One compelling reason to use Star-P is greatly improved productivity. Something that came as a surprise to us is the tremendous interest in Star-P internationally. Initially, I believed that the largest interest would be in developed economies with established engineering firms and universities such as US, UK, Germany, France, Japan, Australia etc. Thus, I was pleasantly surprised when I heard from our sales team about strong interest from emerging economies such as India, China, Brazil, Greece, Spain etc.
Countries like India and China are rapidly developing; playing catch-up definitely has its advantages. For instance, Asian countries have more modern telecommunication networks than the US. Starting late allows allocating resources to newer technology, and learning from the experiences of the trailblazers. Each country also faces its own unique set of challenges and competitive pressures. For example, Ajay Shah, one of India's top economists, says the following about cell phone companies in India:
Roughly a decade ago, the standard engineering solutions that camefrom international telecom vendors induced prices for mobile telephonylike USD 0.1 per minute. In India, there was a unique bulge ofcustomers who were only available at lower prices. This market reality,coupled with competitive pressure, prompted Indian mobile phone vendorsto resort to an array of hardware and software innovations which haveinduced the lowest cost of mobile telephony in the world.
Is something similar happening with high performance computing ? Are firms and universities in rest of the world leapfrogging older tools and technologies with a clean start ? Where will the next generation of innovations come from ?
A quick look at the statistics on the Top500 list shows that the developing nations are rapidly catching up. In 2001, China had 3 entries on the Top500, Brazil had 2, Russia had 1, and India had 0. Fast forward to 2007; we have China with 10, India with 9, Russia with 7, and Brazil with 1. India's placing at number 4 on Top500 in the latest list also generated some headlines. I wrote an opinion piece for the Financial Express in India with my views on the topic. The presence of the BRIC countries at the 2008 SIAM Parallel Processing meeting in Atlanta is more evidence on the state of affairs.
The cost of entry in high performance computing is quite high. After spending large budgets on Top500 computers, how do you program them ? The top US universities train some of the world's best parallel programmers. For an idea of what it takes to get started, see these classes at MIT, UC Santa Barbara, and UC Berkeley. Having been a teaching assistant for some of these, I believe that no more than 50 Computer Science and Engineering students take these classes every year at a given university. Thus, perhaps 500 students receive rigorous training in high performance scientific computing every year. So, how will the rest of the world program these computers ? Platforms such as Star-P bring high performance computing to the masses.
It is also refreshing to see that programs such as the DARPA/DOE HPCS are emphasizing productivity along with performance. This has resulted in three new languages: IBM's X10, Sun's Fortress, and Cray's Chapel. For a systematic approach to measuring productivity, see the work published by the HPCS productivity team. My views on the subject, of course, are in my thesis.
Are programmers going to embrace these brand new programming languages that lack the kind of library support and rich experience that desktop environments such as MATLAB, Mathematica, R, and Python provide ? We'll have to wait a few years for the answer. In the meanwhile, platforms such as Star-P are rapidly bridging the gap between productivity and performance.