Dynamic memory allocation has been a fundamental part of most computer systems since roughly 1960, and memory allocation is widely considered to be either a solved problem or an insoluble one. In this survey, we describe a variety of memory allocator designs and point out issues relevant to their design and evaluation. We then chronologically survey most of the literature on allocators between 1961 and 1995. (Scores of papers are discussed, in varying detail, and over 150 references are given.) We argue that allocator designs have been unduly restricted by an emphasis on mechanism, rather than policy, while the latter is more important; higher-level strategic issues are still more important, but have not been given much attention. Most theoretical analyses and empirical allocator evaluations to date have relied on very strong assumptions of randomness and independence, but real program behavior exhibits important regularities that must be exploited if allocators are to perform well in practice.

Static analysis of concurrent programs has been hindered by the well known state explosion problem. Although many different techniques have been proposed to combat this state explosion, there is little empirical data comparing the performance of the methods. This information is essential for assessing the practical value of a technique and for choosing the best method for a particular problem. In this paper, we carry out an evaluation of three techniques for combating the state explosion problem in deadlock detection: reachability search with a partial order state space reduction, symbolic model checking, and inequality necessary conditions. We justify the method used for the comparison, and carefully analyze several sources of potential bias. The results of our evaluation provide valuable data on the kinds of programs to which each technique might best be applied. Furthermore, we believe that the methodological issues we discuss are of general significance in comparison of analysis te...

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This paper reports the results of an empirical comparison of several static analysis tools for evaluating properties of concurrent software and also reports the results of our attempts to build predictive models for each of the tools based on program and property characteristics. Although this area seems well suited to empirical investigation, we encountered a number of significant issues that make designing a sound and unbiased study surprisingly difficult. These experiment design issues are also discussed in this paper.

Finite state verification is emerging as an important technology for proving properties about software. In our experience, we have found that analysts have different expectations at different times. When an analyst is in an exploratory mode, initially formulating and verifying properties, analyses usually find inconsistencies because of flaws in the properties or in the software artifacts being analyzed. Once an inconsistency is found, the analyst begins to operate in a fault finding mode, during which meaningful counter example traces are needed to help determine the cause of the inconsistency. Eventually systems become relatively stable, but still require re-verification as evolution occurs. During such periods, the analyst is operating in a maintenance mode and would expect re-verification to usually report consistent results. Although it could be that one algorithm suits all three of these modes of use, the hypothesis explored here is that each would be best served by an algorithm optimized for the expectations of the analyst.

Dynamic storage allocation has a significant impact on computer performance. A dynamic storage allocator manages space for objects whose lifetimes are not known by the system at the time of their creation. A good dynamic storage allocator should utilize storage efficiently and satisfy requests in as few instructions as possible. A dynamic storage allocator on a multiprocessor should have the ability to satisfy multiple requests concurrently. This paper examines parallel dynamic storage allocation algorithms and how performancescales with increasing numbers of processors. The highest throughputs and lowest instruction counts are achieved with multiple free list fit I. The best memory utilization is achieved using a best fit system.

Data flow analysis is used widely in program compilation, understanding, design, and analysis tools. In data flow analysis, problemspecific information is associated with nodes and/or edges in the flow graph representation of a program or component and re-computed iteratively. A popular data flow analysis design relies on a worklist that stores all nodes and edges whose data flow information has to be re-computed. While this approach is straightforward, it has some drawbacks. First, the presence of the worklist makes data flow algorithms centralized, which may reduce effectiveness of parallel implementations of these algorithms. Second, the worklist approach is difficult to implement in a way that minimizes the amount of information passed between flow graph nodes. In this paper, we propose to use the well-known Observer pattern for implementation of data flow analyses. We argue that such implementations are more object-oriented in nature, as well as less centralized, than worklist-based ones. We argue that by adopting this Observer-based view, data flow analyses that minimize the amount of information passed between flow graph nodes can be implemented easier than by using the worklist view. We present experimental data indicating that for some types of data flow problems, even single-threaded implementations of Observer-based data flow analysis have better run times than comparable worklist-based implementations.