Interprocedural analyses enable optimizing compilers to more precisely model the effects of non-inlined procedure calls, potentially resulting in substantial increases in application performance. Applying interprocedural analysis to programs written in object-oriented or functional languages is complicated by the difficulty of constructing an accurate program call graph. This paper presents a parameterized algorithmic framework for call graph construction in the presence of message sends and/or firstclass functions. We use this framework to describe and to implement a number of well-known and new algorithms. We then empirically assess these algorithms by applying them to a suite of medium-sized programs written in Cecil and Java, reporting on the relative cost of the analyses, the relative precision of the constructed call graphs, and the impact of this precision on the effectiveness of a number of interprocedural optimizations. 1 Introduction Interprocedural analysis can enable subs...

1 Cache Memories and Real-Time Applications Caches are used to improve the access times of fast microprocessors to relatively slow main memories. They can reduce the number of cycles a processor is waiting for data by providing faster access to recently referenced regions of memory1. Programs with hard real time constraints have to be subjected to a schedulability analysis by the compiler [17, 6]; it has to be determined whether all timing constraints can be satisfied. WCETs (Worst Case Execution Times) for processes have to be used for this. For hardware with caches, the appropriate worst case assumption is that all accesses miss the cache. This is an overly pessimistic assumption which leads to a waste of hardware resources. 1 Hennessy and Patterson [8] describe typical values for caches in 1990 workstations

Precise run-time prediction suffers from a complexity problem when doing an integrated analysis. This problem is characterised by the conflict between an optimal solution and the complexity of the computation of the solution. The analysis

: Although code transformations are routinely applied to improve the performance of programs for both scalar and parallel machines, the properties of code improving transformations are not well understood. In this paper we present a framework that enables the exploration, both analytically and experimentally, of properties of code improving transformations. The major component of the framework is a specification language, Gospel, for expressing the conditions needed to safely apply a transformation and the actions required to change the code to implement the transformation. The framework includes a technique that facilitates an analytical investigation of code improving transformations using the Gospel specifications. It also contains a tool, Genesis, that automatically produces a transformer that implements the transformations specified in Gospel. We demonstrate the usefulness of the framework by exploring the enabling and disabling properties of transformations. We first present ana...

A large number of call graph construction algorithms for object-oriented and functional languages have been proposed, each embodying different tradeoffs between analysis cost and call graph precision. In this article we present a unifying framework for understanding call graph construction algorithms and an empirical comparison of a representative set of algorithms. We first present a general parameterized algorithm that encompasses many well-known and novel call graph construction algorithms. We have implemented this general algorithm in the Vortex compiler infrastructure, a mature, multilanguage, optimizing compiler. The Vortex implementation provides a “level playing field ” for meaningful cross-algorithm performance comparisons. The costs and benefits of a number of call graph construction algorithms are empirically assessed by applying their Vortex implementation to a suite of sizeable (5,000 to 50,000 lines of code) Cecil and Java programs. For many of these applications, interprocedural analysis enabled substantial speed-ups over an already highly optimized baseline. Furthermore, a significant fraction of these speed-ups can be obtained through the use of a scalable, near-linear time call graph construction algorithm.

In previous work [1], we have developed the theoretical basis for the prediction of the cache behavior of programs by abstract interpretation. Abstract interpretation is a technique for the static analysis of dynamic properties of programs. It is semantics based, that is, it computes approximative properties of the semantics of programs. On this basis, it allows for correctness proofs of analyses. It thus replaces commonlyused ad hoc techniques by systematic, provable ones, and it allows the automatic generation of analyzers from specifications as in the Program Analyzer Generator, PAG. In this paper, abstract semantics of machine programs are refined which determine the contents of caches. For interprocedural analysis, existing methods are examined and a new approach that is especially tailored for the analysis of hardware with states is presented. This allows for a static classification of the cache behavior of memory references of programs. The calculated information can be used to...

BANE (the Berkeley Analysis Engine) is a publicly available toolkit for constructing type- and constraint-based program analyses. We describe the goals of the project, the rationale for BANE's overall design, some examples coded in BANE, and briefly compare BANE with other program analysis frameworks.

Dataflow analyses can have mutually beneficial interactions. Previous e#orts to exploit these interactions have either (1) iteratively performed each individual analysis until no further improvements are discovered or (2) developed "superanalyses " that manually combine conceptually separate analyses. We have devised a new approach that allows analyses to be defined independently while still enabling them to be combined automatically and profitably. Our approach avoids the loss of precision associated with iterating individual analyses and the implementation di#culties of manually writing a super-analysis. The key to our approach is a novel method of implicit communication between the individual components of a super-analysis based on graph transformations. In this paper, we precisely define our approach; we demonstrate that it is sound and it terminates; finally we give experimental results showing that in practice (1) our framework produces results at least as precise as iterating the individual analyses while compiling at least 5 times faster, and (2) our framework achieves the same precision as a manually written super-analysis while incurring a compiletime overhead of less than 20%.

Integrating semi-naive fixpoint iteration from deductive data bases [3, 2, 4] as well as continuations into worklist-based solvers, we derive a new application independent local fixpoint algorithm for distributive constraint systems. Seemingly different efficient algorithms for abstract interpretation like those for linear constant propagation for imperative languages [17] as well as for control-flow analysis for functional languages [13] turn out to be instances of our scheme. Besides this systematizing contribution we also derive a new efficient algorithm for abstract OLDT-resolution as considered in [15, 16, 25] for Prolog.

Because dataflow analyses are difficult to implement from scratch, reusable dataflow analysis frameworks have been developed which provide generic support facilities for managing propagation of dataflow information and iteration in loops. We have designed a framework that improves on previous work by making it easy to perform graph transformations as part of iterative analysis, to run multiple analyses “in parallel ” to achieve the precision of a single monolithic analysis while preserving modularity and reusability of the component analyses, and to construct contextsensitive interprocedural analyses from intraprocedural versions. We have implemented this framework in the Vortex optimizing compiler and used the framework to help build both traditional optimizations and non-traditional optimizations of dynamically-dispatched messages and first-class, lexically-nested functions. 1