A chief source of inefficiency in program analysis using abstract interpretation comes from the fact that a large context (i.e., problem state) is propagated from node to node during the course of an analysis. This problem can be addressed and largely alleviated by a technique we call context projection, which projects an input context for a node to the portion that is actually relevant and determines whether the node should be reevaluated based on the projected context. This technique reduces the cost of an evaluation and eliminates unnecessary evaluations. Therefore, the efficiency of computing fixpoints over general lattices is greatly improved. A specific method, reachability, is presented as an example to accomplish context projection. Experimental results using reachability show very convincing speedups (more than eight for larger programs) that demonstrate the practical significance of context projection.

We have designed and implemented an interprocedural program analyzer generator, called system Z. Our goal is to automate the generation and management of semantics-based interprocedural program analysis for a wide range of target languages.

Traditional optimization techniques for sequential programs are not directly applicable to parallel programs where concurrent activities may interfere with each other through shared variables. New compiler techniques must be developed to accommodate features found in parallel languages. In this paper, we use abstract interpretation to obtain useful properties of programs, e.g., side effects, data dependences, object lifetime and concurrent expressions, for a language that supports first-class functions, pointers, dynamic allocations and explicit parallelism through cobegin. These analyses may facilitate many applications, such as program optimization, parallelization, restructuring, memory management, and detecting access anomalies.

The recognition of recurrence relations is important in several ways to the compilation of programs. Induction variables, the simplest form of recurrence, are pivotal in loop optimizations and dependence testing. Many recurrence relations, although expressed sequentially by the programmer, lend themselves to efficient vector or parallel computation. Despite the importance of recurrences, vectorizing and parallelizing compilers to date have recognized them only in an ad-hoc fashion. In this paper we put forth a systematic method for recognizing recurrence relations automatically. Our method has two parts. First, abstract interpretation [CC77, CC79] is used to construct a map that associates each variable assigned in a loop with a symbolic form (expression) of its value. Second, the elements of this map are matched with patterns that describe recurrence relations. The scheme is easily extensible by the addition of templates, and is able to recognize nested recurrences by the propagation of the closed forms of recurrences from inner loops. We present some applications of this method and a proof of its correctness.

The authors discuss run-time microtasking support for executing nested parallel loops on a shared-memory multiprocessor system, and present a scheme called switch-stacks for implementing such support. They first discuss current approaches to flat microtasking, and investigate how to extend these to full microtasking. They point out the problem of dummy waiting in the processor that initiates a parallel loop. To solve this problem, two schemes, dequeue-tasks and dequeue-descendant-tasks, are considered, and their disadvantages are discussed. The proposed switch-stack scheme perfectly solves the problem. These schemes have been implemented in the PARCEL run-time system. The results show that the new scheme nearly always achieves the best performance in execution time and stability.>