high performance computing on graphics processing units: hgpu.org

Ilya Lashuk, Aparna Chandramowlishwaran, Harper Langston, Tuan A. Nguyen, Rahul Sampath, Aashay Shringarpure, Richard Vuduc, Lexing Ying, Denis Zorin, George Biros

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Tags: Computer science, CUDA, Fast multipole method, GPU cluster, MPI, N-body simulation, nVidia, Programming techniques, Tesla S1070

November 27, 2010 by hgpu

An Extension of the StarSs Programming Model for Platforms with Multiple GPUs

Eduard Ayguade, Rosa Badia, Francisco Igual, Jesus Labarta, Rafael Mayo, Enrique Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, nVidia, Tesla S1070

November 27, 2010 by hgpu

Quantum computer simulation using the CUDA programming model

Eladio Gutierrez, Sergio Romero, Maria A. Trenas, Emilio L. Zapata

Tags: Computer science, CUDA, nVidia, Quantum computing

November 27, 2010 by hgpu

Predictive Runtime Code Scheduling for Heterogeneous Architectures

Victor Jimenez, Lluis Vilanova, Isaac Gelado, Marisa Gil, Grigori Fursin, Nacho Navarro

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Tags: Computer science, CUDA, nVidia, nVidia GeForce 8600 GTS, Task scheduling

November 27, 2010 by hgpu

Exploiting graphical processing units for data-parallel scientific applications

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

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Tags: Computer science, CUDA, Field equations, Graph theory, nVidia, nVidia GeForce 8800 GTS

November 27, 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

Programming

Particle-Based Multiple Irregular Volume Rendering on CUDA

Fast Conjugate Gradients with Multiple GPUs

A Note on Auto-tuning GEMM for GPUs

Modeling of tsunami waves and atmospheric swirling flows with graphics processing unit

Accelerating Lattice Boltzmann Fluid Flow Simulations Using Graphics Processors

Accelerating geoscience and engineering system simulations on graphics hardware

Simulation of Shallow-Water systems using Graphics Processing Units

A massively parallel adaptive fast-multipole method on heterogeneous architectures

An Extension of the StarSs Programming Model for Platforms with Multiple GPUs

Quantum computer simulation using the CUDA programming model

Predictive Runtime Code Scheduling for Heterogeneous Architectures

Exploiting graphical processing units for data-parallel scientific applications

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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