In this video from the 2014 HPC User Forum in Seattle, Manuel Vigil from Los Alamos National Laboratory presents: Update on Trinity System Procurement and Plans.
Learn more: http://insidehpc.com/video-gallery-hpc-user-forum-2014-seattle/
TemporalEMF: A Temporal Metamodeling Frameworkabgolla
Existing modeling tools provide direct access to the most current version of a model but very limited support to inspect the model state in the past. This typically requires looking for a model version (usually stored in some kind of external versioning system like Git) roughly corresponding to the desired period and using it to manually retrieve the required data. This approximate answer is not enough in scenarios that require a more precise and immediate response to temporal queries like complex collaborative co-engineering processes or runtime models.
In this paper, we reuse well-known concepts from temporal languages to propose a temporal metamodeling framework, called TemporalEMF, that adds native temporal support for models. In our framework, models are automatically treated as temporal models and can be subjected to temporal queries to retrieve the model contents at different points in time. We have built our framework on top of the Eclipse Modeling Framework (EMF). Behind the scenes, the history of a model is transparently stored in a NoSQL database. We evaluate the resulting TemporalEMF framework with an Industry 4.0 case study about a production system simulator. The results show good scalability for storing and accessing temporal models without requiring changes to the syntax and semantics of the simulator.
Static Memory Management for Efficient Mobile Sensing ApplicationsFarley Lai
Memory management is a crucial aspect of mobile sensing applications that must process high-rate data streams in an energy-efficient manner. Our work is done in the context of synchronous data-flow models in which applications are implemented as a graph of components that exchange data at fixed and known rates over FIFO channels. In this paper, we show that it is feasible to leverage the restricted semantics of synchronous data-flow models to optimize memory management. Our memory optimization approach includes two components: (1) We use abstract interpretation to analyze the complete memory behavior of a mobile sensing application and identify data sharing opportunities across components according to the live ranges of exchanged samples. Experiments indicate that the static analysis is precise for a majority of considered stream applications whose control logic does not depend on input data. (2) We propose novel heuristics for memory allocation that leverage the graph structure of applications to optimize data exchanges between application components to achieve not only significantly lower memory footprints but also increased stream processing throughput.
In this video from the 2014 HPC User Forum in Seattle, Manuel Vigil from Los Alamos National Laboratory presents: Update on Trinity System Procurement and Plans.
Learn more: http://insidehpc.com/video-gallery-hpc-user-forum-2014-seattle/
TemporalEMF: A Temporal Metamodeling Frameworkabgolla
Existing modeling tools provide direct access to the most current version of a model but very limited support to inspect the model state in the past. This typically requires looking for a model version (usually stored in some kind of external versioning system like Git) roughly corresponding to the desired period and using it to manually retrieve the required data. This approximate answer is not enough in scenarios that require a more precise and immediate response to temporal queries like complex collaborative co-engineering processes or runtime models.
In this paper, we reuse well-known concepts from temporal languages to propose a temporal metamodeling framework, called TemporalEMF, that adds native temporal support for models. In our framework, models are automatically treated as temporal models and can be subjected to temporal queries to retrieve the model contents at different points in time. We have built our framework on top of the Eclipse Modeling Framework (EMF). Behind the scenes, the history of a model is transparently stored in a NoSQL database. We evaluate the resulting TemporalEMF framework with an Industry 4.0 case study about a production system simulator. The results show good scalability for storing and accessing temporal models without requiring changes to the syntax and semantics of the simulator.
Static Memory Management for Efficient Mobile Sensing ApplicationsFarley Lai
Memory management is a crucial aspect of mobile sensing applications that must process high-rate data streams in an energy-efficient manner. Our work is done in the context of synchronous data-flow models in which applications are implemented as a graph of components that exchange data at fixed and known rates over FIFO channels. In this paper, we show that it is feasible to leverage the restricted semantics of synchronous data-flow models to optimize memory management. Our memory optimization approach includes two components: (1) We use abstract interpretation to analyze the complete memory behavior of a mobile sensing application and identify data sharing opportunities across components according to the live ranges of exchanged samples. Experiments indicate that the static analysis is precise for a majority of considered stream applications whose control logic does not depend on input data. (2) We propose novel heuristics for memory allocation that leverage the graph structure of applications to optimize data exchanges between application components to achieve not only significantly lower memory footprints but also increased stream processing throughput.
High Performance Stream Processing and OptimizationsFarley Lai
Building scalable systems that process streams of data requires developers to take advantage of the parallelism capabilities offered by today's computer architectures. Existing imperative programming languages provide programmers low-level primitives such as threads, locks, and semaphores. However, programs developed using these primitives tend to be plagued by race conditions and deadlocks, which make it hard to understand and debug non-deterministic behaviors. Acknowledging these limitations, some programming language extensions and libraries (e.g., OpenMPI, OpenMP have been proposed to simplify programming parallel programs. Nevertheless, all these options still burden the programmer with annotating parallelism and specifying data sharing attributes to ensure data consistency.
In recent years, dataflows have attracted significant attention as a model for building highly parallel stream processing applications. According to this model, an application is defined as a graph of processing elements that are connected by communication channels. The processing elements may execute in parallel as long as they have sufficient data to process. A key feature of the dataflow model is that it explicitly capture parallelism and data dependencies between processing elements.
Even though the dataflows provide a simple computational model, using this model to build scalable systems is challenging as naive implementations introduce unexpected runtime scheduling overhead, consume significant memory resources, and are not energy efficient. Consequently, our goal is to develop compiler optimizations and efficient runtime environments for scalable dataflow systems. In the following, we will go through the model of computation, memory optimizations, energy efficiency and stream processing at the scale of cloud computing.
CIM to Modelica Factory - Automated Equation-Based Cyber-Physical Power Syste...Luigi Vanfretti
The Common Information Model (CIM) is described using the Unified Modeling Language (UML). UML can also describe data model of cyber-physical power system components and networks. However, there are several difficulties to transform the data model into a strictly defined mathematical model. A strictly defined mathematical model is one for which all-differential algebraic and discrete model equations are explicitly defined [1] (i.e. the equations are written in human readable form). This is known as equation-based modelling [2], and it is utilized in many areas such as the automotive and aerospace industry [3].
The automated generation of an unambiguous equation-based model would allow performing time-domain simulations of cyber-physical power systems [4] and the assessment of textual requirements, which could be defined from the UML model directly [5]. This flexibility would allow adopting model-based systems engineering practices within the power industry, such as those used in process control.
For the implementation of models in an equation-based language, the Modelica language [6] is the one of the best choices because it follows the Object Oriented Programming (OOP) notation, with a close relation with UML. Furthermore, the ModelicaML [5], an extended subset of the OMG Unified Modeling Language, enables integrated modelling and simulation of system requirements and design. Combining CIM, ModelicaML and Modelica models of cyber-physical power system components it is possible to automatically generate unambiguous mathematical models that can be used for simulation and requirements verification [7].
This CIM to Modelica Factory talk explores this possibility.
One of the main challenges that we face with power systems models defined using the Modelica language is the initialization of the dynamic states (in equilibrium condition) of the components within a model [8, 9]. However, objects and components modelled in CIM standard contain attributes for storing a power flow solution.
The purpose of the work described in this presentation is to develop a software tool capable to transform a CIM object model into a Modelica model that can be directly simulated using different Modelica engines. To this aim, we start from the CIM/UML representation of power system components and models, and exploit the ModelicaML profile to achieve a proper code representation of the power system in Modelica code. To confront issues with dynamic initialization, the power flow solution from CIM is linked to the Modelica component models and utilized within the initialization algorithms of the simulation engines. The result is a software tool that allows performing time domain simulations directly from a CIM/UML structure, while maintaining consistency in the resulting mathematical model within different simulation engines.
Towards CIM-Compliant Model-Based Cyber-Physical Power System Design and Simu...Luigi Vanfretti
Compliance with grid data exchange standards (i.e. CIM) can allow for sustainable software development in power systems if open and equation-based modeling languages and simulation standards are exploited . Together with my PhD student Francisco José Gómez López, we will be @RT-2014 presenting our vision and recent work carried out together with Svein Olsen: "Towards CIM-Compliant Model-Based Cyber-Physical Power System Design and Simulation using Modelica".
DE-CPS 2017 The INTO-CPS Cyber-Physical System Profile Alessandra BagnatoAlessandra Bagnato
The Modelio SysML/INTO-CPS module include an INTO-CPS/SysML profile which is organized around the following logical groups: block, type, instance, library and diagram. Only the block group is presented in this paper. The next two diagrams depict the block group. INTO-CPS profile specializes SysML Block concept into one sub-concept named “Component”, with is also specializes into four sub-concepts respectively named “System”, “Subsystem”, “Cyber”, and “Physical”.
Modular Mathematical Modelling of Biological SystemsDaniele Gianni
Presentation at the 2nd International Workshop on Model-driven Approaches for Simulation Engineering
(held within the SCS/IEEE Symposium on Theory of Modeling and Simulation part of SpringSim 2012)
Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
Wireless body sensor networks (WBSN) are a particular type of wireless sensor networks (WSN)
that are becoming an important topic in the technological research community. Advances in the
reduction of the power consumption and cost of these networks have led to solutions mature enough
for their use in a broad range of applications such as sportsman or health monitoring.
The development of those applications has been stimulated by the finalization of the IEEE 802.15.4
standard, which defines the medium access control (MAC) and physical layer (PHY) for low-rate
wireless personal area networks (LR-WPAN). One of the MAC schemes proposed is slotted Carrier
Sense Multiple Access with Collision Avoidance (CSMA/CA). This project analyzes the performance of
this MAC, based on a state-of-the-art analytical model for a star topology, which captures the behavior
of the MAC using two Markov chain models; the per-node state model and the channel state model.
More importantly, we extend this model to include acknowledged traffic. The impact of including
acknowledgments is evaluated in terms of energy consumption, throughput and latency.
The performance predicted by the analytical model has been extensively verified with simulations
using the ns-2 IEEE 802.15.4 contributed module. Throughput, energy consumption and latency
analysis is performed. Additionally, we have simulated a statistical channel model describing the radio
channel behavior around the human body to calculate the packet error rate (PER) found in a typical
WBSN under the aforementioned standard. This PER is then introduced into our analytical model.
The use of P2P networks for multimedia distribution has spread out globally in recent years.
Therefore, there is a strong need for a content distribution mechanism over P2P networks that do not pose security and privacy threats to the copyright holders or to end users, respectively. The existent systems for copyright and privacy protection employ cryptographic mechanisms at a cost of high computational burden which makes these systems impractical for distributing large files. In this presentation, the authors (Amna Qureshi, David Megías, Helena Rifà-Pous) propose and analyse a P2P content distribution system which allows efficient distribution of large-sized content while preserving the security and privacy of merchants and buyers, respectively. The experimental results confirm that the framework provides an efficient solution to copyright infringement issues over P2P networks, while protecting the end users’ privacy.
Automated Performance Analysis of Business ProcessesDaniele Gianni
Presentation at the 2nd International Workshop on Model-driven Approaches for Simulation Engineering
(held within the SCS/IEEE Symposium on Theory of Modeling and Simulation part of SpringSim 2012)
Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
Modules for reusable and collaborative modeling of biological mathematical sy...Daniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
ModelicaML Value Bindings for Automated Model CompositionDaniele Gianni
Presentation at the 2nd International Workshop on Model-driven Approaches for Simulation Engineering
(held within the SCS/IEEE Symposium on Theory of Modeling and Simulation part of SpringSim 2012)
Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
jEQN a java-based language for the distributed simulation of queueing networksDaniele Gianni
Presentation at the ISCIS 2006 Conference in Istanbul, Turkey.
Simulation language for Extended Queueing Networks on IEEE HLA infrastructures.
For further info, please visit:
https://sites.google.com/site/simulationarchitecture/
Calibration of Deployment Simulation Models - A Multi-Paradigm Modelling Appr...Daniele Gianni
Presentation at the 2nd International Workshop on Model-driven Approaches for Simulation Engineering
(held within the SCS/IEEE Symposium on Theory of Modeling and Simulation part of SpringSim 2012)
Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
Collaborative modeling and co simulation with destecs - a pilot studyDaniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
High Performance Stream Processing and OptimizationsFarley Lai
Building scalable systems that process streams of data requires developers to take advantage of the parallelism capabilities offered by today's computer architectures. Existing imperative programming languages provide programmers low-level primitives such as threads, locks, and semaphores. However, programs developed using these primitives tend to be plagued by race conditions and deadlocks, which make it hard to understand and debug non-deterministic behaviors. Acknowledging these limitations, some programming language extensions and libraries (e.g., OpenMPI, OpenMP have been proposed to simplify programming parallel programs. Nevertheless, all these options still burden the programmer with annotating parallelism and specifying data sharing attributes to ensure data consistency.
In recent years, dataflows have attracted significant attention as a model for building highly parallel stream processing applications. According to this model, an application is defined as a graph of processing elements that are connected by communication channels. The processing elements may execute in parallel as long as they have sufficient data to process. A key feature of the dataflow model is that it explicitly capture parallelism and data dependencies between processing elements.
Even though the dataflows provide a simple computational model, using this model to build scalable systems is challenging as naive implementations introduce unexpected runtime scheduling overhead, consume significant memory resources, and are not energy efficient. Consequently, our goal is to develop compiler optimizations and efficient runtime environments for scalable dataflow systems. In the following, we will go through the model of computation, memory optimizations, energy efficiency and stream processing at the scale of cloud computing.
CIM to Modelica Factory - Automated Equation-Based Cyber-Physical Power Syste...Luigi Vanfretti
The Common Information Model (CIM) is described using the Unified Modeling Language (UML). UML can also describe data model of cyber-physical power system components and networks. However, there are several difficulties to transform the data model into a strictly defined mathematical model. A strictly defined mathematical model is one for which all-differential algebraic and discrete model equations are explicitly defined [1] (i.e. the equations are written in human readable form). This is known as equation-based modelling [2], and it is utilized in many areas such as the automotive and aerospace industry [3].
The automated generation of an unambiguous equation-based model would allow performing time-domain simulations of cyber-physical power systems [4] and the assessment of textual requirements, which could be defined from the UML model directly [5]. This flexibility would allow adopting model-based systems engineering practices within the power industry, such as those used in process control.
For the implementation of models in an equation-based language, the Modelica language [6] is the one of the best choices because it follows the Object Oriented Programming (OOP) notation, with a close relation with UML. Furthermore, the ModelicaML [5], an extended subset of the OMG Unified Modeling Language, enables integrated modelling and simulation of system requirements and design. Combining CIM, ModelicaML and Modelica models of cyber-physical power system components it is possible to automatically generate unambiguous mathematical models that can be used for simulation and requirements verification [7].
This CIM to Modelica Factory talk explores this possibility.
One of the main challenges that we face with power systems models defined using the Modelica language is the initialization of the dynamic states (in equilibrium condition) of the components within a model [8, 9]. However, objects and components modelled in CIM standard contain attributes for storing a power flow solution.
The purpose of the work described in this presentation is to develop a software tool capable to transform a CIM object model into a Modelica model that can be directly simulated using different Modelica engines. To this aim, we start from the CIM/UML representation of power system components and models, and exploit the ModelicaML profile to achieve a proper code representation of the power system in Modelica code. To confront issues with dynamic initialization, the power flow solution from CIM is linked to the Modelica component models and utilized within the initialization algorithms of the simulation engines. The result is a software tool that allows performing time domain simulations directly from a CIM/UML structure, while maintaining consistency in the resulting mathematical model within different simulation engines.
Towards CIM-Compliant Model-Based Cyber-Physical Power System Design and Simu...Luigi Vanfretti
Compliance with grid data exchange standards (i.e. CIM) can allow for sustainable software development in power systems if open and equation-based modeling languages and simulation standards are exploited . Together with my PhD student Francisco José Gómez López, we will be @RT-2014 presenting our vision and recent work carried out together with Svein Olsen: "Towards CIM-Compliant Model-Based Cyber-Physical Power System Design and Simulation using Modelica".
DE-CPS 2017 The INTO-CPS Cyber-Physical System Profile Alessandra BagnatoAlessandra Bagnato
The Modelio SysML/INTO-CPS module include an INTO-CPS/SysML profile which is organized around the following logical groups: block, type, instance, library and diagram. Only the block group is presented in this paper. The next two diagrams depict the block group. INTO-CPS profile specializes SysML Block concept into one sub-concept named “Component”, with is also specializes into four sub-concepts respectively named “System”, “Subsystem”, “Cyber”, and “Physical”.
Modular Mathematical Modelling of Biological SystemsDaniele Gianni
Presentation at the 2nd International Workshop on Model-driven Approaches for Simulation Engineering
(held within the SCS/IEEE Symposium on Theory of Modeling and Simulation part of SpringSim 2012)
Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
Wireless body sensor networks (WBSN) are a particular type of wireless sensor networks (WSN)
that are becoming an important topic in the technological research community. Advances in the
reduction of the power consumption and cost of these networks have led to solutions mature enough
for their use in a broad range of applications such as sportsman or health monitoring.
The development of those applications has been stimulated by the finalization of the IEEE 802.15.4
standard, which defines the medium access control (MAC) and physical layer (PHY) for low-rate
wireless personal area networks (LR-WPAN). One of the MAC schemes proposed is slotted Carrier
Sense Multiple Access with Collision Avoidance (CSMA/CA). This project analyzes the performance of
this MAC, based on a state-of-the-art analytical model for a star topology, which captures the behavior
of the MAC using two Markov chain models; the per-node state model and the channel state model.
More importantly, we extend this model to include acknowledged traffic. The impact of including
acknowledgments is evaluated in terms of energy consumption, throughput and latency.
The performance predicted by the analytical model has been extensively verified with simulations
using the ns-2 IEEE 802.15.4 contributed module. Throughput, energy consumption and latency
analysis is performed. Additionally, we have simulated a statistical channel model describing the radio
channel behavior around the human body to calculate the packet error rate (PER) found in a typical
WBSN under the aforementioned standard. This PER is then introduced into our analytical model.
The use of P2P networks for multimedia distribution has spread out globally in recent years.
Therefore, there is a strong need for a content distribution mechanism over P2P networks that do not pose security and privacy threats to the copyright holders or to end users, respectively. The existent systems for copyright and privacy protection employ cryptographic mechanisms at a cost of high computational burden which makes these systems impractical for distributing large files. In this presentation, the authors (Amna Qureshi, David Megías, Helena Rifà-Pous) propose and analyse a P2P content distribution system which allows efficient distribution of large-sized content while preserving the security and privacy of merchants and buyers, respectively. The experimental results confirm that the framework provides an efficient solution to copyright infringement issues over P2P networks, while protecting the end users’ privacy.
Automated Performance Analysis of Business ProcessesDaniele Gianni
Presentation at the 2nd International Workshop on Model-driven Approaches for Simulation Engineering
(held within the SCS/IEEE Symposium on Theory of Modeling and Simulation part of SpringSim 2012)
Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
Modules for reusable and collaborative modeling of biological mathematical sy...Daniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
ModelicaML Value Bindings for Automated Model CompositionDaniele Gianni
Presentation at the 2nd International Workshop on Model-driven Approaches for Simulation Engineering
(held within the SCS/IEEE Symposium on Theory of Modeling and Simulation part of SpringSim 2012)
Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
jEQN a java-based language for the distributed simulation of queueing networksDaniele Gianni
Presentation at the ISCIS 2006 Conference in Istanbul, Turkey.
Simulation language for Extended Queueing Networks on IEEE HLA infrastructures.
For further info, please visit:
https://sites.google.com/site/simulationarchitecture/
Calibration of Deployment Simulation Models - A Multi-Paradigm Modelling Appr...Daniele Gianni
Presentation at the 2nd International Workshop on Model-driven Approaches for Simulation Engineering
(held within the SCS/IEEE Symposium on Theory of Modeling and Simulation part of SpringSim 2012)
Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
Collaborative modeling and co simulation with destecs - a pilot studyDaniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
Workshop presentation in DSim Day, research event on Distributed Simulation, Rome, Italy, March, 2010.
Please visit:
https://sites.google.com/site/simulationarchitecture/
for further information.
Collaborative engineering solutions and challenges in the development of spac...Daniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
A vision on collaborative computation of things for personalized analysesDaniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
SysML to Discrete-event Simulation to Analyze Electronic Assembly SystemsDaniele Gianni
Presentation at the 2nd International Workshop on Model-driven Approaches for Simulation Engineering
(held within the SCS/IEEE Symposium on Theory of Modeling and Simulation part of SpringSim 2012)
Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
How to create innovative architecture using VisualSim?Deepak Shankar
In this presentation, we will get you started on using VisualSim Architect to conduct performance analysis, power measurement and functional validation. You will learn advanced concepts of system modeling and how to apply VisualSim Architect for a variety of applications.
Highlights include the application for both System-on-Chip and Large Systems including Designing memory interfaces using DDR3 and LPDDR3.
VisualSim Architect is used by systems and semiconductor companies to validate and analyze the specification of the product. The environment offers an easy-to-use methodology, huge library of technology components, extremely fast simulator and a huge reports list.
How to create innovative architecture using ViualSim?Deepak Shankar
In this presentation, we will get you started on using VisualSim Architect to conduct performance analysis, power measurement and functional validation. You will learn advanced concepts of system modeling and how to apply VisualSim Architect for a variety of applications.
Highlights include the application for both System-on-Chip and Large Systems including Designing memory interfaces using DDR3 and LPDDR3.
VisualSim Architect is used by systems and semiconductor companies to validate and analyze the specification of the product. The environment offers an easy-to-use methodology, huge library of technology components, extremely fast simulator and a huge reports list.
Please find our webinar video - How to create innovative architecture using ViualSim? at the last slide.
How to create innovative architecture using VisualSim?Deepak Shankar
In this presentation, we will get you started on using VisualSim Architect to conduct performance analysis, power measurement and functional validation. You will learn advanced concepts of system modeling and how to apply VisualSim Architect for a variety of applications.
Highlights include the application for both System-on-Chip and Large Systems including Designing memory interfaces using DDR3 and LPDDR3.
VisualSim Architect is used by systems and semiconductor companies to validate and analyze the specification of the product. The environment offers an easy-to-use methodology, huge library of technology components, extremely fast simulator and a huge reports list.
"Computing systems for AI workloads have evolved towards data-center clusters of GPUs and TPUs, with architectures optimized for performing linear algebra and tunable for variable precision. As new AI paradigms emerge, more distinct divergence between hardware architectures for powering AI and other workloads are observed. GPU manufacturers are developing different architectures and chipsets for the HPC/supercomputing, cloud, edge computing, and robotics domains. FPGA vendors are also joining this ecosystem (e.g., Intel FPGAs deployed within Microsoft Azure). Moving forward, many industries and services ranging from cloud computing to consumer electronics are making hardware-accelerated AI a prominent component in their portfolio.
In this talk, some examples of AI hardware architectures and available silicon technologies will be presented. The concept of co-design will be discussed. This makes the unique needs of an application domain transparent to the hardware design process. Finally, an overview of design automation tool flows will be presented to gain an understanding of how to support a high productivity framework for domain experts to design and deploy AI hardware."
Many emerging applications require methods tailored towards high-speed data acquisition and filtering of streaming data followed by offline event reconstruction and analysis. In this case, the main objective is to relieve the immense pressure on the storage and communication resources within the experimental infrastructure. In other applications, ultra low latency real time analysis is required for autonomous experimental systems and anomaly detection in acquired scientific data in the absence of any prior data model for unknown events. At these data rates, traditional computing approaches cannot carry out even cursory analyses in a time frame necessary to guide experimentation. In this talk, Prof. Ogrenci will present some examples of AI hardware architectures. She will discuss the concept of co-design, which makes the unique needs of an application domain transparent to the hardware design process and present examples from three applications: (1) An in-pixel AI chip built using the HLS methodology; (2) A radiation hardened ASIC chip for quantum systems; (3) An FPGA-based edge computing controller for real-time control of a High Energy Physics experiment.
Architectural tricks to maximize memory bandwidthDeepak Shankar
Deepak Shankar, CEO and Founder of Mirabilis Deign Inc. hosted a webinar(Feb 17,2016) on the architectural possibilities to improve memory bandwidth. This webinar highlighted that memory plays a role in impacting the performance & power consumption of a system.
RAMSES: Robust Analytic Models for Science at Extreme ScalesIan Foster
RAMSES: A new project in data-driven analytical modeling of distributed systems
RAMSES is a new DOE-funded project on the end-to-end analytical performance modeling of science workflows in extreme-scale science environments. It aims to link multiple threads of inquiry that have not, until now, been adequately connected: namely, first-principles performance modeling within individual sub-disciplines (e.g., networks, storage systems, applications), and data-driven methods for evaluating, calibrating, and synthesizing models of complex phenomena. What makes this fusion necessary is the drive to explain, predict, and optimize not just individual system components but complex end-to-end workflows. In this talk, I will introduce the goals of the project and some aspects of our technical approach.
Improving the Efficiency of Cloud Infrastructures with Elastic Tandem Machine...Frank Dürr
The presentation of our full paper presented at IEEE Cloud 2013.
Abstract: In this paper, we propose a concept for improving the energy efficiency and resource utilization of cloud infrastructures by combining the benefits of heterogeneous machine instances. The basic idea is to integrate low-power system on a chip (SoC) machines and high-power virtual machine instances into so-called Elastic Tandem Machine Instances (ETMI). The low-power machine serves low load and is always running to ensure the availability of the ETMI. When load rises, the ETMI scales up automatically by starting the high-power instance and handing over traffic to it. For the non-disruptive transition from low-power to high-power machines and vice versa, we present a handover mechanism based on software-defined networking technologies. Our evaluations show the applicability of low-power SoC machines to serve low load efficiently as well as the desired scalability properties of ETMIs.
Lessons Learned from Managing Thousands of Production Apache Spark Clusters w...Databricks
At Databricks, we have a unique view into hundreds different companies using Apache Spark for development and production use-cases, from their support tickets and forum posts. Having seen so many different workflows and applications, some discernible patterns emerge when looking at common manageability, debugging, and visibility issues that our users run into. This talk will first show some representatives of these common issues. Then, we will show you what we have done and have been working on in Databricks to make Spark clusters easier to manage, monitor, and debug.
REAL-TIME SIMULATION TECHNOLOGIES FOR POWER SYSTEMS DESIGN, TESTING, AND ANAL...Jithin T
This is the ppt that contains effective elementsof the IEEE research journel "REAL-TIME SIMULATION TECHNOLOGIES FOR POWER
SYSTEMS DESIGN, TESTING, AND ANALYSIS"
Mobile+Cloud: a viable replacement for desktop cheminformatics?Alex Clark
Introduces the prospect of performing cheminformatics workflows using mobile devices for the UI and cloud resources for heavy duty computation. Describes a sample workflow using the SAR Table app to propose potential new drugs for eliminating malaria.
Similar to Integrated modeling and simulation framework for wireless sensor networks (20)
Simulation assisted elicitation and validation of behavioral specifications f...Daniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
Validation of Spacecraft Behaviour Using a Collaborative ApproachDaniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
Collaborative development and cataloguing of simulation and calculation model...Daniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
A package system for maintaining large model distributions in vle softwareDaniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
A framework for distributed control and building performance simulationDaniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
A collaborative environment for urban landscape simulationDaniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
System model optimization through functional models execution methodology and...Daniele Gianni
Presentation delivered at the 3rd IEEE Track on
Collaborative Modeling & Simulation - CoMetS'12.
Please see http://www.sel.uniroma2.it/comets12/ for further details.
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Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
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Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
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Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
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Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
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Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
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Please see: http://www.sel.uniroma2.it/mod4sim12/ for further details
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Please visit
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The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
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The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Integrated modeling and simulation framework for wireless sensor networks
1. Integrated Modeling and Simulation Framework
for Wireless Sensor Networks
Baobing Wang and John S. Baras
Institute for Systems Research
Department of Electrical and Computer Engineering
University of Maryland, College Park, USA
WETICE/COMETS, Toulouse, France
June 27, 2012
2. Content
• Motivation
• Contributions
• System Framework and Design Flow
• Model Libraries
• Case Study
• Conclusion
2
3. Motivation
• WSNs as cyber-physical systems
– Physical environments, physical platforms
– Communication protocols
– Computation algorithms
• Drawbacks of current design methodology
– Ad hoc design approaches
– Test only limited design alternatives
– Lack of reusability
– Little consideration of the interaction between
continuous-time domain and event-triggered domain
3
4. Motivation
• Support system design for heterogeneous
WSNs
– Hybrid design framework required: event-
triggered and continuous-time dynamics
– Component reusability
– Multiple optimization objectives: trade-off
analysis and design space exploration
– Mission-critical applications: model-checking
4
5. Contributions
• Propose a model-based system design
framework for WSNs
– Integrate both event-triggered and continuous-time
dynamics
– Provide a hierarchy of system model libraries
• Propose a system design flow within our model-
based framework
– Based on an industry standard tool
– Simulation codes (Simulink and C++) are generated
automatically
– Support trade-off analysis and optimization
5
6. System Framework
• Model libraries
– Application Model Library
– Service Model Library
– Network Model Library
– Physical System Model Library
– Environment Model Library
• Development Principles
– Event-triggered: Statecharts in SysML
– Continuous-time: Simulink or Modelica
6
7. System Framework
Distributed Computing
Communication and
Sensor Database
Physical World
7
9. System Design Flow
• Trade-off analysis and design space exploration
– Each component is described with performance index
– Parametric Diagrams
– Parametric Constraint Evaluator or CPLEX
• Simulate in Matlab/Simulink
– All SysML blocks are transformed into a single S-function
– Generate Simulink source file
• Simulate in IBM Rational Rhapsody
– Generate C/C++ source code
– Statechart animation
– Interactive simulation
9
11. Model Libraries
Example: behavior
model of a transceiver
using Statechart in IBM
Rhapsody
11
12. Model Libraries
• MAC Layer Components
– Low Power Listener: adjust radio’s power state based
on channel activity
– CSMA/CA Channel Access: gain channel access right
in CSMA/CA mechanisms
– CSMA/CA Sender: send one packet with
retransmissions in CSMA/CA mechanisms
– MAC Controller: specify the control logic of a MAC
protocol (ports are defined, but behavior should be
customized for each protocol)
– Slot Manager, Queue Manager, TDMA Sender,
Receiver …
12
14. Model Libraries
• Physical Environment
– Modeled using Simulink or Modelica
– Built using the Embedded Coder to generate C/C++
codes
– Imported as SysML blocks
– New environment information are pushed to event-
triggered blocks periodically
• Wireless Channels
– Radio propagation models, channel fading models
and BER under different modulation schemes
– Currently support: free space model, UDG model, ITU
indoor model and Rayleigh fading model
14
16. Case Study
• Simulation scenarios
– Wireless + No Pipe: disable pipe, send data
wirelessly, measure period is 5 seconds
– Wireless + Pipe (5s): similar to above, but
pipe is enabled
– Wireless + Pipe (60s): similar to above, but
measure period is 60 seconds
– Wired + Pipe: temperature data are available
immediately and directly
16
19. Conclusions
• We proposed a model-based system
design framework for WSNs
• The proposed system design flow can
integrate both continuous-time and event-
triggered modules, and study the system
performance using generated codes
• Composability and reusability are
demonstrated through a case study
19