Proceedings ArticleDOI
The hardness of cache conscious data placement
Erez Petrank,Dror Rawitz +1 more
- Vol. 37, Iss: 1, pp 101-112
TLDR
This work investigates the complexity of finding the optimal placement of objects (or code) in the memory, in the sense that this placement reduces the cache misses to the minimum, and shows that this problem is one of the toughest amongst the interesting algorithmic problems in computer science.Abstract:
The growing gap between the speed of memory access and cache access has made cache misses an influential factor in program efficiency. Much effort has been spent recently on reducing the number of cache misses during program run. This effort includes wise rearranging of program code, cache-conscious data placement, and algorithmic modifications that improve the program cache behavior. In this work we investigate the complexity of finding the optimal placement of objects (or code) in the memory, in the sense that this placement reduces the cache misses to the minimum. We show that this problem is one of the toughest amongst the interesting algorithmic problems in computer science. In particular, suppose one is given a sequence of memory accesses and one has to place the data in the memory so as to minimize the number of cache misses for this sequence. We show that if P ≠ NP, then one cannot efficiently approximate the optimal solution even up to a very liberal approximation ratio. Thus, this problem joins the small family of extremely inapproximable optimization problems. The other two famous members in this family are minimum coloring and maximum clique.read more
Citations
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Automatic performance tuning of sparse matrix kernels
Richard Vuduc,James Demmel +1 more
TL;DR: An automated system to generate highly efficient, platform-adapted implementations of sparse matrix kernels, and extends SPARSITY to support tuning for a variety of common non-zero patterns arising in practice, and for additional kernels like sparse triangular solve (SpTS) and computation of ATA·x and A ρ·x.
Journal ArticleDOI
Program locality analysis using reuse distance
TL;DR: Two techniques are presented, among the first to enable quantitative analysis of whole-program locality in general sequential code, that predict how the locality of a program changes with its input.
Proceedings ArticleDOI
An efficient profile-analysis framework for data-layout optimizations
TL;DR: This work proposes a parameterizable framework for data-layout optimization of general-purpose applications that can synthesize layouts that outperform existing non-iterative heuristics, tune application-specific memory allocators, as well as compose multiple data- layout optimizations.
Proceedings ArticleDOI
Array regrouping and structure splitting using whole-program reference affinity
TL;DR: A model of reference affinity is defined, which measures how close a group of data are accessed together in a reference trace, and it is proved that the model gives a hierarchical partition of program data.
We make the figures from The Garbage Collection Handbook: The Art of Automatic Memory Management, Richard Jones, Antony Hosking, Eliot Moss (Chapman and Hall, 2011) avail- able for fair use by educators and students We ask that the following credit be given: The Garbage Collection Handbook: The Art of Automatic Memory Management,
TL;DR: The Garbage Collection Handbook: The Art of Automatic Memory Management brings together a wealth of knowledge gathered by automatic memory management researchers and developers over the past fifty years and addresses new challenges to garbage collection made by recent advances in hardware and software.
References
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Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Book
Approximation Algorithms for NP-Hard Problems
TL;DR: This book reviews the design techniques for approximation algorithms and the developments in this area since its inception about three decades ago and the "closeness" to optimum that is achievable in polynomial time.
Journal ArticleDOI
Proof verification and the hardness of approximation problems
TL;DR: It is proved that no MAX SNP-hard problem has a polynomial time approximation scheme, unless NP = P, and there exists a positive ε such that approximating the maximum clique size in an N-vertex graph to within a factor of Nε is NP-hard.
Book
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Giorgio Ausiello,Pierluigi Crescenzi,Giorgio Gambosi,Viggo Kann,Alberto Marchetti-Spaccamela,Marco Protasi +5 more
TL;DR: This book documents the state of the art in combinatorial optimization, presenting approximate solutions of virtually all relevant classes of NP-hard optimization problems.