high performance computing on graphics processing units: hgpu.org

hgpu.org » Probabilistic model checking

PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems

Milan Ceska, Petr Pilar, Nicola Paoletti, Lubos Brim, Marta Kwiatkowska

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Tags: Algorithms, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Probabilistic model checking

February 6, 2016 by hgpu

A Comparison of Sequential and GPU Implementations of Iterative Methods to Compute Reachability Probabilities

Elise Cormie-Bowins

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Tags: Computer science, CUDA, Logic in Computer Science, nVidia, nVidia GeForce GTX 260, Probabilistic model checking

October 25, 2012 by hgpu

Improving GPU Sparse Matrix-Vector Multiplication for Probabilistic Model Checking

Anton J. Wijs, Dragan Bosnacki

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Source codes

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Package, Probabilistic model checking, Sparse matrix

August 6, 2012 by hgpu

Large scale parallel state space search utilizing graphics processing units and solid state disks

Damian Sulewski

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, Probabilistic model checking, Search strategies, Thesis

May 11, 2012 by hgpu

Designing Fast LTL Model Checking Algorithms for Many-Core GPUs

Jiri Barnat, Petr Bauch, Lubos Brim, and Milan Ceska

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Tags: Algorithms, Computer science, CUDA, Memory model, nVidia, nVidia GeForce GTX 480, Probabilistic model checking

January 24, 2012 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

GPU-PRISM: An Extension of PRISM for General Purpose Graphics Processing Units

Dragan Bosnacki, Stefan Edelkamp, Damian Sulewski, Anton Wijs

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

May 19, 2011 by hgpu

Efficient Probabilistic Model Checking on General Purpose Graphics Processors

Dragan Bosnacki, Stefan Edelkamp, Damian Sulewski

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Probabilistic model checking

November 23, 2010 by hgpu

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

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

Optical flow algorithms for SYCL

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

OpenMP5-Offload-OpenMC-Intel-PVC

Distributed OpenMP Offloading of OpenMC on Intel GPU MAX Accelerators

See all packages

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high performance computing on graphics processing units: hgpu.org

PRISM-PSY: Precise GPU-Accelerated Parameter Synthesis for Stochastic Systems

A Comparison of Sequential and GPU Implementations of Iterative Methods to Compute Reachability Probabilities

Improving GPU Sparse Matrix-Vector Multiplication for Probabilistic Model Checking

Large scale parallel state space search utilizing graphics processing units and solid state disks

Designing Fast LTL Model Checking Algorithms for Many-Core GPUs

Parallel probabilistic model checking on general purpose graphics processors

GPU-PRISM: An Extension of PRISM for General Purpose Graphics Processing Units

Efficient Probabilistic Model Checking on General Purpose Graphics Processors

Recent source codes

QArray

Celerity: High-level C++ for Accelerator Clusters

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

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

Optical flow algorithms for SYCL

OpenMP5-Offload-OpenMC-Intel-PVC

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