Memory management

Summary

Memory management is a form of resource management applied to computer memory. The essential requirement of memory management is to provide ways to dynamically allocate portions of memory to programs at their request, and free it for reuse when no longer needed. This is critical to any advanced computer system where more than a single process might be underway at any time. Several methods have been devised that increase the effectiveness of memory management. Virtual memory systems separate the memory addresses used by a process from actual physical addresses, allowing separation of processes and increasing the size of the virtual address space beyond the available amount of RAM using paging or swapping to secondary storage. The quality of the virtual memory manager can have an extensive effect on overall system performance. In some operating systems, e.g. OS/360 and successors, memory is managed by the operating system. In other operating systems, e.g. Unix-like ope

Official source

https://en.wikipedia.org/wiki/Memory_management

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Efficient Data Structures for Decision Diagrams

Nacereddine Ouaret

Dynamic Programming OPtimization (DPOP) is an algorithm proposed for solving distributed constraint optimization problems. In this algorithm, Hypercubes are used as the format of the messages exchanged between the agents. Then, Hybrid-DPOP (H-DPOP), a hybrid algorithm based on DPOP was proposed as an improved solution. Its main advantage over DPOP lies under the use of Multi-valued Decision Diagrams (MDDs) instead of Hypercubes. Furthermore, MDDs give a more compact representation of the messages. The MDDs were implemented and presented in by Kumar even though it was presented as Constrained Decision Diagrams (CDD) which is a generalization of MDDs. The first part of this project aims at implementing Hypercubes. An Implementation already exists. Therefore, the goal is to propose an implementation which is more efficient in term of speed and memory usage for the already implemented functions (join, project and slice). The solution implemented during this project should also provide new functions (split and re-order) and the possibility of saving Hypercubes in the XML format. The second part of the project consists in proposing and implementing a data structure that is more efficient than the MDDs implemented in H-DPOP. This data structure should also provide more functions, and also provide the possibility of saving data in the XML format.

2008

, , ,

This work is dedicated to the sensible optimization and porting of a multi-lead (ML) wavelet-transform (WT)-based electrocardiogram (ECG) wave delineator to a state-of-the-art commercial wearable embedded sensor platform with limited processing and storage resources. The original offline algorithm was recently proposed and validated in the literature, as an extension of an earlier well-established single-lead (SL) WT-based ECG delineator. Several ML ECG delineation approaches, including SL selection according to various criteria and lead combination into a single root-mean-squared (RMS) curve, are carefully optimized for real-time operation on a state-of-the-art commercial wearable embedded sensor platform. Furthermore, these ML ECG delineation approaches are contrasted in terms of their delineation accuracy, complexity and memory usage, as well as suitability for ambulatory real-time operation. Finally, the robustness and stability of the ML ECG delineation approaches are benchmarked with respect to a validated SL implementation.

2009

Adaptive Cache Mode Selection for Queries over Raw Data

Anastasia Ailamaki, Tahir Azim, Azqa Nadeem

Caching the results of intermediate query results for future re-use is a common technique for improving the performance of analytics over raw data sources. An important design choice in this regard is whether to lazily cache only the offsets of satisfying tuples, or to eagerly cache the entire tuples. Lazily cached offsets have the benefit of smaller memory requirement and lower initial caching overhead, but they are much more expensive to reuse. In this paper, we explore this tradeoff and show that neither lazy nor the eager caching mode is optimal for all situations. Instead, the ideal caching mode depends on the workload, the dataset and the cache size. We further show that choosing the sub-optimal caching mode can result in a performance penalty of over 200%. We solve this problem using an adaptive online approach that uses information about query history, cache behavior and cache size to choose the optimal caching mode automatically. Experiments on TPC-H based workloads show that our approach enables execution time to differ by, at most, 16% from the optimal caching mode, and by just 4% on the average.

2018