Interprocedural modification side effect analysis with pointer aliasing
PLDI '93 Proceedings of the ACM SIGPLAN 1993 conference on Programming language design and implementation
Efficient flow-sensitive interprocedural computation of pointer-induced aliases and side effects
POPL '93 Proceedings of the 20th ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Context-sensitive interprocedural points-to analysis in the presence of function pointers
PLDI '94 Proceedings of the ACM SIGPLAN 1994 conference on Programming language design and implementation
Efficient context-sensitive pointer analysis for C programs
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Points-to analysis in almost linear time
POPL '96 Proceedings of the 23rd ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Compositional pointer and escape analysis for Java programs
Proceedings of the 14th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
Interprocedural pointer alias analysis
ACM Transactions on Programming Languages and Systems (TOPLAS)
Pointer analysis: haven't we solved this problem yet?
PASTE '01 Proceedings of the 2001 ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering
Polymorphic versus Monomorphic Flow-Insensitive Points-to Analysis for C
SAS '00 Proceedings of the 7th International Symposium on Static Analysis
Cloning-based context-sensitive pointer alias analysis using binary decision diagrams
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
Parameterized object sensitivity for points-to analysis for Java
ACM Transactions on Software Engineering and Methodology (TOSEM)
May-happen-in-parallel analysis of X10 programs
Proceedings of the 12th ACM SIGPLAN symposium on Principles and practice of parallel programming
Productivity and performance using partitioned global address space languages
Proceedings of the 2007 international workshop on Parallel symbolic computation
Type inference for locality analysis of distributed data structures
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Bootstrapping: a technique for scalable flow and context-sensitive pointer alias analysis
Proceedings of the 2008 ACM SIGPLAN conference on Programming language design and implementation
Semi-sparse flow-sensitive pointer analysis
Proceedings of the 36th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages
Static Detection of Place Locality and Elimination of Runtime Checks
APLAS '08 Proceedings of the 6th Asian Symposium on Programming Languages and Systems
Scaling Java points-to analysis using SPARK
CC'03 Proceedings of the 12th international conference on Compiler construction
Proceedings of the 8th annual IEEE/ACM international symposium on Code generation and optimization
Context-Sensitive points-to analysis: is it worth it?
CC'06 Proceedings of the 15th international conference on Compiler Construction
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X10 is a HPC (High Performance Computing) programming language proposed by IBM for supporting a PGAS (Partitioned Global Address Space) programming model offering a shared address space. The address space can be further partitioned into several logical locations where objects and activities (or threads) will be dynamically created. An analysis of locations can help to check the safety of object accesses through exploring which objects and activities may reside in which locations, while in practice the objects and activities are usually designated at runtime and their locations may also vary under different environments. In this paper, we propose a constraint-based locality analysis method called Leopard for X10. Leopard calculates the points-to relations for analyzing the objects and activities in a program and uses a place constraint graph to analyze their locations.We have developed a tool to support Leopard, and conducted an experiment to evaluate its effectiveness and efficiency. The experimental results show that Leopard can calculate the locations of objects and activities precisely.