Exploiting language abstraction to optimize memory efficiency

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Title: Exploiting language abstraction to optimize memory efficiency
Author: Sartor, Jennifer Bedke
Abstract: 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 .
URI: http : / /hdl .handle .net /2152 /ETD -UT -2010 -08 -1919
Date: 2010-12-13


Exploiting language abstraction to optimize memory efficiency. Doctoral dissertation, University of Texas at Austin. Available electronically from http : / /hdl .handle .net /2152 /ETD -UT -2010 -08 -1919 .

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