Erbium: A Deterministic, Concurrent Intermediate Representation for Portable and Scalable Performance

PosterInternational audienceOptimizing compilers and runtime libraries do not shield programmers from the complexity of multi-core hardware; as a result the need for manual, target-specific optimizations increases with every processor generation. High-level languages are being designed to express concurrency and locality without reference to a particular architecture. But compiling such abstractions into efficient code requires a portable, intermediate representation: this is essential for modular composition (separate compilation), for optimization frameworks independent of the source language, and for just-in-time compilation of bytecode languages. This paper introduces Erbium, an intermediate representation for compilers, a low-level language for efficiency programmers, and a lightweight runtime implementation. It relies on a data structure for scalable and deterministic concurrency, called Event Record, exposing the data-level, task and pipeline parallelism suitable to a given target. We provide experimental evidence of the productivity, scalability and efficiency advantages of Erbium, relying on a prototype implementation in GCC 4.3

Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

An overview of existing modeling tools making use of model checking in the analysis of biochemical networks

Model checking is a well-established technique for automaticallyverifying complex systems. Recently, model checkers have appearedin computer tools for the analysis of biochemical (and generegulatory) networks. We survey several such tools to assess thepotential of model checking in computational biology. Next, our overviewfocuses on direct applications of existing model checkers, as well ason algorithms for biochemical network analysis influenced by modelchecking, such as those using binary decision diagrams or Booleansatisfiability solvers. We conclude with advantages and drawbacks ofmodel checking for the analysis of biochemical networks

ModeL4CEP: Graphical domain-specific modeling languages for CEP domains and event patterns

Complex event processing (CEP) is a cutting-edge technology that allows the analysis and correlation of large volumes of data with the aim of detecting complex and meaningful events through the use of event patterns, as well as permitting the inference of valuable knowledge for end users. Despite the great advantages that CEP can bring to expert or intelligent business systems, it poses a substantial challenge to their users, who are business experts but do not have the necessary knowledge and experience using this technology. The main problem these users have to face is precisely hand-writing the code for event pattern definition, which requires them to implement the conditions to be met to detect relevant situations for the domain in question by using a particular event processing language (EPL). In order to respond to this need, in this paper we propose both a graphical domain-specific modeling language (DSML) for facilitating CEP domain definitions by domain experts, and a graphical DSML for event pattern definition by non-technological users. The proposed languages provide high expressiveness and flexibility and are independent of event patterns and actions’ implementation code. This way, domain experts can define the relevant event types and patterns within their business domain, without having to be experts on EPL programming, nor on other complicated computer science technological issues, beyond an understandable and intuitive graphical definition. Furthermore, with these DSMLs, users will also be able to define the actions to be automatically taken once a pattern is detected in the system. Further benefits of these DSMLs are evaluated and discussed in depth in this paper

An FPGA platform for real-time simulation of spiking neuronal networks

In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing. Field programmable gate arrays (FPGAs) can improve flexibility when simple neuronal models are required, obtaining good accuracy, real-time performance, and the possibility to create a hybrid system without any custom hardware, just programming the hardware to achieve the required functionality. In this paper, this possibility is explored presenting a modular and efficient FPGA design of an in silico spiking neural network exploiting the Izhikevich model. The proposed system, prototypically implemented on a Xilinx Virtex 6 device, is able to simulate a fully connected network counting up to 1,440 neurons, in real-time, at a sampling rate of 10 kHz, which is reasonable for small to medium scale extra-cellular closed-loop experiments

Project Final Report Use and Dissemination of Foreground

This document is the final report on use and dissemination of foreground, part of the CONNECT final report. The document provides the lists of: publications, dissemination activities, and exploitable foregroun

Jolie and LEMMA: Model-Driven Engineering and Programming Languages Meet on Microservices

Part 3: Large-Scale Decentalised SystemsInternational audienceIn microservices, Model-Driven Engineering (MDE) has emerged as a powerful methodology for architectural design. Independently, the community of programming languages has investigated new linguistic abstractions for effective microservice development. Here, we present the first preliminary study of how the two approaches can crosspollinate, taking the LEMMA framework and the Jolie programming language as respective representatives. We establish a common ground for comparing the two technologies in terms of metamodels, discuss practical enhancements that can be derived from the comparison, and present some directions for future work that arise from our new viewpoint