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  1. Proceedings of the 2017 ACM SIGPLAN International Symposium on Memory Management (ISMM 2017)
  2. "What's in a name?" going beyond allocation site names in heap analysis
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Bridging the gap between memory performance and massive parallelism: the critical role of programming systems innovations (keynote)
NG2C: pretenuring garbage collection with dynamic generations for HotSpot big data applications
A marshalled data format for pointers in relocatable data blocks
Analyzing memory management methods on integrated CPU-GPU systems
"What's in a name?" going beyond allocation site names in heap analysis
Type-assisted automatic garbage collection for lock-free data structures
Flexible and efficient memory object metadata
Continuous checkpointing of HTM transactions in NVM
A refinement hierarchy for free list memory allocators
Shadow state encoding for efficient monitoring of block-level properties
RTHMS: a tool for data placement on hybrid memory system
Avoiding consistency exceptions under strong memory models

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"What's in a name?" going beyond allocation site names in heap analysis

Content Provider ACM Digital Library
Author Khedker, Uday P. Kanvar, Vini
Abstract A points-to analysis computes a sound abstraction of heap memory conventionally using a name-based abstraction that summarizes runtime memory by grouping locations using the names of allocation sites: All concrete heap locations allocated by the same statement are grouped together. The locations in the same group are treated alike i.e., a pointer to any one location of the group is assumed to point to every location in the group leading to an over-approximation of points-to relations.
We propose an access-based abstraction that partitions each name-based group of locations into equivalence classes at every program point using an additional criterion of the sets of access paths (chains of pointer indirections) reaching the locations in the memory. The intuition is that the locations that are both allocated and accessed alike should be grouped into the same equivalence class. Since the access paths in the memory could reach different locations at different program points, our groupings change flow sensitively unlike the name-based groupings. This creates a more precise view of the memory. Theoretically, it is strictly more precise than the name-based abstraction except in some trivial cases; practically it is far more precise.
Our empirical measurements show the benefits of our tool Access-Based Heap Analyzer (ABHA) on SPEC CPU 2006 and heap manipulating SV-COMP benchmarks. ABHA, which is field-, flow-, and context-sensitive, scales to 20 kLoC and can improve the precision even up to 99% (in terms of the number of aliases). Additionally, ABHA allows any user-defined summarization of an access path to be plugged in; we have implemented and evaluated four summarization techniques. ABHA can also act as a front-end to TVLA, a parametrized shape analyzer, in order to automate its parametrization by generating predicates that capture the program behaviour more accurately. .
Starting Page 92
Ending Page 103
Page Count 12
File Format PDF
ISBN 9781450350440
DOI 10.1145/3092255.3092267
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2017-06-18
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Access path Alias Heap abstraction Static points-to analysis Summarization Allocation site
Content Type Text
Resource Type Article
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