This paper reports on the design, implementation, and empirical results of a new method for dealing with the aliasing problem in C. The method is based on approximating the points-to relationships between accessible stack locations, and can be used to generate alias pairs, or used directly for other analyses and transformations. Our method provides context-sensitive interprocedural information based on analysis over invocation graphs that capture all calling contexts including recursive and mutually-recursive calling contexts. Furthermore, the method allows the smooth integration for handling general function pointers in C.

Existing methods for alias analysis of recursive pointer data structures are based on two approximation techniques: k-limiting, which blurs distinction between sub-objects below depth k; and store-based (or equivalently location or regionbased) approximations, which blur distinction between elements of recursive data structures. Although notable progress in interprocedural alias analysis has been recently accomplished, very little progress in the precision of analysis of recursive pointer data structures has been seen since the inception of these approximation techniques by Jones and Muchnick a decade ago. As a result, optimizing, verifying and parallelizing programs with pointers has remained difficult. We present a new parametric framework for analyzing recursive pointer data structures which can express a new natural class of alias information not accessib...

algorithm for Java programs. The algorithm is based on the abstraction of points-to escape graphs, which characterize how local variables and elds in objects refer to other objects. Each points-to escape graph also contains escape information, which characterizes how objects allocated in one region of the program can escape to be accessed by another region. The algorithm is designed to analyze arbitrary regions of complete or incomplete programs, obtaining complete information for objects that do not escape the analyzed regions.

In order to analyze a program that involves pointers, it is necessary to have (safe) information about what each pointer points to. There are many different approaches to computing points-to information. This paper addresses techniques for flow- and context-insensitive interprocedural analysis of stack-based storage. The paper makes two contributions to work in this area: ffl The first contribution is a set of experiments that explore the trade-offs between techniques previously defined by Lars Andersen and Bjarne Steensgaard. The former has a cubic worst-case running time, while the latter is essentially linear. However, the former may be much more precise than the latter. We have found that in practice, Andersen's algorithm is consistently more precise than Steensgaard's. For small programs, there is very little difference in the times required by the two approaches; however, for larger programs, Andersen's algorithm can be much slower than Steensgaard's. ffl The second contrib...

The first interprocedural modification side-effects analysis for C (MOD_C) that obtains better than worst-case precision on programs with general-purpose pointer usage is presented with empirical results. The analysis consists of an algorithm schema corresponding to a family of MODC algorithms with two independent phases: one for determining pointer-induced aliases and a subsequent one for propagating interprocedural side effects. These MOD_C algorithms are parameterized by the aliasing method used. The empirical results compare the performance of two dissimilar MOD_C algorithms: MOD_C(FSAlias) uses a flow-sensitive, calling-context-sensitive interprocedural alias analysis [LR92]; MOD_C(FIAlias) uses a flow-insensitive, calling-context-insensitive alias analysis which is much faster, but less accurate. These two algorithms were profiled on 45 programs ranging in size from 250 to 30,000 lines of C code, and the results demonstrate dramatically the possible cost-precision tradeoffs. This first comparative implementation of MODC analyses offers insight into the differences between flow-/context-sensitive and flow-/context-insensitive analyses. The analysis cost versus precision tradeoffs in side-effect information obtained is reported. The results show surprisingly that the precision of flow-sensitive side-effect analysis is not always prohibitive in cost, and that the precision of flow-insensitive analysis is substantially better than worst-case estimates and seems sufficient for certain applications. On average MODC (FSAlias) for procedures and calls is in the range of 20% more precise than MODC (F IAlias); however, the performance was found to be at least an order of magnitude slower than MODC (F IAlias).

This paper addresses the problem of how to apply pointer analysis to a wide variety of compiler applications. We are not presenting a new pointer analysis. Rather, we focus on putting two existing pointer analyses, points-to analysis and connection analysis, to work. We demonstrate that the fundamental problem is that one must be able to compare the memory locations read/written via pointer indirections, at different program points, and one must also be able to summarize the effect of pointer references over regions in the program. It is straightforward to compute read/write sets for indirections involving stack-directed pointers using points-to information. However, for heap-directed pointers we show that one needs to introduce the notion of anchor handles into the connection analysis and then express read/write sets to the heap with respect to these anchor handles. Based on the read/write sets we show how to extend traditional optimizations like common subexpression elimination, loop...

this article, we describe approximation methods for computing interprocedural aliases for a program written in a language that includes pointers, reference parameters, and recursion. We present the following contributions:

Escape analysis [27, 14, 5] is a static analysis that determines whether the lifetime of data exceeds its static scope. The main originality of our escape analysis is that it determines precisely the effect of assignments, which is necessary to apply it to object oriented languages with promising results, whereas previous work [27, 14, 5] applied it to functional languages and were very imprecise on assignments. Our implementation analyses the full Java TM Language. We have applied our analysis to stack allocation and synchronization elimination. We manage to stack allocate 13% to 95% of data, eliminate more than 20% of synchronizations on most programs (94% and 99% on two examples) and get up to 44% speedup (21% on average). Our detailed experimental study on large programs shows that the improvement comes more from the decrease of the garbage collection and allocation times than from improvements on data locality [7], contrary to what happened for ML [5].

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We describe a framework for flow analysis in higher-order languages. It is both a synthesis and extension of earlier work in this area, most notably [20, 22]. The framework makes explicit use of flow graphs for modeling control and data flow properties of untyped higher-order programs. The framework is parameterized, and can express a hierarchy of analyses with different cost/accuracy tradeoffs. The framework is also amenable to a direct, efficient implementation. We develop several instantiations of the framework, and prove their running-time complexity. In addition, we use the simplest instantiation to demonstrate the equivalence of a 0CFA style analysis[20] and the set-based analysis of [8]. 1 Introduction The flow analysis problem for higher-order programming languages such as Scheme[4] or ML[13] is concerned with tracking data and control flow in the presence of first-class (anonymous) procedures, rich data abstractions (e.g., lists, records, tuples, etc), and references. In th...