GTC ON-DEMAND

 
SEARCH SESSIONS
SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

Presentation
Media
Abstract:

Come join us, and learn how to build a data-centric GPU cluster for artificial intelligence. Mellanox is a leader in high-performance, scalable, low-latency network interconnects for both InfiniBand and Ethernet. We will briefly present the state of the art techniques for distributed machine learning, and what special requirements they impose on the system, followed by an overview of interconnect technologies used to scale and accelerate distributed machine learning including RDMA, NVIDIA's GPUDirect technology and in-network computing use to accelerates large scale deployments in HPC and artificial intelligence.

Come join us, and learn how to build a data-centric GPU cluster for artificial intelligence. Mellanox is a leader in high-performance, scalable, low-latency network interconnects for both InfiniBand and Ethernet. We will briefly present the state of the art techniques for distributed machine learning, and what special requirements they impose on the system, followed by an overview of interconnect technologies used to scale and accelerate distributed machine learning including RDMA, NVIDIA's GPUDirect technology and in-network computing use to accelerates large scale deployments in HPC and artificial intelligence.

  Back
 
Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Israel
Year:
2017
Session ID:
SIL7120
Download:
Share:
 
Abstract:
To demonstrate the application performance improvement using GPUDirect RDMA, we utilized a general-purpose GPU Molecular Dynamics simulation application called HOOMD-blue. The code was modified and tuned for GPUDirect RDMA and for dual GPU/InfiniBand configuration in order to exploit higher scalability performance than ever achieved on this energy-efficient cluster before the introduction of GPUDirect RDMA. The goal is to present the improvements seen in the application performance of HOOMD-blue, as well as to show the best practices for properly configuring and running GPUDirect RDMA over both of the GPUs and the dual FDR InfiniBand hardware available on the Wilkes supercomputer.
To demonstrate the application performance improvement using GPUDirect RDMA, we utilized a general-purpose GPU Molecular Dynamics simulation application called HOOMD-blue. The code was modified and tuned for GPUDirect RDMA and for dual GPU/InfiniBand configuration in order to exploit higher scalability performance than ever achieved on this energy-efficient cluster before the introduction of GPUDirect RDMA. The goal is to present the improvements seen in the application performance of HOOMD-blue, as well as to show the best practices for properly configuring and running GPUDirect RDMA over both of the GPUs and the dual FDR InfiniBand hardware available on the Wilkes supercomputer.  Back
 
Topics:
Performance Optimization1, Computational Physics, Life & Material Science, HPC and Supercomputing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5169
Streaming:
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