high performance computing on graphics processing units: hgpu.org

hgpu.org » Probability

Efficient and Good Delaunay Meshes From Random Points

Mohamed S. Ebeida, Scott A. Mitchell, Andrew A. Davidson, Anjul Patney, Patrick M. Knupp, John D. Owens

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Tags: Algorithms, CAD, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Probability, Rendering, Triangular meshes

January 4, 2012 by hgpu

GPU-based solution of Continuous Time Markov Chains using CUSP

Nicholas Dingle

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Tags: CUDA, Mathematics, nVidia, Probability, Tesla C2050

December 7, 2011 by hgpu

A GPU-based Simulation for Stochastic Computing

Peng Li, Weijun Xiao, David J. Lilja

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Tags: Algorithms, Computer science, CUDA, Electronic design automation, nVidia, Probability, Stochastic simulation, Tesla C2050

November 27, 2011 by hgpu

Acceleration Methods for Bayesian Network Sampling

Haohai Yu

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Tags: Algorithms, ATI, ATI Radeon HD 5870, Bayesian, Benchmarking, Clustering, Computer science, OpenCL, Probability, Thesis

November 2, 2011 by hgpu

Pricing composable contracts on the GP-GPU

Joakim Ahnfelt-Ronne, Michael Flaeno Werk

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Tags: Asia options, Finance, Monte Carlo simulation, nVidia, nVidia GeForce GTX 460, OpenCL, Probability, Tesla C2050, Thesis

October 19, 2011 by hgpu

Bit-Packed Damaged Lattice Potts Model Simulations with CUDA and GPUs

Ken A. Hawick, Mitchell G.B. Johnson

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Tags: CUDA, Data parallelism, Ising model, Materials Science, Nearest neighbour, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 295, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Phase transition, Physics, Probability, Tesla C2050, Tesla C2070

October 16, 2011 by hgpu

gpustats: GPU Library for Statistical Computing in Python

Andrew Cron, Wes McKinney

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Tags: Algorithms, Bayesian, Code generation, CUDA, Mathematics, nVidia, nVidia GeForce GTS 250, OpenCL, Package, Probability, Python, Statistics

October 13, 2011 by hgpu

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Mark Silberstein, Assaf Schuster, John D. Owens

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 285, Optimization, Package, Performance, Probability, Task scheduling, Tesla C2050

October 5, 2011 by hgpu

Randomized selection on the GPU

Laura Monroe, Joanne Wendelberger, Sarah Michalak

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce 8800 GT, nVidia GeForce GTX 285, nVidia Quadro FX 5000, nVidia Quadro FX 6000, Optimization, Presentation, Probability, Programming techniques

September 19, 2011 by hgpu

Parallel hierarchical cross entropy optimization for on-chip decap budgeting

Xueqian Zhao, Yonghe Guo, Zhuo Feng, Shiyan Hu

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Tags: Algorithms, Benchmarking, CAD, Computer science, CUDA, Nonlinear optimization, nVidia, nVidia GeForce GTX 285, Optimization, Probability

September 1, 2011 by hgpu

Fast and sleek glyph rendering for interactive HARDI data exploration

T.H.J.M. Peeters, V. Prckovska, M. van Almsick, A. Vilanova, B.M. ter Haar Romeny

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Tags: 3D Graphics and Realism, Computer science, Diffusion tensor, Magnetic resonance imaging, MRI, nVidia, nVidia GeForce 8800 GTX, Probability, Rendering, Visualization

August 27, 2011 by hgpu

Toward real-time kernel density estimate display for instrumentation

Lee Barford, Ivan Gibbs, Richard Kelley

Tags: Computer science, Probability, Visualization

July 29, 2011 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

Efficient and Good Delaunay Meshes From Random Points

GPU-based solution of Continuous Time Markov Chains using CUSP

A GPU-based Simulation for Stochastic Computing

Acceleration Methods for Bayesian Network Sampling

Pricing composable contracts on the GP-GPU

Bit-Packed Damaged Lattice Potts Model Simulations with CUDA and GPUs

gpustats: GPU Library for Statistical Computing in Python

Applying software-managed caching and CPU/GPU task scheduling for accelerating dynamic workloads

Randomized selection on the GPU

Parallel hierarchical cross entropy optimization for on-chip decap budgeting

Fast and sleek glyph rendering for interactive HARDI data exploration

Toward real-time kernel density estimate display for instrumentation

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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