high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Computer science

Multi-Level Graph Layout on the GPU

Yaniv Frishman, Ayellet Tal

View

Download (PDF)

Tags: Cg, Computer science, Graph layout, nVidia, nVidia GeForce 8800 GTS, OpenGL

November 5, 2010 by hgpu

The GPU as numerical simulation engine

J. Bolz, I. Farmer, E. Grinspun, P. Schroder

View

Download (PDF)

Tags: Computer science, Fluid simulation, Navier-Stokes equations, NSEs, Numerical simulation, nVidia, Sparse matrix

November 5, 2010 by hgpu

Using GPUs for Machine Learning Algorithms

Dave Steinkrau, Patrice Y. Simard, Ian Buck

Tags: Computer science, Neural networks, Pattern recognition

November 5, 2010 by hgpu

Clustering billions of data points using GPUs

Ren Wu, Bin Zhang, Meichun Hsu

View

Download (PDF)

Tags: Algorithms, Clustering, Computer science, CUDA, Data mining, nVidia, nVidia GeForce GTX 280

November 5, 2010 by hgpu

Hierarchical parallel processing of large scale data clustering on a PC cluster with GPU co-processing

Hiroyuki Takizawa,Hiroaki Kobayashi

Tags: Computer science, Data mining, PC cluster

November 5, 2010 by hgpu

On Dynamic Load Balancing on Graphics Processors

Daniel Cederman, Philippas Tsigas

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce 8800 GT, nVidia GeForce 9600 GT, Optimization, Performance

November 5, 2010 by hgpu

Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA

Ogier Maitre, Laurent A. Baumes, Nicolas Lachiche, Avelino Corma, Pierre Collet

Tags: Computer science, nVidia, nVidia GeForce 8800 GTX, nVidia GeForce GTX 260, Optimization

November 5, 2010 by hgpu

MPI within a GPU

Bobby Dalton Young

View

Download (PDF)

Tags: Computer science, CUDA, Data parallelism, MPI, nVidia, nVidia GeForce 8800 GTS, Thesis, Virtualization

November 5, 2010 by hgpu

An intelligent semi-automatic application porting system for application accelerators

Thomas Henry Beach, Nicholas J. Avis

Tags: Computer science, Programming techniques

November 5, 2010 by hgpu

An experimental approach to performance measurement of heterogeneous parallel applications using CUDA

Allen D. Malony,Scott Biersdorff,Wyatt Spear,Shangkar Mayanglambam

Tags: Computer science, CUDA, nVidia, Performance

November 5, 2010 by hgpu

Streamlining GPU applications on the fly: thread divergence elimination through runtime thread-data remapping

E. Z. Zhang, Y. Jiang, G. Ziyu, X. Shen

View

Download (PDF)

Tags: Compilers, Computer science, CUDA, nVidia, Performance, Programming Languages, Tesla C1060

November 5, 2010 by hgpu

GPU-Accelerated Nearest Neighbor Search for 3D Registration

Deyuan Qiu,Stefan May,Andreas Nuchter

View

Download (PDF)

Tags: Computer science, Computer vision, CUDA, Nearest neighbour, nVidia, nVidia GeForce GTX 280, nVidia GeForce GTX 295

November 5, 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

Applications

Multi-Level Graph Layout on the GPU

The GPU as numerical simulation engine

Using GPUs for Machine Learning Algorithms

Clustering billions of data points using GPUs

Hierarchical parallel processing of large scale data clustering on a PC cluster with GPU co-processing

On Dynamic Load Balancing on Graphics Processors

Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA

MPI within a GPU

An intelligent semi-automatic application porting system for application accelerators

An experimental approach to performance measurement of heterogeneous parallel applications using CUDA

Streamlining GPU applications on the fly: thread divergence elimination through runtime thread-data remapping

GPU-Accelerated Nearest Neighbor Search for 3D Registration

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

Most viewed papers (last 30 days)