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Dynamic storage allocation: A survey and critical review
, 1995
"... 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 de ..."
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Cited by 214 (6 self)
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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; higherlevel 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.
Scalability of Dynamic Storage Allocation Algorithms
, 1996
"... 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 ..."
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Cited by 15 (3 self)
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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.
A Parallel Implementation Of The BlockPartitioned Inverse Multifrontal Zsparse Algorithm
, 1995
"... . 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, blockpartitioned formulation of the inverse multifrontal algorithm to compute the sparse inverse ..."
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Cited by 2 (0 self)
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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, blockpartitioned formulation of the inverse multifrontal algorithm to compute the sparse inverse subset. Numerical results for an implementation of this algorithm on an 8processor, sharedmemory CrayC98 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 leastsquared datafitting 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...
Parallel Pivots LU Algorithm on the Cray T3E
, 1999
"... . Solving large nonsymmetric sparse linear systems on distributed memory multiprocessors is an active research area. We present a looplevel parallelized generic algorithm which comprises analysefactorize and solve stages. To further exploit matrix sparsity and parallelism, the analyse step looks f ..."
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. Solving large nonsymmetric sparse linear systems on distributed memory multiprocessors is an active research area. We present a looplevel parallelized generic algorithm which comprises analysefactorize and solve stages. To further exploit matrix sparsity and parallelism, the analyse step looks for a set of compatible pivots. Sparse techniques are applied until the reduced submatrix reaches a threshold density. At this point, a switch to dense routines takes place in both analysefactorize and solve stages. The SPMD code follows a sparse cyclic distribution to map the system matrix onto a P \Theta Q processor mesh. Experimental results show a good behavior of our sequential algorithm compared with a standard generic solver: the MA48 routine. Additionally, a parallel version on the Cray T3E exhibits high performance in terms of speedup and efficiency. 1 Introduction The kernel of many computerassisted scientific applications is to solve large sparse linear systems. We find example...
Library and Information Services
, 1996
"... Enquiries about copyright, reproduction and requests for ..."