Andreas de Blanche and Thomas Lundqvist
Cluster, cloud, multicore, memory bandwidth, co-scheduling, slowdown
When two or more programs are co-scheduled on the same multicore computer they might experience a slowdown due to the limited off-chip memory bandwidth. According to our measurements, this slowdown does not depend on the total bandwidth use in a simple way. One thing we observe is that a higher memory bandwidth usage will not always lead to a larger slowdown. This means that relying on bandwidth usage as input to a job scheduler might cause non-optimal scheduling of processes on multicore nodes in clusters, clouds, and grids. To guide scheduling decisions, we instead propose a slowdown based characterization approach. Real slowdowns are complex to measure due to the exponential number of experiments needed. Thus, we present a novel method for estimating the slowdown programs will experience when co-scheduled on the same computer. We evaluate the method by comparing the predictions made with real slowdown data and the often used memory bandwidth based method. This study show that a scheduler relying on slowdown based categorization makes fewer incorrect co-scheduling choices and the negative impact on program execution times is less than when using a bandwidth based categorization method.
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