A cluster computer performance predictor for memory scheduling

Remote Memory Access (RMA) hardware allow a given motherboard in a cluster to directly access the memory installed in a remote motherboard of the same cluster. In recent works, this characteristic has been used to extend the addressable memory space of selected motherboards, which enable a better balance of main memory resources among cluster applications. This way is much more cost-effective than than implementing a full-fledged shared memory system. In this context, the memory scheduler is in charge of finding a suitable distribution of local and remote memory that maximizes the performance and guarantees a minimum QoS among the applications. Note that since changing the memory distribution is a slow process involving several motherboards, the memory scheduler needs to make sure that the target distribution provides better performance than the current one. In this paper, a performance predictor is designed in order to find the best memory distribution for a given set of applications executing in a cluster motherboard. The predictor uses simple hardware counters to estimate the expected impact on performance of the different memory distributions. The hardware counters provide the predictor with the information about the time spent in processor, memory access and network. The performance model used by the predictor has been validated in a detailed microarchitectural simulator using real benchmarks. Results show that the prediction accuracy never deviates more than 5% compared to the real results, being less than 0.5% in most of the cases.This work was supported by Spanish CICYT under Grant TIN2009-14475-C04-01, and by Consolider-Ingenio under Grant CSD2006-00046Serrano Gómez, M.; Sahuquillo Borrás, J.; Hassan Mohamed, H.; Petit Martí, SV.; Duato Marín, JF. (2011). A cluster computer performance predictor for memory scheduling. 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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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