Posts

It has been a greet meeting. Lots of interesting ideas and a lot to explore from now on. Just what I like :D. I summarized below the list of post I make related to the meeting. Introductory post [Data-Intensive Scalable Computing. Randy Bryant, CMU](/posts/data-intensive-scalable-computing-randy-bryant.md" >}}) Text Information Management: Challenges and Opportunities. ChengXiang Zhai, UIUC Clouds and ManyCore: The Revolution. Dan Reed, MSR Computational Paradigms for Genomic Medicine. Jill Mesirov, Broad Institute of MIT and Harvard Simplicity and Complexity in Data Systems (Garth Gibson) Handling Large Datasets at Google: Current Systems and Future Directions....

I was lucky to attend the Big Data Computing Study Group 2008. The line of speaker is impressive. The event was held at Yahoo! Sunnyvale, and Thomas Kwan (UIUC alumni know at Yahoo!) helped organize it. I blogged about it on my DITA blog where you can find links to all the related posts.

NSF listens at you academics. Jeannete opens the floor with this claim. Questions: What are the limitations of this modeling paradigm (data-intensive one)? What are meaningful metrics of performance here? What about security processes and data on a shared resource? How can we reduce power consumption? Can this parading problem not possible otherwise, or simplify them, or open the door to new applications? NSF rolling out cluster exploratory program, also going to roll out a new solicitation for Data-Intensive Computing....

The next speaker of the afternoon is Phil Gibbons from Intel Research. Intel has created a research theme on data-rich computing for the next few years (same as the other one presented on the Hadoop summit about ground modeling). An approach, bring the computation to the data (cluster approach), but there are also two elements in the picture: (1) memory hierarchy issues, and (2) pervasive multimedia sensing. The first one is in important because for pure performance, the second one keeps forcing pushing the computation closer to the sensors....

Alex is opening the talk showing a clear exponential growth in Astronomy (LSST and the petabyte example generation). Data generated from sensors keep growing like crazy. Images have more and more resolution. Hopkin’s databases started with the digital sky initiative, with generated 3 terabytes of data in the 90’s, and the number keep growing up to the point of LSST which will be forced to dump images because is not possible to store all of them....