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.

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.

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. The sparse inverse subset problem is the computation of the entries of the inverse of a sparse matrix for which the corresponding entry is nonzero in the factors of the matrix. We present a parallel, block-partitioned formulation of the inverse multifrontal algorithm to compute the sparse inverse subset. Numerical results for an implementation of this algorithm on an 8-processor, sharedmemory Cray-C98 architecture are discussed. We show that for large problems we obtain efficiency ratings of over 80% and performance in excess of 1 Gflop. 1. Introduction. An efficient method to compute the sparse inverse subset (Zsparse) is important in practical applications such as the computation of short circuit currents in power systems or in estimating the variances of the fitted parameters in the least-squared data-fitting problem. (The sparse inverse subset (Zsparse), is defined as the set of inverse entries in locations corresponding to the positions of nonzero entries in the LDU factorize...