Big data meets high performance computing july 28 2014. Shivnath babu, supervisor je rey chase sandeep uttamchandani jun yang an abstract of a dissertation submitted in partial ful llment of the requirements for. Experts from academia, research laboratories and private industry address both theory and application. Credits part of the course material is based on slides provided by the following authors. Merge or merging is the process of taking two or more groups of data and combining them into a single unified set.
Jianwu wang, daniel crawl, ilkay altintas, weizhong li. San diego supercomputer center san diego supercomputer center. The complexity of their reading environment is affecting their productivity, leading to frustration, disjointed workflow. Introduction abstractions src code conclusion crossplatform querying crossplatform coding comparison to sawzall in comparison, sawzall has. Analysis and optimization of massive data processing on high performance computing architecture he huang, shanshan li, xiaodong yi, feng zhang, xiangke liao and pan dong. Mapreduce, cloud computing, data intensive, cost model, io interference, io behavior 1. Integrated management of the persistentstorage and data. Data intensive computing, cloud computing, and multicore computing are converging as frontiers to address massive data problems with hybrid programming models andor runtimes including mapreduce, mpi, and parallel threading on multicore platforms. Merge excel data into pdf form pdf forms acrobat users. Data intensive computing handout 10 spark according to homepage.
In 2009 sum bought by oracle oracle grid engine, no longer opensource. More advanced merging commands and programs are capable of only merging data that is new or updated to a file. In this chapter, we define dataintensive computing, identify the challenges of massive data, outline solutions for hardware, software, and analytics, and discuss. Mutable state 12 this work is licensed under a creative commons attributionnoncommercialshare alike 3. Large memory node usage when large problems cannot be solved in parallel 2. Dataintensive technologies for cloud computing springerlink. Gpuaccelerated cloud computing for dataintensive applications 107 2 background and related work 2. Do you mean a mail mergetype operation creating a new pdf for each row in the spreadsheet, or just importing a single set of values from excel into a pdf. Integrated management of the persistentstorage and dataprocessing layers in dataintensive computing systems by nedyalko borisov department of computer science duke university date. From mapreduce to spark 12 this work is licensed under a creative commons attributionnoncommercialshare alike 3. This course is a tour through various research topics in distributed dataintensive computing, covering topics in cluster computing, grid computing, supercomputing, and cloud computing. A major challenge is to utilize these technologies and.
Msst tutorial on dataintesive scalable computing for. Data intensive computing demands a fundamentally different set of principles than mainstream computing. Analyzing relational data 23 this work is licensed under a creative commons attributionnoncommercialshare alike 3. No map combine map combine map data cache yes hybrid scheduling of the new iteration job start. Cloud computing reduces cost of infrastructure maintenance and acquisition. Data intensive computing handout 1 sun grid engine and others since 2001, opensource.
Accelerating external sorting via onthefly data merge in. Datacomputeintensive problems combine the need to process very large. Data intensive computing on stampede niall gaffney. The open data movement, advocated by many governments and nonprofits, makes an effort to formalize and standardize methods for placing useful datasets into as many hands as can potentially make use of it. Lectures on p2p overlays given as a part of the course distributed computing, peertopeer and grids, kth royal institute of technology, stockholm, sweden. Dataintensive computing systems hadoop universtity of verona computer science department damiano carra 2 acknowledgements. Dataintensive function merge resultsprocessing modules. Submits a job for execution b yn binary or a script. Janguk in, sanjay ranka, paul avery, laukik chitnis. Dataintensive computing is a class of parallel computing applications which use a data.
Data intensive computing, swedish institute of computer science sics, stockholm, sweden. Computing applications which devote most of their execution time to computational requirements are deemed compute intensive, whereas computing applications which require large. Dataintensive scalable computing architecture clusters with many computers designed for dataintensive operations leverage opensource dataintensive frameworks. Dataintensive computing is a class of parallel computing applications which use a data parallel approach to process large volumes of data typically terabytes or petabytes in size and typically referred to as big data. Spark is available either in scala and therefore in. Accelerating external sorting via onthefly data merge in active ssds youngsik lee, seungryoul maeng kaist luis cavazos quero, youngjae lee, jinsoo kim sungkyunkwan university hotstorage 14 june 18, 2014. Our online tool combines multiple files into one single pdf. My application form is already a pdf document but need to create a mail merge using data from excel and merge into the pdf document. In contrast to generalpurpose computing, the regularity of data. In summary, big data has long been an important part of high performance computing but recent technology advances, coupled with massive volumes of data and innovative new use cases have resulted in data intensive computing becoming even more valuable for solving scientific and commercial technical computing problems. Dataintensive computing the fourth paradigm history of escience big data in science introduction to databases relational databases, acid indexing introduction to sql user defined functions hardware architectures storage hierarchy nature of low level io redundant storage, raid, erasure codes networking issues. With the immense growth of the mobile consumer electronics market, data intensive computing is present in billions of devices. Beside pdf we do support nearly any other input format, such as docx, jpg or png. Technologies like scientific workflows and dataintensive computing.
Modeling io interference for data intensive distributed. However, there are two scriptfree solutions to prepare uniquely named individual pdf records, provided you dont mind merging to a new indesign file first. It is possible to merge the two and try to produce environments that have the performance of hpc and the usability and flexibility of the commodity big data stack, says fox. Big data applications using workflows for data parallel computing. Merge excel data into pdf form solutions experts exchange. Introduction the need to process and analyze large volumes of data is still increasing. Data intensive computing systems duke computer science. Analysis and optimization of massive data processing on. We will explore solutions and learn design principles for building large networkbased computational systems to support data intensive computing. Data in many forms structured and unstructured data text, numbers, and pixels data uncertainty inconsistent, incomplete, ambiguous, and approximated data ig data is defined by im as any data that cannot be captured, managed andor processed using traditional data. Next, build a retrieval application, choosing the merge data to pdf template. Msst tutorial on dataintesive scalable computing for science september 08 hadoop goals scalable petabytes 1015 bytes of data on thousands on nodes. Apache spark is a fast and general engine for largescale data processing. A scheduling middleware for data intensive applications on a grid richard cavanaugh university of florida collaborators.
Dataintensiveness is the main driving force behind the growth of the cloud concept cloud computing is necessary to address the scale and other issues of dataintensive computing cloud is turning computing into an everyday gadget women are indeed experts at. Introduction dataintensive computing processing large volumes of data vast io web, sns, ecommerce, scientific analysis. Data sheet merge unity pacs an allinone diagnostic imaging and information management solution physicians today are juggling multiple platforms to read clinical exams, impacting their focus and reducing their efficiency. Handbook of data intensive computing is written by leading international experts in the field. Data sharing is often framed in terms of these norms of open data, the unrestricted sharing of data with anyone. Sadly, indesign cc 2014 still does not provide an option to export a datamerged pdf directly to individual records. Beyond cmos and beyond vonneumann workshop on memristive systems for space applications european space agency, estec 30 april 2015, noordwijk, netherlands. Data acquisition is concerned with making the required input data available.
Simmons, and carsten varming abstractions for data intensive computing. Pdf the deluge of data that future applications must processin domains ranging from science to business informaticscreates a compelling argument for. The goal is to successfully bring the two data intensive computing paradigms together to share the developments versus reinvent the wheel on either side. Oftentimes mapreduce is used to process the big data in cloud. The merge operation is extremely powerful and makes it easy to construct typical patterns of communication such as. Dataintensive computing platforms typically use a parallel computing approach combining. Pdf chapter 1 applications in dataintensive computing. Pdf big data applications using workflows for data. Dataintensive applications, challenges, techniques and technologies. Two major opensource forks, one of them son of grid engine still active. Generic merging as with the msdos copy command takes one or more files and combines them into one file. Exponential growth of sequence data unstoppable growth of microarray data new petabytes of data set from cell imaging technology i am terrified by terabytes anonymous i am petrified by petabytes jim gray technology innovation moores law computing capacity doubles every 18 months. Computing applications which devote most of their execution time to computational requirements are deemed computeintensive, whereas computing applications which require large.
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