New Mathematics Advance Macromolecular Imaging
In order to visualize the structure of proteins in their native environment, scientists can blast powerful X-ray beams at tiny volumes of proteins in solution. The resulting diffraction patterns can then be interpreted to determine, or reconstruct, information about the protein’s molecular structure. However, traditional solution scattering techniques are often limited by how much structural detail they can extract.
In a paper published August 3, 2015 in the Proceedings of the National Academy of Sciences, Lawrence Berkeley National Laboratory (Berkeley Lab) applied mathematicians Jeffrey Donatelli and James Sethian, and physical bioscientist Peter Zwart have introduced new mathematical theory and an algorithm, which they call "Multi-tiered iterative phasing (M-TIP)," to solve the reconstruction problem from FXS data. Their code can quickly determine general structure in only a few minutes on a desktop computer. The approach is an important step towards new advances in biophysics and offers the prospect for new tools to help solve some of the most challenging problems in the life sciences. »Read more.
Register Now: Python Boot Camp Only Nine Days Away
Don't miss your chance to grab a seat at Software Carpentry's popular Python Boot Camp. To be held at Berkeley Lab from August 19 - 21, the camp is designed to familiarize programmers of other languages (like C, Java, FORTRAN, and Lisp) with the basics of Python. In addition to knowing a different computer language, attendees should understand basic computer science concepts (such as looping, recursion, pointers, etc.) as a foundation for the boot camp curriculum. Camp is held 8:30am to 5:00pm each day and consists of a mixture of formal lectures, in-class demos, coding breakout sessions and homework projects. It is open to anyone within the UC Berkeley and Berkeley Lab community. There is a nominal fee (about $20) for attendance. There will be a limited number of slots open to industry participants. »Learn more. »Apply.
Computing Sciences Staff Help East Bay High Schoolers Upgrade Their Summer
To help prepare students from underrepresented groups learn about careers in a variety of IT fields, the Laney College Computer Information Systems Department offered its Upgrade: Computer Science Program. Thirty-eight students from 10 East Bay high schools registered for the eight-week program, which included presentations by Eli Dart of ESnet and Elizabeth Bautista of NERSC, as well as a tour of the NERSC Center. The lab’s participation grew out of a career pathways workshop where Jon Bashor of Computing Sciences met Laney College’s Johnnie Williams, who developed and led the program. »Read more.
This Week's CS Seminars
Fast Data Analysis Framework for Scientific Big Data Applications
Monday, August 10, 9:30 – 10:30 am, Rm. 238, NERSC, OSF
Jialin Liu, Texas Tech University
Scientific breakthroughs are increasingly powered by advanced computing and data analysis capabilities. The data-driven scientific discovery has become the new fourth paradigm of scientific innovation after theory, experiment, and simulation driven innovations. The data-driven scientific discovery is based upon advanced high performance computing (HPC) that traditionally powers simulation driven research and further requires processing massive amounts of datasets.
Revealing and exploring the interesting knowledge hidden inside scientific datasets faces critical challenges and the problem is beyond the capability of traditional HPC software systems. Not only the existing data and computing model, but also the runtime and the storage architecture in the HPC, need to be revisited to meet the "big data" challenges.
The fundamental issue is the data movement that often dominates the overall analysis performance and the execution time. To optimize and speedup the discovery process, this dissertation research studies the scientific workflow and designs a Fast Data Analysis Framework, that builds a top-down high performance computing framework with a focus on reducing the data movement and boosting the scientific big data processing. This framework has a newly designed Statistical Data model with integrated statistics and subsetting schemes to speedup the query analysis. The framework also has an In-Advance Computing model, which is designed to better support generic scientific analysis routines. This computing model has a flexible two-level design, with a coarse-grain level that performs optimizations at the analysis operation level and a fine-grain level that moves computations in-advance to produce partial analysis results on the incomplete I/O (input/output) streams. The two-level model can work independently and coherently to utilize computation in-advance to reduce the required data movement. The framework further supports a Hierarchical Runtime Scheduling, which considers both the storage side I/O queues and the client side data redistribution, and an Automatic Storage Reorganization, which allows maintaining multiple data layouts and automatically redirecting the I/O to better layouts.
These designs and developments provide novel algorithms to optimize the data movement in both runtime and file systems. The evaluation results confirm that the Fast Data Analysis framework improved and optimized scientific big data applications. It can have an impact on the design and development of future infrastructures, algorithms, and systems for data-driven scientific discovery paradigm.
Link of the Week: Sneaky Facebook Users Fess Up
A survey of 2,000 Facebook users found that many were using the social network to a degree that made them worry their behavior was becoming compulsive. In the survey Facebook users admitted to using the application in the shower, on the toilet and even excusing themselves from a dinner out because they just couldn't stand the thought of missing a funny cat video or a post about what someone else was eating for dinner. Oh, and if this sounds like you, the site that published these (somewhat self-serving) revelations has an app for you: It allows you to block other distracting apps that might be keeping you from having an f2f life, including Facebook. »Read more.