From Availability to Accessibility

Gordon Inggs, David Thomas, Wayne Luk

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Tags: Computational finance, Finance, FPGA, Heterogeneous systems, OpenCL, Option pricing, Package, Python

August 23, 2014 by hgpu

High Level High Performance Computing for Multitask Learning of Time-varying Models

Marco Signoretto, Emanuele Frandi, Zahra Karevan, Johan A. K. Suykens

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Tags: Algorithms, Computer science, CUDA, nVidia, Python

August 18, 2014 by hgpu

Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems

Hyungsuk Choi, Woohyuk Choi, Tran Minh Quan, David G. C. Hildebrand, Hanspeter Pfister, Senior Member, Won-Ki Jeong

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Tags: CUDA, GPU cluster, Heterogeneous systems, Image processing, nVidia, Python, Rendering, Tesla M2090, Visualization

August 9, 2014 by hgpu

Dark Sky Simulations: Early Data Release

Samuel W. Skillman, Michael S. Warren, Matthew J. Turk, Risa H. Wechsler, Daniel E. Holz, P. M. Sutter

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Tags: Astrophysics, CUDA, Gravitation, Instrumentation and Methods for Astrophysics, N-body simulation, nVidia, Package, Python, Tesla K20

July 22, 2014 by hgpu

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

Bartosz D. Wozniak

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Tags: CUBLAS, CUDA, Fluid dynamics, Linear Algebra, Matrix multiplication, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

July 7, 2014 by hgpu

SimCommSys: taking the errors out of error-correcting code simulations

Johann A. Briffa, Stephan Wesemeyer

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Tags: Computer science, CUDA, Error recovery, nVidia, Package, Python

July 6, 2014 by hgpu

Loo.py: transformation-based code generation for GPUs and CPUs

Andreas Klockner

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Tags: ATI, ATI Radeon HD 7990, Code generation, Computer science, Heterogeneous systems, nVidia, nVidia GeForce GTX Titan, OpenCL, Package, Programming Languages, PyOpenCL, Python

June 2, 2014 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

A Framework for Composing High-Performance OpenCL from Python Descriptions

A GPU-based framework for efficient image processing

CUDArray: CUDA-based NumPy

Deep Dynamic Neural Networks for Gesture Segmentation and Recognition

A Domain Specific Approach to Heterogeneous Computing: From Availability to Accessibility

High Level High Performance Computing for Multitask Learning of Time-varying Models

Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems

Dark Sky Simulations: Early Data Release

GiMMiK – Generating Bespoke Matrix Multiplication Kernels for Various Hardware Accelerators; Applications in High-Order Computational Fluid Dynamics

SimCommSys: taking the errors out of error-correcting code simulations

Loo.py: transformation-based code generation for GPUs and CPUs

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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