high performance computing on graphics processing units: hgpu.org

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Performance Portability with the Chapel Language

Albert Sidelnik, Saeed Maleki, Bradford L. Chamberlain, Maria J. Garzaran, David Padua

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 280, Optimization

February 18, 2012 by hgpu

Interactive Manycore Photon Mapping

Bartosz Fabianowski

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Tags: Algorithms, Computer science, CUDA, KD-tree, Nearest neighbour, nVidia, nVidia GeForce GTX 280, Rendering, Thesis

February 17, 2012 by hgpu

Work Stealing Inside GPUs

Julio Toss

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Tags: Computer science, CUDA, Data parallelism, nVidia, nVidia GeForce GTX 280, Task scheduling, Tesla C2050, Thesis

February 13, 2012 by hgpu

LAMMPScuda – a new GPU accelerated Molecular Dynamics Simulations Package and its Application to Ion-Conducting Glasses

Christian Robert Trott

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Tags: Biology, Computational Physics, CUDA, Heterogeneous systems, Molecular dynamics, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 470, Package, Physics, Polymer Systems, Tesla C1060, Thesis

February 13, 2012 by hgpu

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units

Jun Igarashi, Osamu Shouno, Tomoki Fukai, Hiroshi Tsujino

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Tags: Computer science, CUDA, Heterogeneous systems, Neural networks, nVidia, nVidia GeForce GTX 280, Visualization

February 9, 2012 by hgpu

Auto-Generation and Auto-Tuning of 3D Stencil Codes on GPU Clusters

Yongpeng Zhang, Frank Mueller

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Nearest neighbour, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 480, Optimization, Tesla C1060, Tesla C2050

February 8, 2012 by hgpu

GMP implementation on CUDA – A Backward Compatible Design With Performance Tuning

Hao Jun Liu, Chu Tong

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Tags: Algorithms, Computer science, CUDA, Elementary functions, nVidia, nVidia GeForce GTX 280, Performance, Security

February 7, 2012 by hgpu

GPU Nonlinear Fixed Points, with an application to GPU IFS Rendering

Orion Sky Lawlor

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Tags: Algorithms, Computer science, GLSL, nVidia, nVidia GeForce GTX 280, OpenGL, Package, Rendering

January 26, 2012 by hgpu

Using a GPU, Online Diarization – Offline Diarization

Gerald Friedland

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Tags: Acoustics, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, Speech recognition

January 26, 2012 by hgpu

Scientific Computation on Graphics Processing Unit using CUDA

Pradip Narendrakumar Panchal

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Tags: Computer science, CUDA, Differential equations, FDTD, Finite difference, Finite-difference time-domain, Fluid dynamics, Navier-Stokes equations, NSEs, nVidia, nVidia GeForce GTX 280, Partial differential equations, PDEs, Thesis

January 23, 2012 by hgpu

Data Triage and Visual Analytics for Scientific Visualization

Teng-Yok Lee

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Tags: Computer science, CUDA, GLSL, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 280, nVidia GeForce GTX 460, Thesis, Visualization

January 16, 2012 by hgpu

Spectral Method Characterization on FPGA and GPU Accelerators

Karl Pereira, Peter Athanas, Heshan Lin, Wu Feng

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Tags: Computer science, CUDA, DSP, FFT, FPGA, nVidia, nVidia GeForce GTX 280, Performance, Tesla C2050

January 15, 2012 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

Performance Portability with the Chapel Language

Interactive Manycore Photon Mapping

Work Stealing Inside GPUs

LAMMPScuda – a new GPU accelerated Molecular Dynamics Simulations Package and its Application to Ion-Conducting Glasses

Real-time simulation of a spiking neural network model of the basal ganglia circuitry using general purpose computing on graphics processing units

Auto-Generation and Auto-Tuning of 3D Stencil Codes on GPU Clusters

GMP implementation on CUDA – A Backward Compatible Design With Performance Tuning

GPU Nonlinear Fixed Points, with an application to GPU IFS Rendering

Using a GPU, Online Diarization – Offline Diarization

Scientific Computation on Graphics Processing Unit using CUDA

Data Triage and Visual Analytics for Scientific Visualization

Spectral Method Characterization on FPGA and GPU Accelerators

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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