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Adaptable particle-in-cell algorithms for graphical processing units

Viktor K. Decyk, Tajendra V. Singh

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Tags: CUDA, nVidia, Particle-in-cell methods, Physics, Presentation, Tesla C1060

January 26, 2011 by hgpu

Data-parallel algorithms and data structures

John Owens

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Tags: Algorithms, Computer science, Data parallelism, Presentation

January 6, 2011 by hgpu

NVIDIA CUDA software and gpu parallel computing architecture

David Kirk

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Tags: Computer science, CUDA, High-level Languages, nVidia, Presentation

December 28, 2010 by hgpu

A low-power handheld GPU using logarithmic arithmetic and triple DVFS power domains

Byeong-Gyu Nam, Jeabin Lee, Kwanho Kim, Seung Jin Lee, and Hoi-Jun Yoo

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Tags: 3D Graphics and Realism, Computer science, Energy-efficient computing, OpenGL, Presentation

December 28, 2010 by hgpu

Simulation of deformable environment with haptic feedback on GPU

Marco Altomonte, Davide Zerbato, Debora Botturi, Paolo Fiorin

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Tags: Computer science, CUDA, nVidia, Presentation, Surgical simulation

December 28, 2010 by hgpu

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

Veerendra Allada, Troy Benjegerdes, Brett Bode

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Tags: Computer science, CUDA, GPU cluster, nVidia, Performance, Presentation, Tesla S1070, Tesla T10

December 27, 2010 by hgpu

GPU Parallelization of Algebraic Dynamic Programming

Peter Steffen, Robert Giegerich, Mathieu Giraud

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Source codes

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Package, Presentation, Programming techniques

December 17, 2010 by hgpu

GPU-Assisted High Quality Particle Rendering

Deukhyun Cha, Sungjin Son, Insung Ihm

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Tags: 3D Graphics and Realism, Cg, Computer science, nVidia, nVidia GeForce GTX 280, OpenGL, Presentation

December 14, 2010 by hgpu

GPU architecture overview

John Owens

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Tags: Computer science, Hardware, Presentation, Review

December 5, 2010 by hgpu

GPU physics

Mark Harris

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Tags: Computer science, CUDA, nVidia, OpenGL, Presentation, Review

December 5, 2010 by hgpu

Issues and challenges in compiling for graphics processors

Norm Rubin

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Tags: Computer science, High-level Languages, Presentation, Review

November 4, 2010 by hgpu

Implementation of a Lattice–Boltzmann method for numerical fluid mechanics using the nVIDIA CUDA technology

E. Riegel, T. Indinger, N. Adams

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Tags: CUDA, Fluid dynamics, GPU cluster, Lattice Boltzmann model, LBUltra, nVidia, nVidia GeForce 8800 GTS, Presentation, Tesla C1060, Tesla S1070

November 2, 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

* * *

high performance computing on graphics processing units: hgpu.org

Adaptable particle-in-cell algorithms for graphical processing units

Data-parallel algorithms and data structures

NVIDIA CUDA software and gpu parallel computing architecture

A low-power handheld GPU using logarithmic arithmetic and triple DVFS power domains

Simulation of deformable environment with haptic feedback on GPU

Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster

GPU Parallelization of Algebraic Dynamic Programming

GPU-Assisted High Quality Particle Rendering

GPU architecture overview

GPU physics

Issues and challenges in compiling for graphics processors

Implementation of a Lattice–Boltzmann method for numerical fluid mechanics using the nVIDIA CUDA technology

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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