GTC ON-DEMAND

 
SEARCH SESSIONS
SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

Presentation
Media
Abstract:

The goal of this session is to compare the performance of graph matching and graph coloring algorithms on massively parallel devices such as GPUs. We present novel algorithms, which produce superior results for certain graphs and also discuss the techniques used to efficiently implement these algorithms on the GPU.

The goal of this session is to compare the performance of graph matching and graph coloring algorithms on massively parallel devices such as GPUs. We present novel algorithms, which produce superior results for certain graphs and also discuss the techniques used to efficiently implement these algorithms on the GPU.

  Back
 
Topics:
Developer - Algorithms
Type:
Talk
Event:
GTC Silicon Valley
Year:
2012
Session ID:
S2332
Streaming:
Download:
Share:
 
Speakers:
David M., Patrice Castonguay
- Stanford University
Abstract:
We will describe a scalable and efficient high-order unstructured compressible flow solver for GPUs. The solver allows the achievement of arbitrary order of accuracy for flows over complex geometries. High-order solvers require more operations per degree of freedom, thus making them highly suitable for massively parallel processors. Preliminary results indicate speed-ups up to 70x with the Tesla C1060 compared to the Intel i7 CPU. Memory access was optimized using shared and texture memory.
We will describe a scalable and efficient high-order unstructured compressible flow solver for GPUs. The solver allows the achievement of arbitrary order of accuracy for flows over complex geometries. High-order solvers require more operations per degree of freedom, thus making them highly suitable for massively parallel processors. Preliminary results indicate speed-ups up to 70x with the Tesla C1060 compared to the Intel i7 CPU. Memory access was optimized using shared and texture memory.  Back
 
Topics:
Computational Fluid Dynamics, Developer - Algorithms, Physics Simulation
Type:
Talk
Event:
GTC Silicon Valley
Year:
2010
Session ID:
S102079
Streaming:
Download:
Share:
 
Speakers:
Patrice Castonguay
- Stanford University
Abstract:
The objective of this project is to develop a scalable and efficient high-order unstructured compressible flow solver for GPUs. The solver allows the achievement of arbitrary order of accuracy for flows over complex geometries. High-order solvers require more operations per degree of freedom, thus making them highly suitable for massively parallel processors. Preliminary results indicate speed-ups up to 70x with the Tesla C1060 compared to the Intel i7 CPU. Memory access was optimized using shared and texture memory.
The objective of this project is to develop a scalable and efficient high-order unstructured compressible flow solver for GPUs. The solver allows the achievement of arbitrary order of accuracy for flows over complex geometries. High-order solvers require more operations per degree of freedom, thus making them highly suitable for massively parallel processors. Preliminary results indicate speed-ups up to 70x with the Tesla C1060 compared to the Intel i7 CPU. Memory access was optimized using shared and texture memory.  Back
 
Topics:
Computational Fluid Dynamics
Type:
Poster
Event:
GTC Silicon Valley
Year:
2010
Session ID:
P10D01
Download:
Share:
 
 
Previous
  • Amazon Web Services
  • IBM
  • Cisco
  • Dell EMC
  • Hewlett Packard Enterprise
  • Inspur
  • Lenovo
  • SenseTime
  • Supermicro Computers
  • Synnex
  • Autodesk
  • HP
  • Linear Technology
  • MSI Computer Corp.
  • OPTIS
  • PNY
  • SK Hynix
  • vmware
  • Abaco Systems
  • Acceleware Ltd.
  • ASUSTeK COMPUTER INC
  • Cray Inc.
  • Exxact Corporation
  • Flanders - Belgium
  • Google Cloud
  • HTC VIVE
  • Liqid
  • MapD
  • Penguin Computing
  • SAP
  • Sugon
  • Twitter
Next