Performance Characterisation of Intra-Cluster Collective Communications

International audienceAlthough recent works try to improve collective communication in grid systems by separating intra and inter-cluster communication, the optimisation of communications focus only on inter-cluster communications. We believe, instead, that the overall performance of the application may be improved if intra-cluster collective communications performance is known in advance. Hence, it is important to have an accurate model of the intra-cluster collective communications, which provides the necessary evidences to tune and to predict their performance correctly. In this paper we present our experience on modelling such communication strategies. We describe and compare different implementation strategies with their communication models, evaluating the models' accuracy and describing the practical challenges that can be found when modelling collective communications

Embedding Stand-Alone, ‘Local Buzz’ and ‘Global Pipeline’ Firms; a Plea for a Less Traditional Regional Innovation Policy

This paper deals with the policy implications of a research project based on a non-traditional approach to innovation measurement in a Dutch region. This region is characterized by an ‘innovation paradox’, as it lodges large numbers of ‘creative’ people while it also underperforms in traditional innovation measurements. A survey among experts regarding regional innovation yields large numbers of innovative firms in a wide range of industries, which in traditional studies would partly go unnoticed. Further data analysis reveals that innovation in the region has no clear face in terms of firms and sectors. This is due to the embroynic state of clustering in different subsectors, the mostly social and informal nature of network ties between entrepreneurs in the region, the international level at which much innovation-oriented networking takes place, and the lack of connectivity between the latter networks and local informal networks and the embryonic clusters. In terms of their innovation profile, firms in the region are strong in creative, non-technical and combined forms of innovation. So, dynamic capabilities especially show up ‘downstream’, connecting novelty with clients and markets, and translating this into change management and new practices. Next, we found that firms strategically engage in innovation ventures, in the three ways that were explained before by Bathelt et al. (2004), i.e. seeking and combining international knowledge with one’s own (constructing ‘global pipelines’), strengthening regional ties, identity, contact and linkages (‘local buzz’), and relying on one’s own resources for innovation (‘stand alone’ strategy). One challenge for policy is to exploit these three strategies of firms. Such can be done in three ways. One is to use the abundant social capital in the region, with a view to strengthening the economic relevance of existing local networks by constructing and extending ‘global pipelines’. The second is to display leadership and formulate a ‘community argument’ for innovation (dealing with the following sub questions: why must I innovate, why must I interact in networks and clusters, and why should I do so at different spatial scales?), thus strategically reorienting the available ‘local buzz’ and enhancing its economic relevance. Together, these two proposals serve the purpose of stimulating knowledge flows ‘outside-in’ and ‘inside-out’ (cf. Wolfe & Gertler 2005). The third is to correct for the policy myopia on cluster and network initiatives. The price we pay for the Porterian approach to clustering (cf. Martin & Sunley 2003; Hospers 2005) is that a significant number of firms in the region under review that individually engage in innovation processes, are not part of ‘global pipeline’ and ‘local buzz’ processes. Hence, they do not enrich nor benefit from these processes, and may thus relatively easy leave the region. Finally, they may be less effective in innovation, in terms of speed and the market fit of new products and processes. So, both from a regional and firm-level perspective, stand-alone firms merit attention.

Bio-mimetic Spiking Neural Networks for unsupervised clustering of spatio-temporal data

Spiking neural networks aspire to mimic the brain more closely than traditional artificial neural networks. They are characterised by a spike-like activation function inspired by the shape of an action potential in biological neurons. Spiking networks remain a niche area of research, perform worse than the traditional artificial networks, and their real-world applications are limited. We hypothesised that neuroscience-inspired spiking neural networks with spike-timing-dependent plasticity demonstrate useful learning capabilities. Our objective was to identify features which play a vital role in information processing in the brain but are not commonly used in artificial networks, implement them in spiking networks without copying constraints that apply to living organisms, and to characterise their effect on data processing. The networks we created are not brain models; our approach can be labelled as artificial life. We performed a literature review and selected features such as local weight updates, neuronal sub-types, modularity, homeostasis and structural plasticity. We used the review as a guide for developing the consecutive iterations of the network, and eventually a whole evolutionary developmental system. We analysed the model’s performance on clustering of spatio-temporal data. Our results show that combining evolution and unsupervised learning leads to a faster convergence on the optimal solutions, better stability of fit solutions than each approach separately. The choice of fitness definition affects the network’s performance on fitness-related and unrelated tasks. We found that neuron type-specific weight homeostasis can be used to stabilise the networks, thus enabling longer training. We also demonstrated that networks with a rudimentary architecture can evolve developmental rules which improve their fitness. This interdisciplinary work provides contributions to three fields: it proposes novel artificial intelligence approaches, tests the possible role of the selected biological phenomena in information processing in the brain, and explores the evolution of learning in an artificial life system

A survey of self organisation in future cellular networks

This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

A Framework for Adaptive Collective Communications on Heterogeneous Hierarchical Networks

Extended version of the IPDPS 2006 paperToday, due to the wide variety of existing parallel systems consisting on collections of heterogeneous machines, it is very difficult for a user to solve a target problem by using a single algorithm or to write portable programs that perform well on multiple computational supports. The inherent heterogeneity and the diversity of networks of such environments represent a great challenge to model the communications for high performance computing applications. Our objective within this work is to propose a generic framework based on communication models and adaptive techniques for dealing with prediction of communication performances on cluster-based hierarchical platforms. Toward this goal, we introduce the concept of polyalgorithmic model of communications, which correspond to selection of the most adapted communication algorithms and scheduling strategies, giving the characteristics of the hardware resources of the target parallel system. We apply this methodology on collective communication operations and show that the framework provides significant performances while determining the best algorithm depending on the problem and architecture parameters

Industrial clusters and economic integration : theoretic concepts and an application to the European Metropolitan Region Nuremberg

"Economic integration typically goes along with disintegration of production through outsourcing and offshoring (Feenstra 1998). As horizontal and vertical links between firms become more and more pronounced, value chains within regions are increasingly organized by production and innovation clusters. On the basis of a literature overview, we argue that in a world of economic integration clusters can be expected to play a prominent role. Therefore clusters can also be seen as a key element in the European Metropolitan Region concept. Within such an economic space, localisation economies according to the 'Marshallian trinity' (knowledge spillovers, input sharing and labour market pooling (Rosenthal/Strange 2003)) can be realized. The paper builds on a comprehensive company survey for the core of the European Metropolitan Region Nuremberg that includes customer-supplier relationships and various forms of cooperation. As indicated by numerous empirical studies, the characteristics of clusters differ substantially. In order to overcome the fuzziness of the concept we suggest a bottom-up methodology of cluster identification using a set of qualitative and quantitative indicators. Given that many kinds of barriers to interregional and international trade are becoming less and less important and transport cost are falling, modern production clusters tend to have a higher geographical extension than traditional ones. We therefore raise the question of whether clustering is relevant for economic integration on the regional, national and supra-national level." (Author's abstract, IAB-Doku) ((en))Stadtregion, regionales Cluster, Standort, Industrieregion, Regionalökonomie, zwischenbetriebliche Kooperation, Zulieferer, Wirtschaftsstruktur, regionales Netzwerk, Nürnberg, Oberfranken, Mittelfranken, Franken, Bayern

Technoligical Life Cycles Regional Clusters Facing Disruption

The phenomenon of technological life cycles is argued to be of great importance in the development of regional clusters. New 'disruptive' technologies may initiate the emergence of new regional industrial clusters and/or create new opportunities for further development of existing ones. However, they may also result in stagnation and decline of the latter. The term disruptive refers to such significant changes in the basic technologies that may change the industrial landscape, even in the shorter run. The paper examines the key features of a regional cluster, where the economic development patterns are quite closely related to the emergence of new key technologies.Technological life cycles, regional clusters, communication technology

Optimisation for Optical Data Centre Switching and Networking with Artificial Intelligence

Cloud and cluster computing platforms have become standard across almost every domain of business, and their scale quickly approaches $\mathbf{O}(10^6)$ servers in a single warehouse. However, the tier-based opto-electronically packet switched network infrastructure that is standard across these systems gives way to several scalability bottlenecks including resource fragmentation and high energy requirements. Experimental results show that optical circuit switched networks pose a promising alternative that could avoid these. However, optimality challenges are encountered at realistic commercial scales. Where exhaustive optimisation techniques are not applicable for problems at the scale of Cloud-scale computer networks, and expert-designed heuristics are performance-limited and typically biased in their design, artificial intelligence can discover more scalable and better performing optimisation strategies. This thesis demonstrates these benefits through experimental and theoretical work spanning all of component, system and commercial optimisation problems which stand in the way of practical Cloud-scale computer network systems. Firstly, optical components are optimised to gate in $\approx 500 ps$ and are demonstrated in a proof-of-concept switching architecture for optical data centres with better wavelength and component scalability than previous demonstrations. Secondly, network-aware resource allocation schemes for optically composable data centres are learnt end-to-end with deep reinforcement learning and graph neural networks, where $3\times$ less networking resources are required to achieve the same resource efficiency compared to conventional methods. Finally, a deep reinforcement learning based method for optimising PID-control parameters is presented which generates tailored parameters for unseen devices in $\mathbf{O}(10^{-3}) s$. This method is demonstrated on a market leading optical switching product based on piezoelectric actuation, where switching speed is improved $>20\%$ with no compromise to optical loss and the manufacturing yield of actuators is improved. This method was licensed to and integrated within the manufacturing pipeline of this company. As such, crucial public and private infrastructure utilising these products will benefit from this work

Predictive analysis and optimisation of pipelined wavefront applications using reusable analytic models

Pipelined wavefront computations are an ubiquitous class of high performance parallel algorithms used for the solution of many scientific and engineering applications. In order to aid the design and optimisation of these applications, and to ensure that during procurement platforms are chosen best suited to these codes, there has been considerable research in analysing and evaluating their operational performance. Wavefront codes exhibit complex computation, communication, synchronisation patterns, and as a result there exist a large variety of such codes and possible optimisations. The problem is compounded by each new generation of high performance computing system, which has often introduced a previously unexplored architectural trait, requiring previous performance models to be rewritten and reevaluated. In this thesis, we address the performance modelling and optimisation of this class of application, as a whole. This differs from previous studies in which bespoke models are applied to specific applications. The analytic performance models are generalised and reusable, and we demonstrate their application to the predictive analysis and optimisation of pipelined wavefront computations running on modern high performance computing systems. The performance model is based on the LogGP parameterisation, and uses a small number of input parameters to specify the particular behaviour of most wavefront codes. The new parameters and model equations capture the key structural and behavioural differences among different wavefront application codes, providing a succinct summary of the operations for each application and insights into alternative wavefront application design. The models are applied to three industry-strength wavefront codes and are validated on several systems including a Cray XT3/XT4 and an InfiniBand commodity cluster. Model predictions show high quantitative accuracy (less than 20% error) for all high performance configurations and excellent qualitative accuracy. The thesis presents applications, projections and insights for optimisations using the model, which show the utility of reusable analytic models for performance engineering of high performance computing codes. In particular, we demonstrate the use of the model for: (1) evaluating application configuration and resulting performance; (2) evaluating hardware platform issues including platform sizing, configuration; (3) exploring hardware platform design alternatives and system procurement and, (4) considering possible code and algorithmic optimisations