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Improving SMT performance: an application of genetic algorithms to configure resizable caches

Josefa Diaz, J. Ignacio Hidalgo, Francisco Fernandez, Oscar Garnica, Sonia Lopez
University of Extremadura, Merida, Spain
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, GECCO ’09

@conference{diaz2009improving,

   title={Improving SMT performance: an application of genetic algorithms to configure resizable caches},

   author={D{‘i}az, J. and Hidalgo, J.I. and Fern{‘a}ndez, F. and Garnica, O. and L{‘o}pez, S.},

   booktitle={Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers},

   pages={2029–2034},

   year={2009},

   organization={ACM}

}

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Simultaneous Multithreading (SMT) is a technology aimed at improving the throughput of the processor core by applying Instruction Level Parallelism (ILP) and Thread Level Parallelism (TLP). Nevertheless a good control strategy is required when resources are shared among different threads, so that throughput is optimized. We study the application of evolutionary algorithms to improve the allocation of configurations on the cache hierarchy over a Simultaneous Multithreading (SMT) processor. In this way, resizable caches have demonstrated their efficiency by adapting their configuration according to workload settings, at runtime. More-over, some methodologies and a number of techniques, such as dynamic resource allocation, have previously been developed to optimize the cache hit behavior, trying to improve global SMT performance. In this paper we propose the use of a Genetic Algorithm (GA) to optimize dynamically reconfigurable cache designs. Given that different workloads feature different characteristics and needs, we apply a Genetic Algorithm (GA) for cache designing, in order to obtain a better dynamic configuration that increases the number of instructions per cycle (IPC). The obtained results show the feasibility of the approach and the potential of GAs for SMT optimization.
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