Category Archives: Ideas

Cost: The Biggest Pothole on the Exascale Road

Posted on May 31, 2014 by Konstantin S. Solnushkin

Recently, industry analyst John Barr wrote at the ISC blog about “Potholes on the Road to Exascale”. John speaks about a unified programming environment that should be able to support all sorts of computing devices of the future. That’s right: … Continue reading →

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Hungarian Goulash and Parallel Cooking

Posted on February 28, 2014 by Konstantin S. Solnushkin

Most cooking recipes are formulated improperly: they list actions required to prepare food, but fail to explicitly mention which of those actions can be run in parallel. This leaves you guessing how much time you could save if you added … Continue reading →

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Many-core and InfiniBand: Making Your Own CPUs Gives You Independence

Posted on July 2, 2013 by Konstantin S. Solnushkin

The headline sounds like the obvious thing: of course, if you can make your own CPUs for your projects, then you don’t have to rely on CPU manufacturers. “But wait”, you would ask, “Aren’t CPU design and manufacture very expensive?” … Continue reading →

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Clusters: A Disruptive Technology in the Supercomputing World

Posted on May 31, 2013 by Konstantin S. Solnushkin

I recently completed my first Coursera course — “Surviving Disruptive Technologies” by Hank C. Lucas, Jr., Ph.D. and his staff. The course was an eye-opener: it gave enough examples to prove that sometimes companies do odd things and even go … Continue reading →

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Fruits of Computing: Redefining “Green” in HPC Energy Usage

Posted on August 30, 2012 by Konstantin S. Solnushkin

Suppose you are visiting a supercomputing centre for several days. Each day you submit your compute jobs and then go to a nearby tropical garden. While your jobs are being computed, you enjoy fresh tropical fruit, listen to exotic birds … Continue reading →

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Marrying NVIDIA Tesla and InfiniBand?

Posted on July 18, 2012 by Konstantin S. Solnushkin

My friend is working on a research project dedicated to many-core architectures, such as NVIDIA’s GPUs or Intel’s Xeon Phi, that have lots of simple cores best suited to straightforward computations. Sometimes those cores need to communicate with their neighbours … Continue reading →

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