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Primeur weekly 2013-10-14

Special

Big Data analytics: a complex story of disruptive hype countered with converging technologies ...

Big Data forcing enterprises to look into the direction of HPC solutions ...

River no longer too deep between HPC and data intensive computing ...

HPC is HPC, and enterprise is enterprise, and never the twain shall meet? Can Big Data be the catalyst? ...

High Performance Data Analysis ecosystem to grow to more than $2 billion by 2016 ...

Focus

2013: Another year on the road to Exascale - An Interview with Thomas Sterling and Satoshi Matsuoka - Part III ...

The Cloud

Contrail project partners to release version R1.3 of the Contrail software ...

Fujitsu begins global packaged sales of "SPATIOWL" location data Cloud service ...

Dynamically managing network bandwidth in a Cloud ...

EuroFlash

The Transinsight Award for Semantic Intelligence goes to the "Wishart" team from the University of Alberta, Canada ...

Final Call for the HPCAC- ISC 2014 Student Cluster Competition Submission ...

CGG slashes development time with Allinea DDT ...

Bull launches GCOS7 V12 for its large mainframes ...

Bull launches Bull Optimal Database Booster, to optimize the performance of Oracle databases running on its Escala servers ...

Adept project: investigating energy efficiency in parallel technologies ...

DataDirect Networks' scalable, high-performance storage powers Wellcome Trust Sanger Institute's worldwide research efforts to reduce global health burden ...

The Human Brain Project has begun ...

Intel and Janssen Pharmaceutica to collaborate with imec and 5 Flemish universities to open ExaScience Life Lab ...

PRACE to showcase the principles of HPC at the European Union Contest for Young Scientists (EUCYS) ...

The 2013 Nobel Prize in Chemistry goes for multiscale models development ...

USFlash

Jack Dongarra receives high honour for supercomputing accomplishments ...

Cray enhances coprocessor and accelerator programming with support for OpenACC 2.0 ...

NCSA joins OpenSFS ...

Juniper Networks enables the discovery of new data insights in IBM Research Accelerated Discovery Lab ...

Louisiana State University researchers awarded nearly $1 million for Big Data research ...

Winchester Systems introduces FlashDisk RAID arrays with iSCSI 10Gb ...

High Performance Data Analysis ecosystem to grow to more than $2 billion by 2016


26 Sep 2013 Heidelberg - At ISC'13 Big Data, Steve Conway from IDC moderated a panel on the current state and future opportunities for Big Data. Steve Conway started off with a short presentation on the Big Data market. He presented the panel with some IDC insights to ignite the discussion. First, High Performance Data Analysis (HPDA) involves simulation and newer high-performance analytics: IDC predicts a fast growth from a small starting point. HPC and high-end commercial analytics are converging and algorithmic complexity is the common denominator. Currently, some economically important use cases are emerging but which ones will become attractive markets? No single HPC solution is best for all problems. Clusters with MR and Hadoop will handle most but not all the work. Steve Conway then confronted the panel participants with questions about Big data being a hype or a disruptive innovation, and whether we are on the road to convergence between HPC and enterprises. The panel moderator warned that IDC growth estimates might be conservative.

Steve Conway introduced the topic of High Performance Data Analysis by defining the different items. Performance needs HPC resources. It is often near-real time and involves intelligent questions that are algorithmically complex. There is a huge variety in data types: some are partitionable and are handled by clusters; others are non-partitionable and are addressed by tightly coupled systems. As far as the analysis is concerned, this is a process of search and discovery by means of modelling, simulation, analytics, and visualization. Analysis happens in all domains, in governmental, industrial, commercial and academic environments.

HPDA addresses tasks involving sufficient data volumes and algorithmic complexity to require HPC resources with established simulation or newer analytics methods. The data can be structured, unstructured or both showing regular or irregular patterns. IDC also observes upward extensions of commercial business problems. HPDA presents the accumulated results of iterative problem-solving methods, including stochastic and parametric modelling.

The different parameters are velocity, variety, volume and value. Steve Conway stated that HPC architectures today are compute-centric (FLOPS vs. IOPS).