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parallel data processing approaches for effective intensive care units with internet of things

INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2019年第4期19卷 474-482页

作者： Manikandan, Narayanan Subha, Srinivasan VIT Univ Sch Informat Technol & Engn Vellore 632014 Tamil Nadu India

Computerisation in healthcare is more general and monitoring intensive care units (ICUs) is more significant and life critical. Accurate monitoring in ICU is essential. Failing to take the right decision at the right time may prove fatal. Similarly, a timely decision can save people's lives in various critical situations. In order to increase the accuracy and timeliness in ICU monitoring, two major technologies can be used, namely parallel processing through vectorisation of ICU data and data communication through internet of things (IoT). With our approach, we can improve efficiency and accuracy in data processing. This paper proposes a parallel decision tree algorithm in ICU data to take faster and accurate decisions on data selection. The uses of parallelised algorithms optimise the process of collecting a large set of patient information. Decision tree algorithm is used for examining and extracting knowledge-based data from large databases. Finalised information will be transferred to concerned medical experts in case of medical emergency using IoT. parallel implementation of decision tree algorithm is implemented with threads and output data is stored in local IoT table for further processing.

关键词： medical data processing internet of things IoT ICU data vectorisation multicore architecture parallel data processing

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parallel data processing With Magnonic Holographic Co-Processor

Parallel Data Processing With Magnonic Holographic Co-Proces...

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IEEE International Conference on Rebooting Computing (ICRC)

作者： Balynsky, M. Gutierrez, D. Chiang, H. Khitun, A. Kozhevnikov, A. Khivintsev, Y. Dudko, G. Filimonov, Y. Univ Calif Riverside Dept Elect & Comp Engn Riverside CA 92521 USA Russian Acad Sci Kotelnikov Inst Radioengn & Elect Saratov 410019 Russia

ISBN: (纸本)9781509013708

In this work, we present experimental data demonstrating the capabilities of Magnonic Holographic Co-Processor for parallel data processing. It is a type of magnetic logic device, which utilizes spin waves for data transfer and processing. Its operation is based on the correlation between the phases and the amplitudes of the input spin waves and the output inductive voltages. We present experimental data obtained for 8-terminal prototype based on Y3Fe2(FeO4)(3) structure. The input of the device is provided by the phased array of spin wave generating elements allowing us to produce input phase patterns of an arbitrary form. The obtained data demonstrate the capabilities of Magnonic Holographic Co-Processor for parallel data processing by using spin wave superposition. Potentially, magnonic holographic devices can be implemented as complementary logic units to digital processors. Physical limitations and technological constrains are also discussed.

关键词： spin waves holography parallel data processing

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A Locality-aware Cooperative Distributed Memory Caching for parallel data Analytic Applications 36

A Locality-aware Cooperative Distributed Memory Caching for ...

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36th IEEE International parallel and Distributed processing Symposium (IEEE IPDPS)

作者： Hung, Chia-Ting Chou, Jerry Chen, Ming-Hung Chung, I-Hsin Natl Tsing Hua Univ Comp Sci Dept Hsinchu Taiwan IBM TJ Watson Res Ctr New York NY USA

ISBN: (纸本)9781665497473

Memory caching has long been used to fill up the performance gap between processor and disk for reducing the data access time of data-intensive computations. Previous studies on caching mostly focus on optimizing the hit rate of a single machine. But in this paper, we argue that the caching decision of a distributed memory system should be performed in a cooperative manner for the parallel data analytic applications, which are commonly used by emerging technologies, such as Big data and AI (Artificial Intelligence), to perform data mining and sophisticated analytics on larger data volume in a shorter time. A parallel data analytic job consists of multiple parallel tasks. Hence, the completion time of a job is bounded by its slowest task, meaning that the job cannot benefit from caching until all inputs of its tasks are cached. To address the problem, we proposed a cooperative caching design that periodically rearranges the cache placement among nodes according to the data access pattern while taking the task dependency and network locality into account. Our approach is evaluated by a trace-driven simulator using both synthetic workload and real-world traces. The results show that we can reduce the average completion times up to 33% compared to a non-collaborative caching polices and 25% compared to other start-of-the-art collaborative caching policies.

关键词： parallel data processing Caching Algorithm Performance Distributed Systems

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Scheduling parallel data Transfers in Multi-tiered Persistent Storage 14th

Scheduling Parallel Data Transfers in Multi-tiered Persisten...

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14th Asian Conference on Intelligent Information and database Systems (ACIIDS)

作者： Nan Noon Noon Gettn, Janusz R. Xin, Tianbing Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia

ISBN: (纸本)9789811982330;9789811982347

Multi-tiered persistent storage provides a logical view where all available storage is distributed over a number of levels of different speeds and capacities. Efficient scheduling of parallel data transfers in multi-tiered persistent storage is a significant problem for pipelined data processing. This work considers a class of database applications implemented as sequences of operations that transfer data between persistent storage tiers. We show how to partition the sets of data transfers to reduce the number of conflicts when data transfers are performed in parallel. The paper proposes the new rule-based algorithms for allocating parallel data transfer to the processors to minimize total processing time. The new algorithms evenly distribute the workload among the processors and reduce their idle times. We describe a number of experiments that validate the efficiency of parallel data transfer plans generated by the algorithms presented in the paper.

关键词： Multi-tiered persistent storage Scheduling parallel data processing Performance tuning database management systems

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IDIOMS: Index-powered Distributed Object-centric Metadata Search for Scientific data Management 24

IDIOMS: Index-powered Distributed Object-centric Metadata Se...

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24th IEEE/ACM International Symposium on Cluster, Cloud, and Internet Computing (CCGrid)

作者： Zhang, Wei Tang, Houjun Byna, Suren Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Ohio State Univ Columbus OH USA

ISBN: (纸本)9798350395679;9798350395662

Affix-oriented metadata search is one of the essential fuzzy search capabilities that allow users to find data of interest in their voluminous data set with incomplete query conditions. With the recent transition towards object-centric data management systems in the science community, there is a paramount need for the support of such features in distributed settings. However, existing metadata search solutions either do not support efficient affix-oriented metadata search or do not suit well in a distributed setting of object-centric data management systems. To bridge this gap, we introduce IDIOMS, a metadata search solution underpinned by a distributed metadata index, meticulously designed to enable high-performance affix-oriented metadata search for parallel object-centric storage. One of the standout features of IDIOMS is its efficiency in supporting four distinct types of highly demanded metadata queries. Furthermore, IDIOMS is flexibly catering to both independent and collective metadata search operations. Our experimental comparisons with SoMeta, a state-of-the-art metadata query method, demonstrate more than 400x performance boost for independent queries and up to 300x performance improvements for collective queries, while keeping a small index management overhead.

关键词： Metadata Search Metadata Management parallel data processing Distributed data Management Scientific data Management

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Threshold-Variation-Tolerant Coupling-Gate α-IGZO Synaptic Transistor for More Reliably Controllable Hardware Neuromorphic System

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IEEE ACCESS 2021年 9卷 59345-59352页

作者： Kang, Dongyeon Jang, Jun Tae Park, Shinyoung Ansari, Md. Hasan Raza Bae, Jong-Ho Choi, Sung-Jin Kim, Dong Myong Kim, Changwook Cho, Seongjae Kim, Dae Hwan Kookmin Univ Sch Elect Engn Seoul 02707 South Korea Kookmin Univ Circadian ICT Res Ctr Seoul 02707 South Korea Gachon Univ Dept Elect Engn Seongnam Si 13120 South Korea

Hardware-oriented neuromorphic computing is gaining great deal of interest for highly parallel data processing and superb energy efficiency, as the candidate for replacement of conventional von Neumann computing. In this work, a novel synaptic transistor constructing the neuromorphic system is proposed, fabricated, and characterized. Amorphous indium-gallium-zinc-oxide (alpha-IGZO) and Al2O3 are introduced as the channel and gate dielectric materials, respectively. Along with the high functionality and low-temperature processing viability, geometric peculiarity featuring extended gate structure improves the performances of the proposed transistor as synaptic component in the neuromorphic system. The insight into the substantial effect of optimal device structure design on energy efficiency is highlighted.

关键词： Hardware-oriented neuromorphic computing parallel data processing energy efficiency synaptic transistor amorphous indium-gallium-zinc-oxide extended gate device structure design

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Software and Hardware Complex for Calculating Convolutions by Methods Multidimensional Matrix Algebra

Software and Hardware Complex for Calculating Convolutions b...

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IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Goncharov, Evgeniy Munerman, Victor Yakovlev, Georgiy Smolensk State Univ SmolSU Phys & Math Dept Smolensk Russia

ISBN: (纸本)9781665404761

The article contains description of the major types of convolutions and algebra of multidimensional matrices. It is shown that all major types of convolutions have been expressed in terms of the algebra of multidimensional matrices. Examples are given to illustrate this fact. It is shown how the operation of the multiplication of multidimensional matrices is parallelized using GPGPU. Considering this fact, the idea of an SHC implementing MMA is proposed for calculating convolutions.

关键词： multidimensional matrix algebra software and hardware complex convolution (lambda, mu)-convolution product parallel data processing GPGPU

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CASH: A Credit Aware Scheduling for Public Cloud Platforms 21

CASH: A Credit Aware Scheduling for Public Cloud Platforms

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21st IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Sharma, Aakash Dhakshinamurthy, Saravanan Kesidis, George Das, Chita R. Penn State Univ Comp Sci & Engn University Pk PA 16802 USA Facebook Inc Menlo Pk CA USA

ISBN: (纸本)9781728195865

Distributed data processing frameworks such as Hadoop, Tez, Spark, and Flink are exclusively used by public cloud tenants for executing large scale data analytics applications in various domains including but not limited to content management, financial sector, healthcare etc. These frameworks slice a job into a number of smaller tasks, which are then executed by a job scheduler on a multi-node compute cluster. While making scheduling decisions, the State-of-art schedulers employed in these frameworks assume hardware resources such as CPU, disk I/O and network I/O to offer a fixed service rate. However, in a public cloud environment, many of these resources are associated with burstable service rates. More specifically, the resources offer a guaranteed baseline service rate with an option to burst above their baseline rate by expending accumulated burst credits. Being unaware about this underlying hardware burstability, schedulers tend to make sub-optimal task placement decisions, thereby adversely affecting the job completion times, leading to higher deployment costs. In this paper, we propose CASH, a burst credit aware scheduler, which is cognizant about the burst credits associated with the individual hardware resources in the public cloud cluster. Through coarse grained task annotations depicting the burst credit demand of individual tasks and dynamically monitoring the credits for the underlying resources, CASH performs optimal task placement decisions. We prototype CASH on YARN, Hadoop, and Tez, and extensively evaluate it using both batch and streaming workloads. Our experimental results with CASH show CPU-credit based instances, like AWS T3, are a viable cost effective alternative when compared to self-managed offerings like Amazon EMR, for running large scale batch workloads. Furthermore, we demonstrate that CASH can accelerate streaming SQL queries on a large Hive database by up to 39.4%, leading to public cloud cost savings by up to 22%.

关键词： Public Cloud Burst Credits Cluster Scheduling parallel data processing Cost Savings

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Mode-Oriented Permutation Cipher Encryption and Passive Signal Switching Based on Multiobjective Optimized Silicon Subwavelength Metastructures

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ACS PHOTONICS 2020年第8期7卷 2163-2172页

作者： Jia, Hao Yang, Shanglin Zhou, Ting Zhang, Lei Wang, Tao Chen, Haoxiang Yang, Jianhong Yang, Lin Lanzhou Univ Sch Phys Sci & Technol Lanzhou 730000 Gansu Peoples R China Chinese Acad Sci Inst Semicond State Key Lab Integrated Optoelect Beijing 100083 Peoples R China Univ Chinese Acad Sci Ctr Mat Sci & Optoelect Engn Beijing Peoples R China

The rapid development of silicon photonics provides the possibility for various applications. In this paper, we utilize silicon subwavelength metastructures as hardware solutions for permutation cipher and signal switching. Based on the intrinsic similarities of behavior at the level of the mathematical model, we use the physical parameters of optical systems as carriers to link the feature parameters of the problems. By manipulating the evolution of optical parameters through a device, we can solve these issues in the optical field. The orthogonal mode is widely used in multimode systems. We mapped the sequence of sliced messages and signal channels to the mode orders, thus, the permutation cipher encryption, and passive signal switching problems can be refined to the arbitrary transformation of mode orders. We propose a structure fulfilling the functionality and implement the multiobjective inverse-design method to optimize the device. As a proof of concept, we adopt four modes and realize the mode transformation of TE0, TE1, TE2, and TE3 to TE2, TE0, TE3, and TE1 at a footprint cost of 4 mu m x 3.4 mu m. The simulated insertion losses are lower than 1.7 dB at 1525-1565 nm. The measured insertion losses for the fabricated device are lower than 3.1 dB. Then we demonstrate the permutation cipher encryption with a 12.5 Gbps message-rate and the passive signal switching with 40 Gbps OOK and 32G Baud PAM-4 signals. The methodology can fully release the advantages and potentials of subwavelength structures and provide a powerful tool for solving interdisciplinary problems via optics.

关键词： integrated optics silicon photonics optical metastructure parallel data processing multiplexing

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P4: Portable parallel processing Pipelines for Interactive Information Visualization

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2020年第3期26卷 1548-1561页

作者： Li, Jianping Kelvin Ma, Kwan-Liu Univ Calif Davis Dept Comp Sci Davis CA 95616 USA

We present P4, an information visualization toolkit that combines declarative design specification and GPU computing for building high-performance interactive systems. Most of the existing information visualization toolkits do not harness the power of parallel processors in today's mainstream computers. P4 leverages GPU computing to accelerate both data processing and visualization rendering for interactive visualization applications. P4's programming interface offers a declarative visualization grammar for rapid specifications of data transformations, visual encodings, and interactions. By simplifying the development of GPU-accelerated visualization systems while supporting a high degree of flexibility and customization for design specification, P4 narrows the gap between expressiveness and scalability in information visualization toolkits. Through a range of examples and benchmark tests, we demonstrate that P4 provides high efficiency for creating interactive visualizations and offers drastic performance improvement over current state-of-the-art toolkits.

关键词： data visualization Graphics processing units Visualization Programming Grammar Rendering (computer graphics) data models information visualization interactive visualization parallel data processing GPU computing data exploration

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