CP/M

Summary

CP/M, originally standing for Control Program/Monitor and later Control Program for Microcomputers, is a mass-market operating system created in 1974 for Intel 8080/85-based microcomputers by Gary Kildall of Digital Research, Inc. Initially confined to single-tasking on 8-bit processors and no more than 64 kilobytes of memory, later versions of CP/M added multi-user variations and were migrated to 16-bit processors. The combination of CP/M and S-100 bus computers became an early standard in the microcomputer industry. This computer platform was widely used in business through the late 1970s and into the mid-1980s. CP/M increased the market size for both hardware and software by greatly reducing the amount of programming required to install an application on a new manufacturer's computer.

Official source

https://en.wikipedia.org/wiki/CP/M

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Cherry Picking: A Semantic Query Processing Strategy for the Evaluation of Expensive Predicates

A common requirement of many scienti c applications is the ability to process queries involving expensive predicates corresponding to user programs. Optimizing such queries is hard because static cost predictions and statistical estimates are not applicable. In this paper, we propose a novel approach, called Cherry Picking (CP), based on the modelling of data dependencies among expensive predicate input values as a k-partite graph. We show how CP can be easily integrated into a cost-based query processor. We propose a CP Greedy algorithm that processes the graph by selecting candidate values that minimize query execution cost, and the Epredicate algorithm that processes tuples in pipeline following the CP approach. Based on performance simulation, we show that these algorithms yields executions up to 86% faster than statically chosen pipeline strategies.

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Parallel algorithms for tensor completion in the CP format

Daniel Kressner, André Uschmajew

Low-rank tensor completion addresses the task of filling in missing entries in multidimensional data. It has proven its versatility in numerous applications, including context aware recommender systems and multivariate function learning. To handle large-scale datasets and applications that feature high dimensions, the development of distributed algorithms is central. In this work, we propose novel, highly scalable algorithms based on a combination of the canonical polyadic (CP) tensor format with block coordinate descent methods. Although similar algorithms have been proposed for the matrix case, the case of higher dimensions gives rise to a number of new challenges and requires a different paradigm for data distribution. The convergence of our algorithms is analyzed and numerical experiments illustrate their performance on distributed-memory architectures for tensors from a range of different applications. (C) 2015 Elsevier B.V. All rights reserved.

Elsevier Science Bv2016