The programming language and underlying hardware determine application
performance, and both are undergoing revolutionary shifts. As
applications have become more sophisticated and capable, programmers
have chosen managed languages in many domains for ease of development.
These languages abstract memory management from the programmer, which
can introduce time and space overhead but also provide opportunities
for dynamic optimization. Optimizing memory performance is in part
paramount because hardware is reaching physical limits. Recent trends
towards chip multiprocessor machines exacerbate the memory system
bottleneck because they are adding cores without adding commensurate
bandwidth. Both language and architecture trends add stress to the
memory system and degrade application performance.
This dissertation exploits the language abstraction to analyze and
optimize memory efficiency on emerging hardware. We study the sources
of memory inefficiencies on two levels: heap data and hardware storage
traffic. We design and implement optimizations that change the heap
layout of arrays, and use program semantics to eliminate useless
memory traffic. These techniques improve memory system efficiency and
performance.
We first quantitatively characterize the problem by comparing many
data compression algorithms and their combinations in a limit study of
Java benchmarks. We find that arrays are a dominant source of heap
inefficiency. We introduce z-rays, a new array layout design, to
bridge the gap between fast access, space efficiency and
predictability. Z-rays facilitate compression and offer flexibility,
and time and space efficiency.
We find that there is a semantic mismatch between managed languages,
with their rapid allocation rates, and current hardware, causing
unnecessary and excessive traffic in the memory subsystem. We take
advantage of the garbage collector's identification of dead data
regions, communicating information to the caches to eliminate useless
traffic to memory. By reducing traffic and bandwidth, we improve
performance.
We show that the memory abstraction in managed languages is not just a
cost to be borne, but an opportunity to alleviate the memory
bottleneck. This thesis shows how to exploit this abstraction to
improve space and time efficiency and overcome the memory wall. We
enhance the productivity and performance of ubiquitous managed
languages on current and future architectures. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2010-08-1919 |
| Date | 13 December 2010 |
| Creators | Sartor, Jennifer Bedke |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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