Energy efficient speculative threads

Luo, Yangchun; Packirisamy, Venkatesan; Hsu, Wei-Chung; Zhai, Antonia

doi:10.1145/1854273.1854329

Cited by 17 publications

(1 citation statement)

References 38 publications

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“…Most of them have been conducted in the context of native systems. [9,17,18,[21][22][23]26] aim at determining the best thread to core mapping in order to improve thread performance. [26] investigates applications which are composed of both parallel and sequential phases.…”

Section: The Heterogeneity Issue In Amp Cloudsmentioning

confidence: 99%

Dealing with Performance Unpredictability in an Asymmetric Multicore Processor Cloud

Teabe

Wapet

Tchana

et al. 2017

Lecture Notes in Computer Science

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In a Cloud computing data center and especially in a IaaS (Infrastructure as a Service), performance predictability is one of the most important challenges. For a given allocated virtual machine (VM) in one IaaS, a client expects his application to perform identically whatever is the hosting physical server or its resource management strategy. However, performance predictability is very difficult to enforce in a heterogeneous hardware environment where machines do not have identical performance characteristics, and even more difficult when machines are internally heterogeneous as for Asymmetric Multicore Processor machines. In this paper, we introduce a VM scheduler extension which takes into account hardware performance heterogeneity of Asymmetric Multicore Processor machines in the cloud. Based on our analysis of the problem, we designed and implemented two solutions: the first weights CPU allocations according to core performance, while the second adapts CPU allocations to reach a given instruction execution rate (Ips) regardless the core types. We demonstrate that such scheduler extensions can enforce predictability with a negligible overhead on application performance.

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