A customizable multi-agent system for distributed data mining

We present a general Multi-Agent System framework for distributed data mining based on a Peer-to-Peer model. Agent protocols are implemented through message-based asynchronous communication. The framework adopts a dynamic load balancing policy that is particularly suitable for irregular search algorithms. A modular design allows a separation of the general-purpose system protocols and software components from the specific data mining algorithm. The experimental evaluation has been carried out on a parallel frequent subgraph mining algorithm, which has shown good scalability performances

Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections

Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192x192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz clock rates. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table

Design and evaluation of dynamic policy-based flow redirection for multihomed mobile netwotks

This paper presents the design, implementation and evaluation of a solution for dynamic redirection of traffic flows for multihomed mobile networks. The solution was developed for a mobile user that disposes of a Personal Area Network (PAN) with a Personal Mobile Router (PMR), in order to achieve Always Best Connected(ABC) service by distributing flows belonging to different applications among the most appropriate access networks. Designed in a modular way for a NEMO based mobility and multihoming support, the proposed flow redirection solution can be easily coupled with and controlled by dynamic traffic policies that come from advanced network intelligence, according to the currently available network resources and user and application requirements. A prototype implementation was validated and assessed on a testbed as proof-of-concept

Scaling of a large-scale simulation of synchronous slow-wave and asynchronous awake-like activity of a cortical model with long-range interconnections

Cortical synapse organization supports a range of dynamic states on multiple spatial and temporal scales, from synchronous slow wave activity (SWA), characteristic of deep sleep or anesthesia, to fluctuating, asynchronous activity during wakefulness (AW). Such dynamic diversity poses a challenge for producing efficient large-scale simulations that embody realistic metaphors of short- and long-range synaptic connectivity. In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates, which implies a wide variety of loads of local computation and communication. A balanced evaluation of simulation performance and robustness should therefore include tests of a variety of cortical dynamic states. Here, we demonstrate performance scaling of our proprietary Distributed and Plastic Spiking Neural Networks (DPSNN) simulation engine in both SWA and AW for bidimensional grids of neural populations, which reflects the modular organization of the cortex. We explored networks up to 192x192 modules, each composed of 1250 integrate-and-fire neurons with spike-frequency adaptation, and exponentially decaying inter-modular synaptic connectivity with varying spatial decay constant. For the largest networks the total number of synapses was over 70 billion. The execution platform included up to 64 dual-socket nodes, each socket mounting 8 Intel Xeon Haswell processor cores @ 2.40GHz clock rates. Network initialization time, memory usage, and execution time showed good scaling performances from 1 to 1024 processes, implemented using the standard Message Passing Interface (MPI) protocol. We achieved simulation speeds of between 2.3x10^9 and 4.1x10^9 synaptic events per second for both cortical states in the explored range of inter-modular interconnections.Comment: 22 pages, 9 figures, 4 table

Statistical Power Supply Dynamic Noise Prediction in Hierarchical Power Grid and Package Networks

One of the most crucial high performance systems-on-chip design challenge is to front their power supply noise sufferance due to high frequencies, huge number of functional blocks and technology scaling down. Marking a difference from traditional post physical-design static voltage drop analysis, /a priori dynamic voltage drop/evaluation is the focus of this work. It takes into account transient currents and on-chip and package /RLC/ parasitics while exploring the power grid design solution space: Design countermeasures can be thus early defined and long post physical-design verification cycles can be shortened. As shown by an extensive set of results, a carefully extracted and modular grid library assures realistic evaluation of parasitics impact on noise and facilitates the power network construction; furthermore statistical analysis guarantees a correct current envelope evaluation and Spice simulations endorse reliable result

Analytical study of modular cutting tools dynamic properties

The paper studies a comparative evaluation method for the modular cutting tools dynamic properties under the cutting forces loading with the help of the finite element numerical method. The method allows forecasting the modular cutting tool dynamic properties with identification of the confidence bounds for its exploitation in compliance with its intended use and reference operating conditions. The conducted modeling describes the modular cutting tool structure as a ranked set of structural components, such as a frame, a cassette, a cutting insert, etc., oriented towards a certain direction with some surfaces being contiguous and thus making contact interactions. The analytical model is represented by a multi-mass system in the form of elastic rods connected at elastic and damping joints. The research examined different tool structural component layout options, including those equipped/not equipped with a shim, having a radial/tangential (face or peripheral) location type of cutting components. The dynamic compliance values and vibrational modes at the natural frequencies were calculated for all structural component layout options. The calculation results showed an acceptable level of model convergence with the existing experimental data on the cutting tool condition express diagnostics

Supporting dynamic aspect-oriented features

Aspect-oriented programming techniques extend object-oriented programming with new methods to modularize concerns that otherwise would be non-modular. For example, logging concerns are typically scattered across a system but using aspect-oriented techniques they can be localized into a single high-level module. These techniques typically take modular high-level code and statically transform it into non-modular intermediate code. The contribution of this work is the design and implementation of a flexible and dynamic intermediate-language (IL) model. The main motivation for the design of this IL model is to support a variety of dynamic aspect-oriented language constructs that are proposed in recent literature such as CaeserJ\u27s deploy, history-based pointcuts, and control flow constructs. Our IL model provides a higher level of abstraction compared to traditional object-oriented ILs as a compilation target for such constructs, which makes it easier to provide efficient implementations of these constructs. We demonstrate these benefits by providing an industrial strength implementation for our IL model, by showing translation strategies from dynamic source-level constructs to our improved IL, and by analyzing the performance of the resulting IL code. Our evaluation using the SPEC JVM98 and Java Grande benchmarks shows that the overhead of supporting a dynamic deployment model can be reduced to as little as ~1.5%, when compared to the unmodified VM

DIANNE: a modular framework for designing, training and deploying deep neural networks on heterogeneous distributed infrastructure

Deep learning has shown tremendous results on various machine learning tasks, but the nature of the problems being tackled and the size of state-of-the-art deep neural networks often require training and deploying models on distributed infrastructure. DIANNE is a modular framework designed for dynamic (re)distribution of deep learning models and procedures. Besides providing elementary network building blocks as well as various training and evaluation routines, DIANNE focuses on dynamic deployment on heterogeneous distributed infrastructure, abstraction of Internet of Things (loT) sensors, integration with external systems and graphical user interfaces to build and deploy networks, while retaining the performance of similar deep learning frameworks. In this paper the DIANNE framework is proposed as an all-in-one solution for deep learning, enabling data and model parallelism though a modular design, offloading to local compute power, and the ability to abstract between simulation and real environment. (C) 2018 Elsevier Inc. All rights reserved