Top 5 Blog Posts From 2015

Happy New Year! Check out some of the top blog posts from 2015: Why Do Better Forecasts Matter? Meteorologists have been striving to increase the accuracy of their work since the first weather forecasts were issued in the mid-19th century by pioneers such as Robert FitzRoy. In the 1950s and 1960s, the development of computers and observational technologies enabled forecast generation using the numerical weather prediction theories of Vilhelm Bjerknes and Lewis Fry Richardson. Today the numerical models that support our weather and climate forecasts are true “grand challenge” problems, requiring on the order of 10,000 billion (1016) mathematical operations and the exchange of 150 billion (1.5 x 1014) bytes of information to generate a ... [ Read More ]

Scratching an Itch: Mitigating HPC Scratch Bottlenecks

“I have a simple philosophy. Fill what's empty. Empty what's full. And scratch where it itches.” - Alice Roosevelt Longworth When one orchestrates HPC operations, scratch space often comes to the fore as a limiting factor that is often too small, big or slow. Many types of jobs — seismic, life sciences, financial services, etc. — don’t do all their work purely in memory. Each of many workers loads an initial configuration and reads/writes many intermediate points to scratch storage — often shared — before the total job completes. What can easily happen is that huge I/O problems replace huge computational problems as bottlenecks shift, and scratch management is a common issue. Traditional HPC systems often have local storage on each ... [ Read More ]

Controlling the Tide of Weather and Climate Data with Tiered Storage

There’s an old cliché that everyone talks about the weather, but no one does anything about it. While Cray can’t (yet) prevent droughts or cool off hot spells, we can help make the lives of weather professionals easier. An abundance of data riches, but where to store it? Weather and climate modeling centers strive to improve the accuracy of their models by gathering and assimilating more diverse input data and by increasing model resolution and complexity. As a result, these models are ingesting and producing ever-increasing volumes of data. These weather and climate organizations often find themselves challenged by the sheer volume of data, trying to manage various ways it may be used and simply trying to find the resources, ... [ Read More ]

Deepening Cray’s Involvement in Collaborative R&D and Codesign

Cray recently announced the birth of our new computing research organization for Europe, the Middle East and Africa (EMEA), the Cray EMEA Research Lab (CERL). Our investment in Europe is not new (the Cray®-1 and every machine since found a European home), but an explicit focus on research is a big and bold move for our company. I am very honored to be leading that change, and I will explain here what you can expect to see from CERL. Recent collaborations in EMEA For our current customers, this move is very welcome, but may not have been a huge surprise. That’s because during the last five years we have become more involved in deep research collaborations in EMEA. Most notably, Centers of Excellence at EPCC, HLRS and recently also at ... [ Read More ]

Algorithmic Trading: Faster Execution or Smarter Strategies?

The short answer is: You need both. Since the advent of the first high-frequency trading (HFT) firm, the quest for low-latency trading has been paramount. Strategies that were profitable before HFT are now obsolete. Among those strategies with questionable profitability today are: Arbitrage: Markets move too quickly to allow time for arbitrage. Market making: HFT imposes excessive risks on those traders. Event trading: Competing against HFT in terms of speed of response to scheduled economic reports and conventional news is impossible, since HFT systems can process and react to the information quicker. Faster execution is necessary to take advantage of short-term opportunities. Profitability is directly correlated to volume ... [ Read More ]