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Movement computing Education for Middle Grades 20

Movement Computing Education for Middle Grades

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7th international conference on Movement and computing

作者： Bergner, Yoav Damast, Deborah Romita, Allegra Smock, Anne Marie Robson NYU New York NY 10012 USA

ISBN: (纸本)9781450375054

this paper takes a theoretical approach to movement computing education for young learners, with a focus on middle grades (grades 6-8, ages 11-14). this age group is targeted as a lower bound because, while some elements of computational thinking may be available to still younger learners, there are abstractions involved in movement computation that pre-require a certain amount of formal operation, in the Piagetian sense. We outline a parallel foundation of key ideas in movement (specifically dance) and key ideas in computing (specifically data representations) at this age-appropriate level. We describe how these foundations might be laid down together early on so that they can later be integrated via the introduction of sensing and feedback technology. Concepts in movement and choreography are studied using words and bodies, as in traditional dance education, and later using computer simulations and motion capture. Data concepts are introduced first by appeal to general questions and later by specification to the movement of individual and collective joints and bodies.

关键词： dance analytics motion capture data science education

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the Design of Lightweight and Multi parallel CNN Accelerator Based on FPGA 8

The Design of Lightweight and Multi Parallel CNN Accelerator...

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IEEE 8th Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Li Zong-ling Wang Lu-yuan Yu Ji-yang Cheng Bo-wen Hao Liang China Acad Space Technol Beijing Inst Spacecraft Syst Engn Beijing 100094 Peoples R China

ISBN: (纸本)9781538681787

In this paper, a lightweight, parallel computing and storage, high energy efficiency CNN accelerator based on FPGA was designed. According to the characteristics of forward inference structure of deep convolution neural network, the parallel characteristics of convolution operation are analyzed from two aspects of computational efficiency and data reuse rate, and the full parallel pipeline implementation of full connection layer is studied. the accelerator has adopted full parallel pipeline structure to improve the computational efficiency, and making full use of various convolution parallel computing architectures to balance the requirements of computing efficiency, parameters and data load bandwidth, a lightweight and tightly coupled accelerator architecture is designed based on the characteristics of hardware resources such as internal computing, logic and storage in FPGA. In the validation test of open data set based on ImageNet, the accelerator is deployed on Xilinx Virtex7-690T FPGA with 28nm technology, under the working frequency of 200MHz, the maximum number of multiplication and addition operation is 1728 times per cycle, the peak operation rate is 691.2Gops, the energy ratio is 138.24Gops/w. In the end, the classification results are consistent with the processing results using double-precision floating-point data format.

关键词： Convolution neural network MobileNetV2 Model Lightweight parallel compute and storage High energy efficiency ratio FPGA

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Scale Drone Mapping on K8S: Auto-scale Drone Imagery Processing on Kubernetes-orchestrated On-premise Cloud-computing Platform 6

Scale Drone Mapping on K8S: Auto-scale Drone Imagery Process...

引用

6th international conference on Geographical Information Systems theory, Applications and Management (GISTAM)

作者： Vithlani, Hemang Narendra Dogotari, Marcel Lam, Olee Hoi Ying Pruem, Moritz Melville, Bethany Zimmer, Frank Becker, Rolf Rhine Waal Univ Appl Sci Fac Commun & Environm D-47475 Kamp Lintfort Germany Astron Environm Serv 129 Royal St East Perth WA 6004 Australia

ISBN: (纸本)9789897584251

Aerial images acquired using drone-based imaging sensors can be processed by photogrammetry toolkits to create geometrically corrected 2D orthophoto and/or 3D models. this is a crucial step for many of the ever-evolving civil applications of drones such as precision agriculture and surveying. Nevertheless, limited computational resources become bottleneck in providing these results quickly. Cloud computing helps in such scenarios because of its value-added features, namely virtualization, elasticity, high performance and distributed computing for the web-based image processing. the containerization approach plays a vital role in cloud computing by providing operational efficiency. Container orchestration engine, Kubernetes, not only provides template-based or GUI-based service deployment but also better monitoring, log querying and autoscaling. the present work displays a scalable photogrammetry service, deployed on a Kubernetes-orchestrated on-premise cluster. this reference implementation on Kubernetes enables the parallel processing of large datasets in less time than a single computer using the free and open-source toolkit OpenDroneMap.

关键词： Drone Imagery Opendronemap Photogrammetry Open-source Kubernetes Cloud computing

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Enabling Fast and Highly Effective FPGA Design Process Using the CAPI SNAP Framework 34th

Enabling Fast and Highly Effective FPGA Design Process Using...

引用

34th international conference on High Performance computing (ISC High Performance)

作者： Castellane, Alexandre Mesnet, Bruno OpenCAPI & CAPI Snap Enablement IBM France 1 Rue Vieille Poste F-34006 Montpellier France

ISBN: (纸本)9783030343569;9783030343552

the CAPI SNAP (Storage, network, and Analytics Programming) is an open source framework which enables C/C++ as well as FPGA programmers to quickly create FPGA-based accelerated computing that works on server host data, as well as data from storage, flash, Ethernet, or other connected resources. the SNAP framework is based on the IBM Coherent Accelerator Processor Interface (CAPI). From POWER8 with CAPI1.0, to POWER9 with CAPI2.0 and OpenCAPI, programmers can have access to a very simple framework to develop accelerated applications using high speed and very lowlatency interfaces to access an external FPGA. With SNAP, no specific hardware skill is required to port or develop an application and then accelerate it. Even more, a cloud environment is being offered as a cost effective, ready-to-use environment for a first-time right experience as well as a deeper development so that it can be achieved with very little investment.

关键词： Innovative hardware/software co-design Processor architecture Chip multiprocessors Custom and reconfigurable logic Solutions for parallel programming challenges parallel programming languages Libraries Models and notations Alternative and specialized parallel operating systems and runtime systems

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A Generic Flexible and Scalable Framework for Hierarchical parallelization of Population-Based Metaheuristics 20

A Generic Flexible and Scalable Framework for Hierarchical P...

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12th international conference on Management of Digital EcoSystems

作者： Khalloof, Hatem Mohammad, Mohammad Shahoud, Shadi Duepmeier, Clemens Hagenmeyer, Veit Karlsruhe Inst Technol IAI Karlsruhe Germany

ISBN: (纸本)9781450381154

Population-based metaheuristics -such as Evolutionary Algorithms (EAs)- are one of the most popular methods for solving highly complex and large-scale optimization problems. Nevertheless, finding an adequate solution with such approaches often requires computationally intensive fitness function evaluations especially in real-world applications. To speed up the computation, exploiting modern software techniques for parallelizing population-based metaheuristics on a cluster or a cloud is a viable approach. In the present paper, a generic, flexible and scalable framework for hierarchical hybridization of distributed population-based metaheuristics in a cluster environment is introduced. three lightweight technologies, namely microservices, container virtualization and the publish/subscribe messaging paradigm are used to develop this framework. the combination of these technologies enables easy hybridizations of different parallelization models of population-based metaheuristics, a full decoupling between services providing basic building blocks of the algorithm and a seamless deployment in a scalable runtime environment. For evaluation purposes, the EA GLEAM (General Learning Evolutionary Algorithm and Method) is exemplarily integrated into the framework and successfully deployed in a cluster environment. Scalability and applicability of the framework are explored by hybridizing the Coarse-Grained Model with the Global Model for solving the problem of unit commitment of distributed energy resources utilizing renewable energy generation. the results show that the new proposed framework introduces an excellent performance for scaling up the optimization speed of complex unit commitment optimization problems.

关键词： parallel EAs Hierarchical Model (Hybrid Model) Coarse-Grained Model (Island Model) Global Model (Master-Slave Model) Microservice Virtualization Container Scheduling Distributed Energy Resources parallel computing Scalability

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Detecting Uncertain BNN Outputs on FPGA Using Monte Carlo Dropout Sampling 29th

Detecting Uncertain BNN Outputs on FPGA Using Monte Carlo Dr...

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29th international conference on Artificial Neural networks (ICANN)

作者： Myojin, Tomoyuki Hashimoto, Shintaro Ishihama, Naoki Japan Aerosp Explorat Agcy Tsukuba Ibaraki Japan Hitachi Ltd Yokohama Kanagawa Japan

ISBN: (纸本)9783030616168;9783030616151

Monte Carlo dropout sampling (MC Dropout), which approximates a Bayesian Neural network, is useful for measuring the uncertainty in the output of a Deep Neural network (DNN). However, because it takes a long time to sample DNN's output for calculating its distribution, it is difficult to apply it to edge computing where resources are limited. thus, this research proposes a method of reducing a sampling time required for MC Dropout in edge computing by parallelizing the calculation circuit using FPGA. To apply MC dropout in an FPGA, this paper shows an efficient implementation by binarizing the neural network and simplifying dropout computation by pre-dropout and localizing parallel circuits. the proposed method was evaluated using the MNIST dataset and a dataset of satellite images of ships at sea captured. As a result, it was possible to reject approximately 60% of data which the model had not learned as "uncertain" on a classification identification problem of the image on an FPGA. Furthermore, for 20 units in parallel, the amount of increase in the circuit scale was only 2-3 times that of non-parallelized circuits. In terms of inference speed, parallelization of dropout circuits has achieved up to 3.62 times faster.

关键词： Deep learning FPGA Binarized neural network Uncertainty Monte Carlo dropout sampling SAR

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Proceedings of IA3 2020: 10th Workshop on Irregular Applications: Architectures and Algorithms, Held in conjunction with SC 2020: the international conference for High Performance computing, networking, Storage and Analysis

Proceedings of IA3 2020: 10th Workshop on Irregular Applicat...

引用

10th Workshop on Irregular Applications: Architectures and Algorithms, IA3 2020

ISBN: (纸本)9780738110905

the proceedings contain 8 papers. the topics discussed include: accelerating domain propagation: an efficient GPU-parallel algorithm over sparse matrices;parallelizing irregular computations for molecular docking;reducing queuing impact in irregular data streaming applications;supporting irregularity in throughput-oriented computing by SIMT-SIMD integration;DistDGL: distributed graph neural network training for billion-scale graphs;labeled triangle indexing for efficiency gains in distributed interactive subgraph search;distributed memory graph coloring algorithms for multiple GPU;and performance evaluation of the vectorizable binary search algorithms on an FPGA platform.

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A parallel Heterogeneous Computation Library for Embedded ARM Clusters 6

A Parallel Heterogeneous Computation Library for Embedded AR...

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6th IEEE international conference on Electronics, computing and Communication Technologies, CONECCT 2020

作者： Khatri, Nikhil Bagaria, Priya Sudarshan, T.S.B Pes University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781728168289

In recent years several hobbyists and research institutions have turned to ARM based cluster computers to meet their high performance computing needs. these systems are typically low cost, power efficient machines made of 4 or more nodes. However, current applications running on these machines are limited to the computational capabilities of the on-board CPUs. An attempt to fully utilise the available hardware is to use the on-board GPUs for general purpose computation. the challenge with this approach is the difficulty of programming for embedded ARM GPUs, which support a very limited programming interface such as OpenGL ES 2.0. In this paper, we present a system which allows a high level program to utilise multiple embedded ARM based GPUs in parallel to perform computation, without the application developer concerning themselves with the details of OpenGL and writing shaders. We describe the vector and matrix functions which we implement and provide results detailing their speedup. For matrix multiplication, our system shows a speedup of nearly 7X on the 8 nodes of the PHINEAS cluster computer. We further describe how these functions may be composited with some CPU functionality to implement inferencing for a Convolutional Neural network trained in Keras on a different machine. Our benchmarks demonstrate that the system's performance scales well across several nodes for a variety of tasks. Our work brings the benefits of GPGPU computing to an as yet untouched class of cluster computers. © 2020 IEEE.

关键词： Cluster computing

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Task scheduling of gpu cluster for large-scale data process with temperature constraint 8th

Task scheduling of gpu cluster for large-scale data process ...

引用

8th international conference on Computer Engineering and networks, CENet 2018

作者： Wang, Haifeng Cao, Yunpeng School of Information Science and Engineering Linyi University Institute of Linyi University of Shandong Provincial Key Laboratory of Network Based Intelligent Computing Linyi276000 China

ISBN: (纸本)9783030146795

With the development of GPU general-purpose computing, GPU heterogeneous cluster has become a widely used parallel processing solution for Large-scale data. Considering temperature management and controlling becomes a new research topic in high-performance computing field. A novel task scheduling model for GPU cluster with temperature limitation was built to balance the heat distribution and prevent the temperature hotspots occur. the scheduling index was introduced by combining the utilization of GPU and temperature. And the state matrix was designed to monitor the GPU cluster and provided status information for scheduler. When the temperature exceeds specific threshold value, the scheduler can improve the speed of fans to reduce the temperature. the experimental results show that the proposed scheduler can balance the heat distribution and prevent the temperature hotspots. Compared with the benchmark scheduling model, the loss of scheduling performance is in the acceptable range. © Springer Nature Switzerland AG 2020.

关键词： Temperature

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Model Checking as a Service using Dynamic Resource Scaling 27

Model Checking as a Service using Dynamic Resource Scaling

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27th IEEE international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Palavalasa, Surya Teja Singh, Yuvraj Singla, Adhish Purini, Suresh Choppella, Venkatesh Int Inst Informat Technol Hyderabad India

ISBN: (纸本)9781665422925

Model checking is now a standard technology for verifying large and complex systems. While there are a range of tools and techniques to verify various properties of a system under consideration, in this work, we restrict our attention to safety checking procedures using explicit state space generation. the necessary hardware resources required in this approach depends on the model complexity and the resulting state transition graph that gets generated. this cannot be estimated apriori. For reasonably realistic models, the available main memory in even high end servers may not be sufficient. Hence, we have to use distributed safety verification approaches on a cluster of nodes. However, the problem of estimating the minimum number of nodes in the cluster for the verification procedure to complete successfully remains unsolved. In this paper, we propose a dynamically scalable model checker using an actor based architecture. Using the proposed approach, an end user can invoke a model checker hosted on a cloud platform in a push button fashion. Our safety verification procedures automatically expands the cluster by requesting more virtual machines from the cloud provider. Finally, the user gets to pay only for the hardware resources he rented for the duration of the verification procedure. We refer to this as Model Checking as Service. We approach this problem by proposing an asynchronous algorithm for safety checking in actor framework. the actor based approach allows for scaling the resources on a need basis and redistributes the work load transparently through state migration. We tested our approach by developing a distributed version of SpinJA model checker using Akka actor framework. We conducted our experiments on Google Cloud Engine (GCE) platform wherein we scale our resources automatically using the GCE API. On large models such as anderson.8 from BEEM benchmark suite, our approach reduced the model checking cost in dollars by 8.6x while reducing the wall clock

关键词： Model Checking Cloud Native Horizontal Scaling Actor model Distributed Asynchronous computing

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