scholarly journals Bonsai: An event-based framework for processing and controlling data streams

2014 ◽  
Author(s):  
Gonçalo Lopes ◽  
Niccolò Bonacchi ◽  
João Frazão ◽  
Joana P. Neto ◽  
Bassam V. Atallah ◽  
...  
Keyword(s):  
Data Streams ◽  
High Performance ◽  
Visual Programming ◽  
Scientific Data ◽  
Video Tracking ◽  
Loop Control ◽  
Online Processing ◽  
Parallel Data ◽  
Develop Software ◽  
Event Based

The design of modern scientific experiments requires the control and monitoring of many parallel data streams. However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data. Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams. We describe Bonsai's core principles and architecture and demonstrate how it allows for flexible and rapid prototyping of integrated experimental designs in neuroscience. We specifically highlight different possible applications which require the combination of many different hardware and software components, including behaviour video tracking, electrophysiology and closed-loop control of stimulation parameters.

Using computer clusters for processing spatial-temporal data streams in data acquisition systems

2018 ◽  
Vol 935 (5) ◽  
pp. 54-63
Author(s):  
A.A. Maiorov ◽  
A.V. Materuhin ◽  
I.N. Kondaurov
Keyword(s):  
Data Acquisition ◽  
Data Streams ◽  
Spatial Data ◽  
High Performance ◽  
Temporal Data ◽  
Cyberphysical Systems ◽  
Software Environment ◽  
Computer Clusters ◽  
Data Acquisition Systems ◽  
Essential Components

Geoinformation technologies are now becoming “end-to-end” technologies of the new digital economy. There is a need for solutions for efficient processing of spatial and spatio-temporal data that could be applied in various sectors of this new economy. Such solutions are necessary, for example, for cyberphysical systems. Essential components of cyberphysical systems are high-performance and easy-scalable data acquisition systems based on smart geosensor networks. This article discusses the problem of choosing a software environment for this kind of systems, provides a review and a comparative analysis of various open source software environments designed for large spatial data and spatial-temporal data streams processing in computer clusters. It is shown that the software framework STARK can be used to process spatial-temporal data streams in spatial-temporal data streams. An extension of the STARK class system based on the type system for spatial-temporal data streams developed by one of the authors of this article is proposed. The models and data representations obtained as a result of the proposed expansion can be used not only for processing spatial-temporal data streams in data acquisition systems based on smart geosensor networks, but also for processing spatial-temporal data streams in various purposes geoinformation systems that use processing data in computer clusters.

Application of High Performance Electro-Hydraulic Synchronous Servo System on NC Broaching Machine

2013 ◽  
Vol 721 ◽  
pp. 497-500
Author(s):  
Guo Jin Chen ◽  
Jing Ni ◽  
Ting Ting Liu ◽  
Ming Xu
Keyword(s):  
High Performance ◽  
Control Process ◽  
Servo System ◽  
Numerical Control ◽  
Servo Drive ◽  
Loop Control ◽  
Performance Accuracy ◽  
Drive Control ◽  
Hydraulic Servo ◽  
Output Quantity

Aiming at the lower performance, accuracy and efficiency of the existing motion control process for the traditional broaching machine, the paper studies the high-performance dual-hydraulic synchronous servo drive control technology. The synchronous electro-hydraulic servo system forms the closed loop control by the detection and feedback of the output quantity. It eliminates and restrains largely the influence of the adverse factors to obtain the high-precision synchronous driving performance. The numerical control system based on the real-time error compensation and the intelligent control to the auxiliary machinery is developed. It is used for the CNC broaching machine to make the steady-state synchronous displacement error of the double cylinders be ≤ 0.5mm.

scholarly journals Beyond HEP: Photon and accelerator science computing infrastructure at DESY

2020 ◽  
Vol 245 ◽  
pp. 07036
Author(s):  
Christoph Beyer ◽  
Stefan Bujack ◽  
Stefan Dietrich ◽  
Thomas Finnern ◽  
Martin Flemming ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
High Energy Physics ◽  
High Energy ◽  
Resource Provisioning ◽  
Small Scale ◽  
Online Processing ◽  
Offline Processing ◽  
National Analysis ◽  
Energy Physics

DESY is one of the largest accelerator laboratories in Europe. It develops and operates state of the art accelerators for fundamental science in the areas of high energy physics, photon science and accelerator development. While for decades high energy physics (HEP) has been the most prominent user of the DESY compute, storage and network infrastructure, various scientific areas as science with photons and accelerator development have caught up and are now dominating the demands on the DESY infrastructure resources, with significant consequences for the IT resource provisioning. In this contribution, we will present an overview of the computational, storage and network resources covering the various physics communities on site. Ranging from high-throughput computing (HTC) batch-like offline processing in the Grid and the interactive user analyses resources in the National Analysis Factory (NAF) for the HEP community, to the computing needs of accelerator development or of photon sciences such as PETRA III or the European XFEL. Since DESY is involved in these experiments and their data taking, their requirements include fast low-latency online processing for data taking and calibration as well as offline processing, thus high-performance computing (HPC) workloads, that are run on the dedicated Maxwell HPC cluster. As all communities face significant challenges due to changing environments and increasing data rates in the following years, we will discuss how this will reflect in necessary changes to the computing and storage infrastructures. We will present DESY compute cloud and container orchestration plans as a basis for infrastructure and platform services. We will show examples of Jupyter notebooks for small scale interactive analysis, as well as its integration into large scale resources such as batch systems or Spark clusters. To overcome the fragmentation of the various resources for all scientific communities at DESY, we explore how to integrate them into a seamless user experience in an Interdisciplinary Data Analysis Facility.

A new opportunity for the emerging tellurium semiconductor: resistive switching device implementation

2021 ◽  
Author(s):  
Yifei Yang ◽  
Mingkun Xu ◽  
Lujie Xu ◽  
Xinxin Wang ◽  
Huan Liu ◽  
...  
Keyword(s):  
Data Storage ◽  
Resistive Switching ◽  
High Performance ◽  
Visual Recognition ◽  
Stimulus Frequency ◽  
Dielectric Materials ◽  
Cross Point ◽  
Electrochemically Active ◽  
Event Based

Abstract The electrochemical (EC) resistive switching (RS) cross-point arrays, composed of nonvolatile RS (NV-RS) memories and volatile RS (V-RS) selectors, hold promise for high-density data storage, in-memory computing and neuromorphic computing. However, the conventional EC-RS devices based on metallic filaments suffer from the notorious current-volatility dilemma that the low and high current requirements for NV-RS memories and V-RS selectors, respectively, cannot be satisfied simultaneously, due to the dominant EC nature of the RS. In this work, we demonstrate electrochemically active, low thermal-conductivity and low melting-temperature semiconducting tellurium filament-based RS devices that solve this dilemma, enabling NV-RS memories to operate under lower currents than do V-RS selectors. This novel phenomenon arises as the consequence of the adversarial EC and Joule heating (JH) effects. The devices also show unusual stimulus frequency dependent long-term plasticity (LTP)-to-short-term plasticity (STP) transition. Devices with this property can be generically utilized as spatial-temporal filters in spiking neural networks (SNNs) for high-performance event-based visual recognition tasks, as illustrated in our noise filtering simulations. By regulating the EC-JH relationship using dielectric materials with decreasing thermal conductivities, full functional-range tunable Te filament-based devices, from always-NV RS, to NV-to-V transitionable RS, and to always-V RS, are also demonstrated. The tellurium filament-based RS devices are promising enablers for functional cross-point arrays.

A Tour of Lattice-Based Skyline Algorithms

2018 ◽  
pp. 96-122
Author(s):  
Markus Endres ◽  
Lena Rudenko
Keyword(s):  
Real Time ◽  
Data Streams ◽  
High Performance ◽  
State Of The Art ◽  
Experimental Results ◽  
Lattice Structures ◽  
Skyline Query ◽  
Basic Concepts ◽  
Generic Index

A skyline query retrieves all objects in a dataset that are not dominated by other objects according to some given criteria. There exist many skyline algorithms which can be classified into generic, index-based, and lattice-based algorithms. This chapter takes a tour through lattice-based skyline algorithms. It summarizes the basic concepts and properties, presents high-performance parallel approaches, shows how one overcomes the low-cardinality restriction of lattice structures, and finally presents an application on data streams for real-time skyline computation. Experimental results on synthetic and real datasets show that lattice-based algorithms outperform state-of-the-art skyline techniques, and additionally have a linear runtime complexity.

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