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Generation of Random Numbers on Graphics Processors: Forced Indentation In Silico of the Bacteriophage HK97

A. Zhmurov, K. Rybnikov, Y. Kholodov, V. Barsegov

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Tags: Biological Physics, Chemical Physics, CUDA, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Physics, Pseudo-random number generators

January 2, 2011 by hgpu

A fast high quality pseudo random number generator for nVidia CUDA

William B. Langdon

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Tags: Computer science, CUDA, nVidia, Package, Pseudo-random number generators, Tesla T10

December 25, 2010 by hgpu

Generating massive high-quality random numbers using GPU

Wai-Man Pang, Tien-Tsin Wong, Pheng-Ann Heng

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Tags: Algorithms, Cg, Computer science, nVidia, nVidia GeForce 7800 GTX, Pseudo-random number generators

December 22, 2010 by hgpu

Pseudo-random number generators for Monte Carlo simulations on ATI Graphics Processing Units

Vadim Demchik

Tags: ATI, ATI IL, ATI Radeon HD 4850, ATI Radeon HD 4870, ATI Radeon HD 5850, ATI Radeon HD 5870, ATI Stream, Computational Physics, High Energy Physics - Lattice, Monte Carlo simulation, Physics, Pseudo-random number generators

December 13, 2010 by hgpu

Efficient pseudo-random number generators for biomolecular simulations on graphics processors

A. Zhmurov, K. Rybnikov, Y. Kholodov, V. Barsegov

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Tags: Biological Physics, Chemical Physics, CUDA, nVidia, nVidia GeForce GTX 295, Physics, Pseudo-random number generators

November 9, 2010 by hgpu

Pseudo-random number generators for Monte Carlo simulations on Graphics Processing Units

Vadim Demchik

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Tags: ATI, ATI IL, ATI Radeon HD 4850, ATI Radeon HD 4870, ATI Radeon HD 5850, ATI Stream, Computational Physics, High Energy Physics - Lattice, Monte Carlo simulation, Physics, Pseudo-random number generators

October 28, 2010 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

Generation of Random Numbers on Graphics Processors: Forced Indentation In Silico of the Bacteriophage HK97

A fast high quality pseudo random number generator for nVidia CUDA

Generating massive high-quality random numbers using GPU

Pseudo-random number generators for Monte Carlo simulations on ATI Graphics Processing Units

Efficient pseudo-random number generators for biomolecular simulations on graphics processors

Pseudo-random number generators for Monte Carlo simulations on Graphics Processing Units

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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