PPT-High Performance Processing of Streaming Data

Author : olivia-moreira | Published Date : 2017-12-18

Workshops on Dynamic Data Driven Applications SystemsDDDAS In conjunction with 22nd International Conference on High Performance Computing HiPC Bengaluru

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High Performance Processing of Streaming Data: Transcript

Workshops on Dynamic Data Driven Applications SystemsDDDAS In conjunction with 22nd International Conference on High Performance Computing HiPC Bengaluru India 12162015. Large-scale near-real-time stream processing. Tathagata . Das (TD). along with. Matei. . Zaharia. , . Haoyuan. Li, Timothy Hunter, Scott . Shenker. , Ion . Stoica. , and many others. UC BERKELEY. What is . Session at . Silicon India. Rajgopal Kishore. Vice President and Global Head of BI & Analytics, . HCL Technologies. rkishore@hcl.in. rkishore9@gmail.com. . State of data. Challenges. Need of the day. multi-room audio hi- streaming high-performance home theater More music. More variety. multimedia gaming High-end active loudspeaker Excite X14A Excite X14A – smart, compact, active. Excellen (and Stream Processing). Aditya Akella. Resilient Distributed . Datasets (NSDI 2012). A Fault-Tolerant Abstraction for. In-Memory Cluster Computing. Piccolo (OSDI 2010). Building Fast, Distributed Programs with Partitioned Tables. Multicore. Architecture. Michael . Gschwind. , et al.. Presented by: . Jia. . Zou. CS258. 3/5/08. Goal for Cell. Increase processor efficiency for most performance per area. Reduce area per core, have more core in a given chip are. Introduction . Apache Storm is a real-time fault-tolerant and distributed Stream Processing Engine.. Open Sourced September 19. th. 2011. Main languages-. Clojure. and the JAVA.. Some of the Characteristics of storm are Fast, Scalable, Fault-tolerant, Reliable and Easy to operate.. By. Nirvan Sagar – 14563364. Srishti. . Ganjoo. – 53526280. Syed . Shahbaaz. . Safir. - 64882986. Introduction. Increasing consumer demand for streaming of high definition (HD) content has led to the need for resilient, fault tolerant, and high bandwidth connectivity.. Data in the Cloud. AFOSR FA9550-13-1-0225: Cloud-Based Perception and Control of Sensor Nets and Robot Swarms . 01/27/2016. 1. Geoffrey Fox, David . Crandall,. Supun. . Kamburugamuve. , . Jangwon. Lee, . NSF 1443054: CIF21 DIBBs: Middleware and High Performance Analytics Libraries for Scalable Data Science. Big Data . Use Cases February 2017. 1. Application Nexus of HPC, Big Data, Simulation Convergence. Theory and Practice with Apache Flink at Uber. QCon.ai, . San Francisco. April, 11th 2018. Fabian . Hueske. Shuyi Chen. What is Apache Flink?. 2. Batch Processing. process static and. historic data. Twister2 for BDEC2 https://twister2.gitbook.io/twister2/ Poznan, Poland Geoffrey Fox, May 15 , 2019 gcf@indiana.edu , http://www.dsc.soic.indiana.edu/ , http://spidal.org / 1 5/10/2019 Digital Science Center E. nvironment for . R. eal-time . S. treaming, . A. cquisition and . P. rocessing . FBP based, reactive data-stream processing framework. V. Gyurjyan, G. . Heyes. , D. Lawrence, C. Timmer, N. Brey, C. Cuevas, D. Abbott, B. . Session at . Silicon India. Rajgopal Kishore. Vice President and Global Head of BI & Analytics, . HCL Technologies. rkishore@hcl.in. rkishore9@gmail.com. . State of data. Challenges. Need of the day. Architecture for Real-Time Data Compression. 05/10/2023. European Data Handling & Data Processing Conference. Samuel Torres Fau. , Antonio J. Sánchez, . Yubal. Barrios and . Roberto Sarmiento. 5.

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