high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla C1060

Analyzing the CUDA Applications with its Latency and Bandwidth Tolerance

V.N. Pistulkar, C.A. Uttarwar

View

Download (PDF)

Tags: Computer science, CUDA, GPU cluster, nVidia, PTX, Tesla C1060

July 5, 2012 by hgpu

CUDA-accelerated Hierarchical K-means

Che-Chun Lee, Kuan-Yu Chu

View

Download (PDF)

Tags: Clustering, CUDA, Image processing, nVidia, Tesla C1060

July 1, 2012 by hgpu

A Scheduling Framework for a Heterogeneous Parallel Architecture

Wei Zhang

View

Download (PDF)

Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, nVidia, nVidia Quadro FX 1800, Task scheduling, Tesla C1060, Thesis

June 30, 2012 by hgpu

A Parallel Image Segmentation Algorithm on GPUs

P. N. Happ, R. Q. Feitosa, C. Bentes, R. Farias

View

Download (PDF)

Tags: CUDA, Image processing, nVidia, nVidia GeForce 9600 GT, Tesla C1060

June 29, 2012 by hgpu

Improving GPU Simulations of Spiking Neural P Systems

Francis George C. Cabarle, Henry N. Adorna, Miguel A. Martinez-Del-Amor, Mario J. Perez-Jimenez

View

Download (PDF)

Tags: Computer science, CUDA, Linear Algebra, Membrane computing, Neural networks, nVidia, P systems, Tesla C1060

June 25, 2012 by hgpu

Parakeet: A Just-In-Time Parallel Accelerator for Python

Alex Rubinsteyn, Eric Hielscher, Nathaniel Weinman, Dennis Shasha

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, Python, Tesla C1060

June 14, 2012 by hgpu

Range query processing in a multi-GPU environment

Ricardo J. Barrientos, Jose I. Gomez, Christian Tenllado, Manuel Prieto Matias, Mauricio Marin

View

Download (PDF)

Tags: Algorithms, Biology, Computational biology, Computer science, CUDA, Databases, GPU cluster, nVidia, OpenMP, Tesla C1060

June 11, 2012 by hgpu

Autotuning Stencil-Based Computations on GPUs

Azamat Mametjanov, Daniel Lowell, Ching-Chen May, Boyana Norris

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Differential equations, nVidia, Package, Partial differential equations, PDEs, Sparse matrix, Tesla C1060, Tesla C2070

June 9, 2012 by hgpu

Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born

Andreas W. Gotz, Mark J. Williamson, Dong Xu, Duncan Poole, Scott Le Grand, Ross C. Walker

View

Download (PDF)

Tags: Algorithms, Chemistry, CUDA, Molecular dynamics, nVidia, nVidia GeForce GTX 580, Tesla C1060, Tesla C2070, Tesla M2090

May 26, 2012 by hgpu

GMProf: A Low-Overhead, Fine-Grained Profiling Approach for GPU Programs

Mai Zheng, Vignesh T. Ravi, Wenjing Ma, Feng Qin, Gagan Agrawal

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, Optimization, Performance, Tesla C1060

May 23, 2012 by hgpu

Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing

Carlos Gonzalez, Sergio Sanchez, Abel Paz, Javier Resano, Daniel Mozos, Antonio Plaza

View

Download (PDF)

Tags: Algorithms, CUDA, FPGA, Image processing, nVidia, Sensing, Tesla C1060

May 19, 2012 by hgpu

Mapping a Data-Flow Programming Model onto Heterogeneous Platforms

Alina Sbirlea, Yi Zou, Zoran Budimlic, Jason Cong, Vivek Sarkar

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, FPGA, Heterogeneous systems, nVidia, Programming techniques, Tesla C1060

May 14, 2012 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Analyzing the CUDA Applications with its Latency and Bandwidth Tolerance

CUDA-accelerated Hierarchical K-means

A Scheduling Framework for a Heterogeneous Parallel Architecture

A Parallel Image Segmentation Algorithm on GPUs

Improving GPU Simulations of Spiking Neural P Systems

Parakeet: A Just-In-Time Parallel Accelerator for Python

Range query processing in a multi-GPU environment

Autotuning Stencil-Based Computations on GPUs

Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born

GMProf: A Low-Overhead, Fine-Grained Profiling Approach for GPU Programs

Use of FPGA or GPU-based architectures for remotely sensed hyperspectral image processing

Mapping a Data-Flow Programming Model onto Heterogeneous Platforms

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Most viewed papers (last 30 days)