Scalable Breadth-First Search on a GPU Cluster

On a GPU cluster, the ratio of high computing power to communication bandwidth makes scaling breadth-first search (BFS) on a scale-free graph extremely challenging. By separating high and low out-degree vertices, we present an implementation with scalable computation and a model for scalable communication for BFS and direction-optimized BFS. Our communication model uses global reduction for high-degree vertices, and point-to-point transmission for low-degree vertices. Leveraging the characteristics of degree separation, we reduce the graph size to one third of the conventional edge list representation. With several other optimizations, we observe linear weak scaling as we increase the number of GPUs, and achieve 259.8 GTEPS on a scale-33 Graph500 RMAT graph with 124 GPUs on the latest CORAL early access system.Comment: 12 pages, 13 figures. To appear at IPDPS 201

Structural Robustness of Large Games

This short survey discusses recent findings on the robustness of Nash equilibria of strategic games with many semianonymous players. It describes the notion of structural robustness and its general consequences, as well as implications to particular games, such as ones played on the web and market games.Nash Equilibrium,ex-post Nash, anonymous games, market games, rational expectations, structural robustness, information proofness, web games

Facing Openness with Socio Cognitive Trust and Categories.

Typical solutions for agents assessing trust relies on the circulation of information on the individual level, i.e. reputational images, subjective experiences, statistical analysis, etc. This work presents an alternative approach, inspired to the cognitive heuristics enabling humans to reason at a categorial level. The approach is envisaged as a crucial ability for agents in order to: (1) estimate trustworthiness of unknown trustees based on an ascribed membership to categories; (2) learn a series of emergent relations between trustees observable properties and their effective abilities to fulfill tasks in situated conditions. On such a basis, categorization is provided to recognize signs (Manifesta) through which hidden capabilities (Kripta) can be inferred. Learning is provided to refine reasoning attitudes needed to ascribe tasks to categories. A series of architectures combining categorization abilities, individual experiences and context awareness are evaluated and compared in simulated experiments

New Solution of Abstract Architecture for Control and Coordination Decentralized Systems

This paper contains a new approach that combines the advantages and disadvantages of suppressing hierarchical and heterarchical control architectures, creating a semi-heterarchical (holonic) control architecture. The degree of subordinate unit autonomy changes dynamically, depending on the presence of a system disruption, and its scope allows for a smooth transition from hierarchical to heterarchic control architecture in subordinate units. We have proposed a representation of the dynamic degree of autonomy and its possible application to subordinate units, which are, in our case, one-directional Automated Guided Vehicles (AGVs) and are guided by magnetic tape. In order to achieve such a semi-heterarchic management architecture with a dynamic degree of autonomy, approaches such as smart product, stymergic (indirect) communication, or basic principles of holon approach have been implemented