From Grand Challenge Applications to Critical Workflows for Exascale | Part I


(This is the first in a series of three posts. The second will discuss critical workflows in oil and gas and the life sciences while the last will speculate about machine learning techniques to optimize such workflows). A bit of background In the late ’80s, U.S. federal government agencies became convinced that a substantial effort to fund R&D in high performance computing (HPC) was required to address so-called “grand challenges” — fundamental problems in science and engineering that are ambitious (requiring some advances in science and technology) but achievable. In 1992, the Office of Science and Technology Policy (OST) released a recommendation proposing investments in HPC systems, technology and algorithms, a national ... [ Read More ]

[Infographic] The Emergence of Analytics & Big Data in Baseball


As the economics of baseball have changed with higher salaries and increased player activity, along with public interest in game information, we have advanced from hand-coded historical models to the real-time capture of every movement and action in data collection and the use of advanced analytics have evolved from a part-time hobby of a few die-hard fans into a major business, one where advanced big data technology will become a necessity and not a rarity. Take a look at the infographic below to take a stroll through the history of analytics and big data in baseball.\   ... [ Read More ]

Cray’s Innovative Analytics System Named “Best of Show”


At last week’s Bio-IT Conference & Expo, Cray’s Urika-XA™ extreme analytics platform was selected as Best of Show in the IT Infrastructure and Hardware category. For Cray, this recognition was a validation of the work we’ve undertaken in recent years to leverage our experience and expertise in supercomputing and analytics and apply it to the unique challenges of the life sciences industry. I’d like to highlight the three reasons I believe we were selected. First, for IT organizations in the life sciences, the struggle to address two corporate imperatives — reducing the costs of computing while enabling the timely delivery of results from complex computing tasks — creates an almost overwhelming challenge. For many companies, a shift ... [ Read More ]

Thinking Out of the (Music) Box for Creative Data Analytics


Among a host of other achievements, the multi-talented David Byrne wrote a great book. You may know Byrne as the cofounder and creative force behind the American rock band Talking Heads. His most recent book, How Music Works,  is a brilliant and insightful look at “how music works, or doesn’t work,” as a phenomenon that is inextricably part of its environment. “How Music Works” begins with the revelation of Byrne’s acknowledging his “extremely slow-dawning insight about creation,” to wit:  “…[C]ontext largely determines what is written, painted, sculpted, sung, or performed.” He adds that, consciously or unconsciously, we inevitably create in reverse, in a sense working backward to produce work that fits the context into which it is ... [ Read More ]

Scale Out Analytics, Not People


We’ve been living in a scale-out world for analytic workloads during the last handful of years. MPP data warehouses, clustered NoSQL, Hadoop® and Spark™, falling collectively under the big data umbrella, all readily come to mind. When better performance, support for a greater number of users or increased capacity is required, we scale out with server nodes comprising compute, memory and storage resources. But unless you operate Amazon Mechanical Turk, scaling the number of people as analytic needs grow is something to be avoided. One topic ESG analyst Nik Rouda will discuss in our joint webinar, How to Realize Analytics Value Faster, is current research showing how few organizations are planning significant growth in IT staff. This is ... [ Read More ]