Quantitative performance modeling of scientific computations and creating locality in numerical algorithms

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (p. 141-150) and index.by Sivan Avraham Toledo.Ph.D

Competitive analysis of flash-memory algorithms

Abstract. The cells of flash memories can only endure a limited number of write cycles, usually between 10,000 and 1,000,000. Furthermore, cells containing data must be erased before they can store new data, and erasure operations erase large blocks of memory, not individual cells. To maximize the endurance of the device (the amount of useful data that can be written to it before one of its cells wears out), flash-based systems move data around in an attempt to reduce the total number of erasures and to level the wear of the different erase blocks. This data movement introduces interesting online problems called wear-leveling problems. We show that a simple randomized algorithm for one problem is essentially optimal. For a more difficult problem, we show that clever offline algorithms can improve upon naive approaches, but online algorithms essentially cannot.