Bubble-Up: increasing utilization in modern warehouse scale computers via sensible co-locations
Proceedings of the 44th Annual IEEE/ACM International Symposium on Microarchitecture
Virtualized environments in cloud can have superlinear speedup
Proceedings of the Fifth Balkan Conference in Informatics
An empirical model for predicting cross-core performance interference on multicore processors
PACT '13 Proceedings of the 22nd international conference on Parallel architectures and compilation techniques
USENIX ATC'13 Proceedings of the 2013 USENIX conference on Annual Technical Conference
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QoS criteria in cloud computing require guarantees about application runtimes, even if CMP servers are shared among multiple parallel or serial applications. Performance of computation-intensive application depends significantly on memory performance and especially cache performance. Recent trends are toward configurable caches that can dynamically partition the cache among cores. Then, proper cache partitioning should consider the applications' different cache needs and their sensitivity towards insufficient cache space. We present a simple, yet effective and therefore practically feasible black-box model that describes application performance in dependence on allocated cache size and only needs three descriptive parameters. Learning these parameters can therefore be done with very few sample points. We demonstrate with the SPEC benchmarks that the model adequately describes application behavior and that curve fitting can accomplish very high accuracy, with mean relative error of 2.8% and maximum relative error of 17%.