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Energy-efficiency has become a major challenge in modern computer systems. To address this challenge, candidate systems increasingly integrate heterogeneous cores in order to satisfy diverse computation requirements by selecting cores with suitable features. In particular, single-ISA heterogeneous multicore processors such as ARM big.LITTLE have become very attractive since they offer good opportunities in terms of performance and power consumption trade-off. While existing works already showed that this feature can improve system energy-efficiency, further gains are possible by generalizing the principle to higher levels of heterogeneity. The present paper aims to explore these gains by considering single-ISA heterogeneous multicore architectures including three different types of cores. For this purpose, we use the Samsung Exynos Octa 5422 chip as baseline architecture. Then, we model and evaluate Cortex A7, A9, and A15 cores using the gem5 simulation framework coupled to McPAT for power estimation. We demonstrate that varying the level of heterogeneity as well as the different core ratio can lead to up to 2.3x gains in energy efficiency and up to 1.5x in performance. This study further provides insights on the impact of workload nature on performance/energy trade-off and draws recommendations concerning suitable architecture configurations. This contributes in fine to guide future research towards dynamically reconfigurable HSAs in which some cores/clusters can be disabled momentarily so as to optimize certain metrics such as energy efficiency. This is of particular interest when dealing with quality-tunable algorithms in which accuracy can be then traded for compute effort, thereby enabling to use only those cores that provide the best energy-efficiency for the chosen algorithm., Comment: 11 pages, 6 figure, 2 tables

Resource allocation is today an integral part of cloud infrastructures management to efficiently exploit resources. Cloud infrastructures centers generally use custom built heuristics to define the resource allocations. It is an immediate requirement for the management tools of these centers to have a fast yet reasonably accurate simulation and evaluation platform to define the resource allocation for cloud applications. This work proposes a framework allowing users to easily specify mappings for cloud applications described in the AMALTHEA format used in the context of the DreamCloud European project and to assess the quality for these mappings. The two quality metrics provided by the framework are execution time and energy consumption., Comment: 2nd International Workshop on Dynamic Resource Allocation and Management in Embedded, High Performance and Cloud Computing DREAMCloud 2016 (arXiv:cs/1601.04675)

ISBN : 9973-43-072-7; International audience