high performance computing on graphics processing units: hgpu.org

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Batch Method for Efficient Resource Sharing in Real-time Multi-GPU Systems

Uri Verner, Avi Mendelson, Assaf Schuster

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, Task scheduling, Tesla C2050, Tesla K10

November 4, 2013 by hgpu

GGAS: Global GPU Address Spaces for Efficient Communication in Heterogeneous Clusters

Lena Oden, Holger Froning

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, Hybrid computing, Memory model, nVidia, nVidia Quadro 2000, nVidia Quadro FX 5800, Tesla K10, Tesla K20

October 25, 2013 by hgpu

A multi-Teraflop Constituency Parser using GPUs

John Canny, David Hall, Dan Klein

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Source codes

Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 680, Package, Tesla K10

October 24, 2013 by hgpu

Three-dimensional LBM simulations of buoyancy-driven flow using Graphics processing units

Prasanna R. Redapangu, Kirti Chandra Sahu

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Tags: CUDA, Fluid dynamics, Lattice Boltzmann model, nVidia, Tesla C2070, Tesla K10

May 9, 2013 by hgpu

Automatic Code Generation for Stencil Computations on GPU Architectures

Justin Andrew Holewinski

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Tags: Code generation, Computer science, CUDA, nVidia, nVidia GeForce GTX 280, OpenCL, Tesla C2050, Tesla K10, Thesis

January 4, 2013 by hgpu

Case Studies in Acceleration of Heston’s Stochastic Volatility Financial Engineering Model: GPU, Cloud and FPGA Implementations

Christos Delivorias

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Tags: Cloud, CUDA, Finance, FPGA, nVidia, Tesla K10, Thesis

August 30, 2012 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

Batch Method for Efficient Resource Sharing in Real-time Multi-GPU Systems

GGAS: Global GPU Address Spaces for Efficient Communication in Heterogeneous Clusters

A multi-Teraflop Constituency Parser using GPUs

Three-dimensional LBM simulations of buoyancy-driven flow using Graphics processing units

Automatic Code Generation for Stencil Computations on GPU Architectures

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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