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Parallelizing the QUDA Library for Multi-GPU Calculations in Lattice Quantum Chromodynamics

Ronald Babich, Michael A. Clark, Balint Joo

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Tags: CUDA, GPU cluster, High Energy Physics - Lattice, Monte Carlo simulation, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Package, Physics, QCD, Tesla C1060, Tesla C2050, Tesla C870

November 9, 2010 by hgpu

Quantile Mechanics II: Changes of Variables in Monte Carlo methods and a GPU-Optimized Normal Quantile

William T. Shaw, Nick Brickman

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Tags: Computational finance, CUDA, Finance, Monte Carlo simulation, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, nVidia Quadro FX 4800, Risk Management, Statistical Finance

November 8, 2010 by hgpu

Phase diagram and critical behavior of the square-lattice Ising model with competing nearest- and next-nearest-neighbor interactions

Junqi Yin, D. P. Landau

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Tags: Computational Physics, CUDA, Ising model, Monte Carlo simulation, MPI, nVidia, nVidia GeForce GTX 285, Phase transition, Physics, Statistical Mechanics

November 8, 2010 by hgpu

Evolution of thread-level parallelism in desktop applications

Geoffrey Blake, Ronald G. Dreslinski, Trevor Mudge, Krisztian Flautner

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Tags: Benchmarking, Computer science, Measurement techniques, nVidia, nVidia GeForce GT 120, nVidia GeForce GTX 285, nVidia ION

November 7, 2010 by hgpu

Iterative induced dipoles computation for molecular mechanics on GPUs

Frederico Pratas,Ricardo A. Mata,Leonel Sousa

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Tags: Chemistry, Computational chemistry, Computer science, CUDA, Molecular dynamics, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285

November 5, 2010 by hgpu

Implementing sparse matrix-vector multiplication on throughput-oriented processors

Nathan Bell, Michael Garland

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Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 285, Package, Sparse matrix

November 4, 2010 by hgpu

Fast and accurate protein substructure searching with simulated annealing and GPUs

Alex D. Stivala, Peter J. Stuckey, Anthony I. Wirth

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Tags: Bioinformatics, Biological Physics, Biology, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 285, Package, Tesla C1060

November 2, 2010 by hgpu

Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study

Shigeyoshi Tsutsui, Noriyuki Fujimoto

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Tags: Algorithms, Artificial intelligence, Computer science, CUDA, nVidia, nVidia GeForce GTX 285, Programming techniques

October 30, 2010 by hgpu

Monte Carlo integration on GPU

J. Kanzaki

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Tags: Computational Physics, CUDA, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Monte Carlo integration, nVidia, nVidia GeForce GTX 285, Package, Physics

October 27, 2010 by hgpu

GPU-based ultrafast IMRT plan optimization

Chunhua Men,Xuejun Gu,Dongju Choi,Amitava Majumdar,Ziyi Zheng,Klaus Mueller,Steve B. Jiang

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Tags: CUDA, Medicine, nVidia, nVidia GeForce 9500 GT, nVidia GeForce GTX 285, Tesla C1060, Tesla C1070

October 27, 2010 by hgpu

Importance of Explicit Vectorization for CPU and GPU Software Performance

Neil G. Dickson, Kamran Karimi, Firas Hamze

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Tags: Computer science, CUDA, Monte Carlo simulation, nVidia, nVidia GeForce GTX 285, Physics

October 27, 2010 by hgpu

On the limits of GPU acceleration

Richard Vuduc, Aparna Chandramowlishwaran, Jee Choi, Murat Guney, Aashay Shringarpure

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Tags: Computer science, nVidia, nVidia GeForce GTX 285, Performance, Tesla C1060, Tesla S1070

October 27, 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

Parallelizing the QUDA Library for Multi-GPU Calculations in Lattice Quantum Chromodynamics

Quantile Mechanics II: Changes of Variables in Monte Carlo methods and a GPU-Optimized Normal Quantile

Phase diagram and critical behavior of the square-lattice Ising model with competing nearest- and next-nearest-neighbor interactions

Evolution of thread-level parallelism in desktop applications

Iterative induced dipoles computation for molecular mechanics on GPUs

Implementing sparse matrix-vector multiplication on throughput-oriented processors

Fast and accurate protein substructure searching with simulated annealing and GPUs

Solving quadratic assignment problems by genetic algorithms with GPU computation: a case study

Monte Carlo integration on GPU

GPU-based ultrafast IMRT plan optimization

Importance of Explicit Vectorization for CPU and GPU Software Performance

On the limits of GPU acceleration

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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