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Automatically Generating Efficient Simulation Codes on GPUs from Partial Differential Equations

K.A. Hawick and D.P. Playne

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Tags: Code generation, Compilers, Computer science, CUDA, Finite difference, nVidia, Partial differential equations, PDEs

February 14, 2011 by hgpu

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

K.A. Hawick, A. Leist, and D.P. Playne

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Tags: Cell processor, Computer science, CUDA, Data parallelism, Finite difference, nVidia, nVidia GeForce GTX 295, OpenCL, Partial differential equations, PDEs, Playstation, Pseudo-random number generators

February 14, 2011 by hgpu

Automated and parallel code generation for finite-differencing stencils with arbitrary data types

K.A. Hawick and D.P. Playne

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Tags: Code generation, Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX 295, Partial differential equations, PDEs

February 13, 2011 by hgpu

Comparison of GPU Architectures for Asynchronous Communication with Finite-Differencing Applications

D.P. Playne, K.A. Hawick

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Tags: Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, Partial differential equations, PDEs

February 13, 2011 by hgpu

Auto-Generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

D. P. Playne, K. A. Hawick

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Tags: Code generation, Computer science, CUDA, Finite difference, MPI, nVidia, nVidia GeForce GTX 480

February 13, 2011 by hgpu

Asynchronous Communication Schemes for Finite Difference Methods on Multiple GPUs

Daniel P. Playne, Kenneth A. Hawick

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Tags: Computer science, CUDA, Finite difference, GPU cluster, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 295

January 30, 2011 by hgpu

A New Era in Scientific Computing: Domain Decomposition Methods in Hybrid CPU-GPU Architectures

M. Papadrakakis, G. Stavroulakis, A. Karatarakis

Tags: Computational Engineering, Computer science, Finite difference, Finite element method

January 25, 2011 by hgpu

Accelerating a three-dimensional finite-difference wave propagation code using GPU graphics cards

David Michea, Dimitri Komatitsch

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Tags: CUDA, Earth and Space Sciences, FDTD, Finite difference, Geoscience, GPU cluster, MPI, nVidia, nVidia GeForce 8800 GTX, Tesla S1070

December 15, 2010 by hgpu

Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures

Kaushik Datta, Mark Murphy, Vasily Volkov, Samuel Williams, Jonathan Carter, Leonid Oliker, David Patterson, John Shalf, Katherine Yelick

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Tags: Cell processor, Computer science, CUDA, Finite difference, nVidia, nVidia GeForce GTX 280, Performance

December 14, 2010 by hgpu

On optimization of finite-difference time-domain (FDTD) computation on heterogeneous and GPU clusters

Ramtin Shams, Parastoo Sadeghi

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Tags: Computer science, FDTD, Finite difference, GPU cluster, nVidia, Optimization

November 22, 2010 by hgpu

Parallel Implementation on GPUs of ADI Finite Difference Methods for Parabolic PDEs with Applications in Finance

Duy M. Dang, Christina Christara, Kenneth R. Jackson

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Tags: CUDA, Finance, Finite difference, nVidia, Partial differential equations, PDEs, Tesla T10

November 19, 2010 by hgpu

Theory of square, rectangular, and microband electrodes through explicit GPU simulation

Ian Cutress, Richard Compton

Tags: Chemistry, Electrochemistry, Finite difference

November 8, 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

See all packages

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high performance computing on graphics processing units: hgpu.org

Automatically Generating Efficient Simulation Codes on GPUs from Partial Differential Equations

Mixing Multi-Core CPUs and GPUs for Scientific Simulation Software

Automated and parallel code generation for finite-differencing stencils with arbitrary data types

Comparison of GPU Architectures for Asynchronous Communication with Finite-Differencing Applications

Auto-Generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

Asynchronous Communication Schemes for Finite Difference Methods on Multiple GPUs

A New Era in Scientific Computing: Domain Decomposition Methods in Hybrid CPU-GPU Architectures

Accelerating a three-dimensional finite-difference wave propagation code using GPU graphics cards

Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures

On optimization of finite-difference time-domain (FDTD) computation on heterogeneous and GPU clusters

Parallel Implementation on GPUs of ADI Finite Difference Methods for Parabolic PDEs with Applications in Finance

Theory of square, rectangular, and microband electrodes through explicit GPU simulation

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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