Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now-with the rise of multimodal acquisition systems and the associated processing capabilit...
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Improvements in scientific instrumentation allow imaging at mesoscopic to atomic length scales, many spectroscopic modes, and now-with the rise of multimodal acquisition systems and the associated processing capability-the era of multidimensional, informationally dense data sets has arrived. Technical issues in these combinatorial scientific fields are exacerbated by computational challenges best summarized as a necessity for drastic improvement in the capability to transfer, store, and analyze large volumes of data. The Bellerophon Environment for Analysis of Materials (BEAM) platform provides material scientists the capability to directly leverage the integrated computational and analytical power of High Performance Computing (HPC) to perform scalable data analysis and simulation via an intuitive, cross-platform client user interface. This framework delivers authenticated, "push-button" execution of complex user workflows that deploy data analysis algorithms and computational simulations utilizing the converged compute-and-data infrastructure at Oak Ridge National Laboratory's (ORNL) Compute and Data Environment for Science (CADES) and HPC environments like Titan at the Oak Ridge Leadership Computing Facility (OLCF). In this work we address the underlying HPC needs for characterization in the material science community, elaborate how BEAM's design and infrastructure tackle those needs, and present a small sub-set of user cases where scientists utilized BEAM across a broad range of analytical techniques and analysis modes.
As systems continue to evolve they rely less on human decision-making and more on computational intelligence. This trend in conjunction to the available technologies for providing advanced sensing, measurement, proces...
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ISBN:
(纸本)9781467373111
As systems continue to evolve they rely less on human decision-making and more on computational intelligence. This trend in conjunction to the available technologies for providing advanced sensing, measurement, process control, and communication leads us towards the new field of Cyber-Physical System (CPS). Although these systems exhibit remarkable characteristics, the increased complexity imposed by numerous components and services makes their design extremely difficult. This paper proposes a software-supported framework for reducing the design complexity regarding the modeling, as well as the simulation of CPS. For this purpose, a novel technique based on system scenarios is applied. Evaluation results prove the effectiveness of introduced framework, as we achieve to reduce mentionable the modeling and simulation complexity with a controllable overhead in accuracy.
After their success in the high performance and desktop market, Graphic Processing Units (GPUs), that can be used for general purpose computing are introduced for embeddedsystems on a chip (SOCs). Due to some advance...
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ISBN:
(纸本)9781467373111
After their success in the high performance and desktop market, Graphic Processing Units (GPUs), that can be used for general purpose computing are introduced for embeddedsystems on a chip (SOCs). Due to some advanced architectural features, like massive simultaneous multithreading, static performance analysis and high-level timing simulation are difficult to apply to code running on these systems. This paper extends a method for performance simulation of GPUs. The method uses automated performance annotations in the application's OpenCL C source code, and an extended performance model for derivation of a kernels runtime from metrics produced by the execution of annotated kernels. The final results are then generated using a probabilistic resource conflict model. The model reaches an accuracy of 90% on most test cases and delivers a higher average accuracy than previous methods.
Deep Learning (DL) is becoming popular in a wide range of domains. Many emerging applications, ranging from image and speech recognition to natural language processing and information retrieval, rely heavily on deep l...
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ISBN:
(纸本)9781509035892
Deep Learning (DL) is becoming popular in a wide range of domains. Many emerging applications, ranging from image and speech recognition to natural language processing and information retrieval, rely heavily on deep learning techniques, especially the Neural Networks (NNs). NNs have led to great advances in recognition accuracy compared with other traditional methods in recent years. NN-based methods demand much more computation and memory resource, and therefore a number of NN accelerators have been proposed on CMOS-based platforms, such as FPGA and GPU [1]. However, it becomes more and more difficult to obtain substantial power efficiency and gains directly through the scaling down of traditional CMOS technique. Meanwhile, the large data amount in DL applications also meets an ever-increasing “memory wall” challenge because of the efficiency of von Neumann architecture. Consequently, there is a growing research interest of exploring emerging nano-devices and new computing architectures to further improve power efficiency [2].
Advances in digital-microfluidic biochips have led to miniaturized platforms that can implement biomolecular assays. However, these designs are not adequate for running multiple sample pathways because they consider u...
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ISBN:
(纸本)9781509035892
Advances in digital-microfluidic biochips have led to miniaturized platforms that can implement biomolecular assays. However, these designs are not adequate for running multiple sample pathways because they consider unrealistic static schedules; hence runtime adaptation based on assay outcomes is not supported and only a rigid path of bioassays can be run on the chip. We present a design framework that performs fluidic task assignment, scheduling, and dynamic decision-making for quantitative epigenetics. We first describe our benchtop experimental studies to understand the relevance of chromatin structure on the regulation of gene function and its relationship to biochip design specifications. The proposed method models biochip design in terms of real-time multiprocessor scheduling and utilizes a heuristic algorithm to solve this NP-hard problem. simulation results show that the proposed algorithm is computationally efficient and it generates effective solutions for multiple sample pathways on a resource-limited biochip. We also present experimental results using an embedded microcontroller as a testbed.
In this work, we introduce an application auto-tuning framework to dynamically adapt applications in multicore architectures. In particular, the framework exploits design-time knowledge and multi-objective requirement...
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ISBN:
(纸本)9781467373111
In this work, we introduce an application auto-tuning framework to dynamically adapt applications in multicore architectures. In particular, the framework exploits design-time knowledge and multi-objective requirements expressed by the user, to drive the autotuning process at the runtime. It also exploits a monitoring infrastructure to get runtime feed-back and to adapt to external changing conditions. The intrusiveness of the autotuning framework in the application (in terms of refactoring and lines of code to be added) has been kept limited, also to minimize the integration cost. To assess the proposed framework, we carried out an experimental campaign to evaluate the overhead, the relevance of the described features and the efficiency of the framework.
The proceedings contain 27 papers. The special focus in this conference is on Software Engineering for Defence Applications. The topics include: Managing increasing user needs complexity within the ITA army agile fram...
ISBN:
(纸本)9783319278940
The proceedings contain 27 papers. The special focus in this conference is on Software Engineering for Defence Applications. The topics include: Managing increasing user needs complexity within the ITA army agile framework;ita army agile software implementation of the LC2EVO army infrastructure strategic management tool;consumer electronics augmented reality in defense applications;the human factors as the weakest link in the chain;rapid prototyping;pair programming and other agile techniques;experiences from the market;supplementing agile practices with decision support methods for military software development;benefits of open source software in defense environments;a modeling and simulation showcase in military logistics;software characteristics for program forza NEC main systems;agile plus new army diffused and shared leadership;role of the design authority in large scrum of scrum multi-team-based programs;make your enterprise agile transformation initiative an awesome success;DevOps movement of enterprise agile breakdown silos, create collaboration, increase quality, and application speed;MBDA extendible C2 weapon system in collaboration environment;a new device for high-accuracy measurements of the hardness depth profile in steels;AGILE methodology in progesi MDA model;self-validating bundles for flexible data access control;improving bug predictions in multicore cyber-physical systems;predicting the fate of requirements in embedded domains;capturing user needs for agile software development and a course on software architecture for defense applications.
The requirements' demands of applications, such as real-time response, are pushing the wearable devices to leverage more power-efficient processors inside the SoC (System-on-chip). However, existing wearable devic...
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ISBN:
(纸本)9781509035892
The requirements' demands of applications, such as real-time response, are pushing the wearable devices to leverage more power-efficient processors inside the SoC (System-on-chip). However, existing wearable devices are not well suited for such challenging applications due to poor performance, while the conventional powerful many-core architectures are not appropriate either due to the stringent power budget in this domain. We propose LOCUS - a low-power, customizable, many-core processor for next-generation wearable devices. LOCUS combines customizable processor cores with a customizable network on a message-passing architecture to deliver very competitive performance/watt - an average 3.1× compared to quad-core ARM processors used in the state-of-the-art wearable devices. A combination of full-system simulation with representative applications from wearable domain and RTL synthesis of the architecture show that 16-core LOCUS achieves an average 1.52× performance/watt improvement over a conventional 16-core shared-memory many-core architecture.
FMI (Functional Mockup Interface) is a standard for exchanging and co-simulating model components (called FMUs) coming from potentially different modeling formalisms, languages, and tools. Previous work has proposed a...
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ISBN:
(纸本)9781467373111
FMI (Functional Mockup Interface) is a standard for exchanging and co-simulating model components (called FMUs) coming from potentially different modeling formalisms, languages, and tools. Previous work has proposed a formal model for the co-simulation part of the FMI standard, and also presented two co-simulation algorithms which can be proven to have desirable properties, such as determinacy, provided the FMUs satisfy a formal contract. In this paper we discuss the principles for encoding different modeling formalisms, including state machines (both untimed and timed), discrete-event systems, and synchronous dataflow, as FMUs. The challenge is to bridge the various semantic gaps (untimed vs. timed, signals vs. events, etc.) that arise because of the heterogeneity between these modeling formalisms and the FMI API.
In this work we propose to control robot using computed torque compensator based on robust estimation method called Support Vector Regression (SVR). This method represent the innovative side of this work;as it is powe...
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ISBN:
(纸本)9783319410098;9783319410081
In this work we propose to control robot using computed torque compensator based on robust estimation method called Support Vector Regression (SVR). This method represent the innovative side of this work;as it is powerful to modeling and identifying the nonlinear systems such as the disturbances that can appear during robot tracking a desired trajectory. The computed torque technique is also used from to pre-compensate the dynamics behavior of the nominal system. In order to demonstrate and show the robustness of the proposed control law, we have tested the system (Puma 560 robot) with a series of tests in simulation environment. The obtained results allow us to validate the proposed control law. As a result, the SVR reacts quickly to reject the errors originating from the disturbances.
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