Marrying Generational GC and Region Techniques for High-Throughput, Low-Latency Big Data Memory Management

Speaker:        Dr. Guoqing (Harry) Xu
                University of California, Irvine

Title:          "Marrying Generational GC and Region Techniques for
                 High-Throughput, Low-Latency Big Data Memory Management"

Date:           Friday, 8 July 2016

Time:           11:00am - 12 noon

Venue:          Room 3501 (via lifts 25/26), HKUST

Abstract:

Most "Big Data" systems are written in managed languages such as Java, C#,
or Scala. These systems suffer from severe memory problems due to massive
volumes of objects created to process input data. Allocating and
deallocating a sea of data objects puts a severe strain on existing
garbage collectors (GC), leading to high memory management overhead and
reduced performance. We have developed a series of techniques at UC Irvine
to tackle this problem. In this talk, I will first talk about Facade, a
compiler and runtime system that can statically bounds the number of data
objects created in the heap. Next, I will talk about our recent work on
Yak, a new hybrid garbage collector that splits the managed heap into a
control and a data space, and uses a generational GC and a region-based
technique to manage them, respectively.


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Biography:

Guoqing (Harry) Xu is an assistant professor at UC Irvine. He is broadly
interested in program languages and (distributed, operating, and runtime)
systems. His recent interests center on (1) how to exploit
language/compiler techniques to build scalable Big Data systems and (2)
how to build Big Data systems to parallelize and scale sophisticated
program analyses. He publishes broadly in PL, systems, and SE conferences
such as SOSP, ASPLOS, PLDI, and OOPSLA and is an author of several papers
awarded or nominated distinguished paper award.