Abstract

Heterogeneous multicore systems have gained momentum, specially for embedded applications, thanks to the performance and energy consumption trade-offs provided by inorder and out-of-order cores. Micro-architectural simulation models the behavior of pipeline structures and caches with configurable parameters. This level of abstraction is well known for being flexible enough to quickly evaluate the performance of new hardware implementations, such as future heterogeneous multicore platforms. However, currently, there is no open-source micro-architectural simulator supporting both in-order and out-of-order ARM cores. This article describes the implementation and accuracy evaluation of a micro-architectural simulator of Cortex-A cores, supporting in-order and out-of-order pipelines and based on the open-source gem5 simulator. We explain how to simulate CortexA8 and Cortex-A9 cores in gem5, and compare the execution time of ten benchmarks with real hardware. Both models, with average absolute errors of only 7 %, are more accurate than similar microarchitectural simulators, which show average absolute errors greater than 15 %.

Abstract

In this paper four energy scenarios for Westy-European passenger transport are developed. To start with, the present transport system as a base-line case is described and analysed. Next, for each scenario we sketch how the passenger transport system may look like in terms of the use of various existing and future transport technologies and the corresponding model split. Based on this information, expected energy consumption features of the various transport modes are described, data on the present fuel supply and electricity generation system are offered, and estimations of the future energy system are given. Then, the energy consumption and CO2 emissions associated with the future passenger transport systems are stressed and these impacts are compared with the current system. The conclusion is that a large-scale reduction of CO2 emissions is possible in several ways. Each way will cause many problems, since drastic policy measures will have to be introduced, which may affect economic growth and the lifestyles of individuals.

Abstract

We investigate the hypothesis that long-term memory in populations of agents can lead to counterproductive emergent properties at the system level. Our investigation is framed in the context of a discrete, one-dimensional road-traffic congestion model: we investigate the influence of simple cognition in a population of rational commuter agents that use memory to optimise their departure time, taking into account congestion delays on previous trips. Our results differ from the well-known minority game in that crowded slots do not carry any explicit penalty. We use Markov chain analysis to uncover fundamental properties of this model and then use the gained insight as a benchmark. Then, using Monte Carlo simulations, we study two scenarios: one in which "myopic” agents only remember the outcome (delay) of their latest commute, and one in which their memory is practically infinite. We show that there exists a trade-off, whereby myopic memory reduces congestion but increases uncertainty, while infinite memory does the opposite. We evaluate the performance against the optimal distribution of departure times (i.e. where both delay and uncertainty are minimised simultaneously). This optimal but unstable distribution is identified using a genetic algorithm. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.