PhD Defense by Alexander Merritt

Event Details
  • Date/Time:
    • Tuesday May 31, 2016
      10:00 am - 12:00 pm
  • Location: KACB 3100
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Summary Sentence: Scalable Main-Memory Object Management

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Title: Scalable Main-Memory Object Management

 

Alexander Merritt

School of Computer Science

College of Computing

Georgia Institute of Technology

 

Date: Tuesday, May 31, 2016

Time: 10AM to 12PM EST / 7AM to 9AM PDT

Location: KACB 3100

 

Committee:

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Dr. Karsten Schwan (Advisor, School of Computer Science, Georgia Tech) Dr. Ada Gavrilovska (Committee Chair, School of Computer Science, Georgia Tech) Dr. Taesoo Kim (School of Computer Science, Georgia Tech) Dr. Kishore Ramachandran  (School of Computer Science, Georgia Tech) Dr. Moinuddin Qureshi (School of Electrical and Computer Engineering, Georgia Tech) Dr. Dejan Milojicic (Hewlett Packard Labs, Hewlett Packard Enterprise)

 

Abstract:

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New and emerging memory technologies are giving rise to servers with massive pools of main memory, but these systems are difficult to program efficiently: terabytes of memory, disaggregated bandwidth, and hundreds of cores pose scalability challenges for all layers in the software stack. Transparent, granular operating system interfaces make it difficult and inefficient for applications to express semantic relationships with their data. Library allocators are subjected to much larger scales they were not designed for, as well as increasingly complex allocation behaviors, creating high memory fragmentation.

 

To navigate these challenges, this thesis proposes (1) new memory-centric operating system abstractions to more effectively manage and share both virtual and physical memory without interfacing with the filesystem or network APIs, and (2) a log-structured memory object allocator that leverages these new abstractions to more effectively make informed decisions about where data is placed and how it is accessed, scaling up to hundreds of cores by partitioning and decentralizing its components across large shared-memory platforms. We demonstrate the feasibility of this approach with data-intensive applications and workloads, including a tightly-coupled image analytics pipeline we developed, to stress the real-time capabilities of our solution.

 

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Phd Defense
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  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: May 25, 2016 - 8:51am
  • Last Updated: Oct 7, 2016 - 10:17pm