To distribute or not to distribute: The question of load balancing for performance or energy

Heterogeneous systems are nowadays a common choice in the path to Exascale. Through the use of accelerators they offer outstanding energy efficiency. The programming of these devices employs the host-device model, which is suboptimal as CPU remains idle during kernel executions, but still consumes energy. Making the CPU contribute computin effort might improve the performance and energy consumption of the system. This paper analyses the advantages of this approach and sets the limits of when its beneficial. The claims are supported by a set of models that determine how to share a single data-parallel task between the CPU and the accelerator for optimum performance, energy consumption or efficiency. Interestingly, the models show that optimising performance does not always mean optimum energy or efficiency as well. The paper experimentally validates the models, which represent an invaluable tool for programmers when faced with the dilemma of whether to distribute their workload in these systems.This work has been supported by the University of Cantabria (CVE-2014-18166), the Spanish Science and Technology Commission (TIN2016-76635-C2-2-R), the European Research Council (G.A. No 321253) and the European HiPEAC Network of Excellence. The Mont-Blanc project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 671697.Peer ReviewedPostprint (author's final draft

Whether ideal free or not, predatory mites distribute so as to maximize reproduction

Ideal free distribution (IFD) models predict that animals distribute themselves such that no individual can increase its fitness by moving to another patch. Many empirical tests assume that the interference among animals is independent of density and do not quantify the effects of density on fitness traits. Using two species of predatory mites, we measured oviposition as a function of conspecific density. Subsequently, we used these functions to calculate expected distributions on two connected patches. We performed an experimental test of the distributions of mites on two such connected patches, among which one had a food accessibility rate that was twice as high as on the other. For one of the two species, Iphiseius degenerans, the distribution matched the expected distribution. The distribution also coincided with the ratio of food accessibility. The other species, Neoseiulus cucumeris, distributed itself differently than expected. However, the oviposition rates of both species did not differ significantly from the expected oviposition rates based on experiments on single patches. This suggests that the oviposition rate of N. cucumeris was not negatively affected by the observed distribution, despite the fact that N. cucumeris did not match the predicted distributions. Thus, the distribution of one mite species, I. degenerans, was in agreement with IFD theory, whereas for the other mite species, N. cucumeris, unknown factors may have influenced the distribution of the mites. We conclude that density-dependent fitness traits provide essential information for explaining animal distributions

Truth Discovery in Crowdsourced Detection of Spatial Events

ACKNOWLEDGMENTS This research is based upon work supported in part by the US ARL and UK Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the US ARL, the US Government, the UK Ministry of Defense or the UK Government. The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.Peer reviewedPostprin

Adaptive Non-uniform Compressive Sampling for Time-varying Signals

In this paper, adaptive non-uniform compressive sampling (ANCS) of time-varying signals, which are sparse in a proper basis, is introduced. ANCS employs the measurements of previous time steps to distribute the sensing energy among coefficients more intelligently. To this aim, a Bayesian inference method is proposed that does not require any prior knowledge of importance levels of coefficients or sparsity of the signal. Our numerical simulations show that ANCS is able to achieve the desired non-uniform recovery of the signal. Moreover, if the signal is sparse in canonical basis, ANCS can reduce the number of required measurements significantly.Comment: 6 pages, 8 figures, Conference on Information Sciences and Systems (CISS 2017) Baltimore, Marylan

Distributed Gaussian Processes

To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or variational parameters. The key idea is to recursively distribute computations to independent computational units and, subsequently, recombine them to form an overall result. Efficient closed-form inference allows for straightforward parallelisation and distributed computations with a small memory footprint. The rBCM is independent of the computational graph and can be used on heterogeneous computing infrastructures, ranging from laptops to clusters. With sufficient computing resources our distributed GP model can handle arbitrarily large data sets.Comment: 10 pages, 5 figures. Appears in Proceedings of ICML 201