BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20210402T160023Z
LOCATION:Poster Module
DTSTART;TZID=America/New_York:20201117T083000
DTEND;TZID=America/New_York:20201117T170000
UID:submissions.supercomputing.org_SC20_sess342_drs104@linklings.com
SUMMARY:Machine Intelligent and Timely Data Management for Hybrid Memory S
 ystems
DESCRIPTION:Doctoral Showcase, Posters\n\nMachine Intelligent and Timely D
 ata Management for Hybrid Memory Systems\n\nDoudali, Gavrilovska\n\nBig da
 ta analytics in datacenter platforms and data intensive simulations in exa
 scale computing environments create the need for massive main memory capac
 ities, on the order of terabytes, to boost application performance. To sat
 isfy these requirements, memory hierarchies become more complex, incorpora
 ting emerging types of technologies or disaggregation techniques to offset
  the skyrocketing cost that DRAM-only systems would impose. As we shift aw
 ay from traditional memory hierarchies, the effectiveness of existing data
  management solutions decreases, as these have not provisioned against the
  even bigger disparity in the access speeds of the heterogeneous component
 s that are now part of the memory subsystem. Additionally, system-level co
 nfiguration knobs need to be re-tuned to adjust to the speeds of the newly
  introduced memory hardware. In the face of this complexity, conventional 
 approaches to designing data management solutions with empirically-derived
  configuration parameters become impractical. This makes the case for leve
 raging machine intelligence in building a new generation of data managemen
 t solutions for hybrid memory systems. This thesis identifies the machine 
 intelligent methods that can be effective for and practically integrated w
 ith system-level memory management, and demonstrates their importance thro
 ugh the design of new components of the memory management stack; from syst
 em-level support for configuring stack parameters to memory scheduling.\n\
 nRegistration Category: Tech Program Reg Pass, Exhibits Reg Pass
END:VEVENT
END:VCALENDAR