A cost-effective heuristic to schedule local and remote memory in cluster computers

Cluster computers represent a cost-effective alternative solution to supercomputers. In these systems, it is common to constrain the memory address space of a given processor to the local motherboard. Constraining the system in this way is much cheaper than using a full-fledged shared memory implementation among motherboards. However, memory usage among motherboards can be unfairly balanced. On the other hand, remote memory access (RMA) hardware provides fast interconnects among the motherboards of a cluster. RMA devices can be used to access remote RAM memory from a local motherboard. This work focuses on this capability in order to achieve a better global use of the total RAM memory in the system. More precisely, the address space of local applications is extended to remote motherboards and is used to access remote RAM memory. This paper presents an ideal memory scheduling algorithm and proposes a cost-effective heuristic to allocate local and remote memory among local applications. Compared to the devised ideal algorithm, the heuristic obtains the same (or closely resembling) results while largely reducing the computational cost. In addition, we analyze the impact on the performance of stand alone applications varying the memory distribution among regions (local, local to board, and remote). Then, this study is extended to any number of concurrent applications. Experimental results show that a QoS parameter is needed in order to avoid unacceptable performance degradation. © 2011 Springer Science+Business Media, LLC.This work was supported by Spanish CICYT under Grant TIN2009-14475-C04-01 and by Consolider-Ingenio under Grant CSD2006-00046.Serrano Gómez, M.; Sahuquillo Borrás, J.; Petit Martí, SV.; Hassan Mohamed, H.; Duato Marín, JF. (2012). A cost-effective heuristic to schedule local and remote memory in cluster computers. 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View-Oriented Parallel Programming and View-based Consistency

This paper proposes a novel View-Oriented Parallel Programming style for parallel programming on cluster computers. View-Oriented Parallel Programming is based on Distributed Shared Memory. It requires the programmer to divide the shared memory into views according to the nature of the parallel algorithm and its memory access pattern. The advantage of this programming style is that it can help the Distributed Shared Memory system optimise consistency maintenance. Also it allows the programmer to participate in performance optimization of a program through wise partitioning of the shared memory into views. The View-based Consistency model and its implementation, which supports View-Oriented Parallel Programming, is discussed as well in this paper. Finally some preliminary experimental results are shown to demonstrate the performance gain of View-Oriented Parallel Programming

Werstein P.: View-Oriented Parallel Programming and View-based Consistency

Abstract. This paper proposes a novel View-Oriented Parallel Programming style for parallel programming on cluster computers. View-Oriented Parallel Programming is based on Distributed Shared Memory. It requires the programmer to divide the shared memory into views according to the nature of the parallel algorithm and its memory access pattern. The advantage of this programming style is that it can help the Distributed Shared Memory system optimise consistency maintenance. Also it allows the programmer to participate in performance optimization of a program through wise partitioning of the shared memory into views. The View-based Consistency model and its implementation, which supports View-Oriented Parallel Programming, is discussed as well in this paper. Finally some preliminary experimental results are shown to demonstrate the performance gain of View-Oriented Parallel Programming.

Performance Evaluation of View-Oriented Parallel Programming

This paper evaluates the performance of a novel View-Oriented Parallel Programming style for parallel programming on cluster computers. View-Oriented Parallel Programming is based on Distributed Shared Memory which is friendly and easy for programmers to use. It requires the programmer to divide shared data into views according to the memory access pattern of the parallel algorithm. One of the advantages of this programming style is that it offers the performance potential for the underlying Distributed Shared Memory system to optimize consistency maintenance. Also it allows the programmer to participate in performance optimization of a program through wise partitioning of the shared data into views. Experimental results demonstrate a significant performance gain of the programs based on the View-Oriented Parallel Programming style