high performance computing on graphics processing units: hgpu.org

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Parallel SAT-Solving with OpenCL

Sander Beckers, Gorik De Samblanx, Floris De Smedt, Toon Goedeme, Lars Struyf, Joost Vennekens

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Tags: Algorithms, Computer science, nVidia, OpenCL, SAT

October 3, 2011 by hgpu

Heterogeneous Computing with OpenCL

Benedict Gaster, David R. Kaeli, Lee Howes, Perhaad Mistry, Dana Schaa

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Tags: Algorithms, Book, Computer science, Heterogeneous systems, OpenCL, Optimization, Tutorial

October 3, 2011 by hgpu

An OpenCL Fast Fourier Transformation

Sven De Smet

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Tags: Computer science, Data Structures and Algorithms, FFT, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce 9400 M, nVidia GeForce GTX 220, nVidia GeForce GTX 280, OpenCL

October 3, 2011 by hgpu

Towards robust automatic detection of vulnerable road users: monocular pedestrian tracking from a moving vehicle

Kristof Van Beeck, Floris De Smedt, Sander Beckers, Lars Struyf, Joost Vennekens, Gorik De Samblanx, Toon Goedeme, Tinne Tuytelaars

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Tags: Algorithms, Computer science, Computer vision, nVidia, nVidia GeForce GTX 295, OpenCL

October 3, 2011 by hgpu

An Auto-tuning Solution to Data Streams Clustering in OpenCL

Jianbin Fang, Ana Lucia Varbanescu, Henk Sips

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Tags: ATI, ATI Radeon HD 5870, Clustering, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, OpenCL, Optimization, Performance

October 3, 2011 by hgpu

A New Class of Parallel Scheduling Algorithms

Wojciech Bozejko

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Tags: Algorithms, Book, Computer science, CUDA, Metaheuristics, nVidia, Optimization, Programming techniques, Task scheduling, Tesla C1060, Tesla C2050

October 2, 2011 by hgpu

Power Management and Optimization

Hari Sundararajan

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Tags: Computer science, CUDA, Energy-efficient computing, Green, nVidia, nVidia GeForce GTX 260, Performance, Thesis

October 2, 2011 by hgpu

An Execution Model and Runtime For Heterogeneous Many-Core Systems

Gregory Diamos

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Source codes

Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce 8800 GTS, nVidia GeForce 9800 GX2, nVidia GeForce GTX 280, Optimization, Package, PTX, Tesla C1060, Tesla S870, Thesis

October 2, 2011 by hgpu

Power-Efficient Accelerators for High-Performance Applications

Ganesh Suryanarayan Dasika

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Tags: Computer science, Energy-efficient computing, Performance, Thesis

October 2, 2011 by hgpu

PTask: Operating System Abstractions To Manage GPUs as Compute Devices

Christopher J. Rossbach, Jon Currey, Mark Silberstein, Baishakhi Ray, Emmett Witchel

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Tags: Computer science, CUDA, HLSL, nVidia, nVidia GeForce GT 230, nVidia GeForce GTX 470, nVidia GeForce GTX 580, OpenCL, Operating systems, Performance, Programming techniques

October 2, 2011 by hgpu

A Complete Descritpion of the UnPython and Jit4GPU Framework

Rahul Garg, Jose Nelson Amaral

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Tags: ATI, ATI CAL, ATI IL, ATI Radeon HD 5850, ATI Stream, Compilers, Computer science, OpenMP, Optimization, Performance, Python

October 2, 2011 by hgpu

Parallel probabilistic model checking on general purpose graphics processors

Dragan Bosnacki, Stefan Edelkamp, Damian Sulewski, Anton Wijs

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 285, Probabilistic model checking

October 2, 2011 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

Parallel SAT-Solving with OpenCL

Heterogeneous Computing with OpenCL

An OpenCL Fast Fourier Transformation

Towards robust automatic detection of vulnerable road users: monocular pedestrian tracking from a moving vehicle

An Auto-tuning Solution to Data Streams Clustering in OpenCL

A New Class of Parallel Scheduling Algorithms

Power Management and Optimization

An Execution Model and Runtime For Heterogeneous Many-Core Systems

Power-Efficient Accelerators for High-Performance Applications

PTask: Operating System Abstractions To Manage GPUs as Compute Devices

A Complete Descritpion of the UnPython and Jit4GPU Framework

Parallel probabilistic model checking on general purpose graphics processors

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)