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3D Modeling, Distance and Gradient Computation for Motion Planning: A Direct GPGPU Approach

Rene Wagner, Udo Frese, Berthold Bauml

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 680

March 12, 2013 by hgpu

Solutions For Optimizing The Radix Sort Algorithmic Function Using The Compute Unified Device Architecture

Alexandru Pirjan, Dana-Mihaela Petrosanu

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Sorting

March 7, 2013 by hgpu

Performance Upper Bound Analysis and Optimization of SGEMM on Fermi and Kepler GPUs

Junjie Lai, Andre Seznec

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Tags: Algorithms, Benchmarking, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce GTX 680, Optimization, Performance

February 22, 2013 by hgpu

GPU-based Motion Planning under Uncertainties using POMDP

Taekhee Lee, Young J. Kim

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Tags: Algorithms, Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 680

February 22, 2013 by hgpu

Developing Performance-Portable Molecular Dynamics Kernels in OpenCL

S. J. Pennycook, S. A. Jarvis

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Source codes

Tags: ATI, ATI FirePro V7800, ATI Radeon HD 6550, Benchmarking, Computer science, Molecular dynamics, nVidia, nVidia GeForce GTX 680, OpenCL, Package, Performance, Tesla C1060, Tesla C2050

February 14, 2013 by hgpu

An Implementation of Conflict-Free Offline Permutation on the GPU

Akihiko Kasagi, Koji Nakano, Yasuaki Ito

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Tags: Computer science, CUDA, Memory, Memory model, nVidia, nVidia GeForce GTX 680, Performance

February 6, 2013 by hgpu

A Multi-GPU Sources Reconstruction Method for Imaging Applications

Miguel Lopez-Portugues, Yuri Alvarez, Jesus A. Lopez-Fernandez, Cebrian Garcia, Rafael G. Ayestaran, Fernando Las-Heras

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Tags: CUDA, Electrodynamics, GPU cluster, nVidia, nVidia GeForce GTX 560, nVidia GeForce GTX 680, Signal processing

February 6, 2013 by hgpu

Regularization and nonlinearities for neural language models: when are they needed?

Marius Pachitariu, Maneesh Sahani

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Tags: CUDA, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 680, Statistics

January 25, 2013 by hgpu

Analytic Visibility on the GPU

T. Auzinger, M. Wimmer, S. Jescke

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Tags: 3D Graphics and Realism, Algorithms, Computer science, CUDA, Image generation, nVidia, nVidia GeForce GTX 680, Rendering, Triangular meshes

January 16, 2013 by hgpu

Automatic scanning of nuclear emulsions with wide-angle acceptance for nuclear fragment detection

T. Fukuda, S. Fukunaga, H. Ishida, K. Kodama, T. Matsuo, S. Mikado, S. Ogawa, H. Shibuya, J. Sudo

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Tags: CUDA, High Energy Physics - Experiment, Nuclear Experiment, Nuclear physics, nVidia, nVidia GeForce GTX 680, Physics, Tesla C2050

January 11, 2013 by hgpu

Implementation of FDTD-Compatible Green’s Function on Heterogeneous CPU-GPU Parallel Processing System

Tomasz P. Stefanski

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Tags: CUDA, Electrodynamics, FDTD, Finite-difference time-domain, Heterogeneous systems, nVidia, nVidia GeForce GTX 680

January 8, 2013 by hgpu

uBench: Performance Impact of CUDA Block Geometry

Yuri Torres, Arturo Gonzalez, Diego R. Llanos

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 680, Performance

January 3, 2013 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

3D Modeling, Distance and Gradient Computation for Motion Planning: A Direct GPGPU Approach

Solutions For Optimizing The Radix Sort Algorithmic Function Using The Compute Unified Device Architecture

Performance Upper Bound Analysis and Optimization of SGEMM on Fermi and Kepler GPUs

GPU-based Motion Planning under Uncertainties using POMDP

Developing Performance-Portable Molecular Dynamics Kernels in OpenCL

An Implementation of Conflict-Free Offline Permutation on the GPU

A Multi-GPU Sources Reconstruction Method for Imaging Applications

Regularization and nonlinearities for neural language models: when are they needed?

Analytic Visibility on the GPU

Automatic scanning of nuclear emulsions with wide-angle acceptance for nuclear fragment detection

Implementation of FDTD-Compatible Green’s Function on Heterogeneous CPU-GPU Parallel Processing System

uBench: Performance Impact of CUDA Block Geometry

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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