GTC ON-DEMAND

 
SEARCH SESSIONS
SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

Presentation
Media
Abstract:
Most large companies use online analytical processing (OLAP) to gain insight from available data and guide business decisions. To support time-critical business decisions, companies must answer queries as quickly as possible. For OLAP, the performance bottlenecks are joins of large relations. GPUs can significantly accelerate these joins, but often the speed or memory capacity of a single GPU is not sufficient to join input tables or unable to do it quickly enough. We'll discuss how we're addressing these problems by proposing join algorithms that scale to multiple GPUs.
Most large companies use online analytical processing (OLAP) to gain insight from available data and guide business decisions. To support time-critical business decisions, companies must answer queries as quickly as possible. For OLAP, the performance bottlenecks are joins of large relations. GPUs can significantly accelerate these joins, but often the speed or memory capacity of a single GPU is not sufficient to join input tables or unable to do it quickly enough. We'll discuss how we're addressing these problems by proposing join algorithms that scale to multiple GPUs.  Back
 
Topics:
Accelerated Data Science, Performance Optimization
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9557
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