About a decade ago, when I was working for Apple, I was heavy into the support of supercomputing for customers at various research labs. They were already using the power of OS X on Macs with vector processors for their work. In the process, I got to know Dr. Gaurav Khanna at the University of Massachusetts who himself has made considerable contributions to other Apple customers using Macs for scientific computation.
Recently, Dr. Khanna and I were in a FaceTime session, and he was explaining how the 2013 Mac Pro in concert with OpenCL, has greatly assisted his scientific work. That conversation led to this interview which, we hope, provides substantial insights into using these Apple tools for scientific computing.
TMO: First, give us a little background on yourself. Who are you, and what do you do?
GK: I’m a computational scientist with research interests in the area of black hole physics and gravitation. I received my Ph.D. from Penn State back in 2000 and have been actively working in my field for around 15 years now. Currently, I’m an Associate Professor in the Physics Department at the University of Massachusetts Dartmouth. I also serve as the Associate Director of the newly established Center for Scientific Computing and Visualization Research (CSCVR) on my university campus.
TMO: Tell us more about your interest in black hole physics and also computational science in general.
GK: Well, even as a kid, I somehow developed a strong interest in gravity, probably due to my father who is also a physicist. It turns out that gravity, which is the most familiar force to all of us, is still perhaps the least understood force of Nature. Our current understanding of gravity was developed by Einstein nearly a century ago — in general relativity theory — which certainly has stood the test of time. However, to truly understand gravity and test our theories thoroughly, we must study phenomena wherein gravity is extremely strong. And that immediately brings us to black holes — perhaps the most intriguing and significant astrophysical objects in Nature — where gravity is infinitely powerful.
Now, Einstein’s theory of gravity and black holes are mathematically very complex, so one can only do rather limited calculations with pencil-and-paper. Sooner or later, one needs to take advantage of modern computational technologies to make significant advances. This realization is what led me to computational science and high-performance computing well over a decade ago and is also the reason why we developed the CSCVR on-campus.
TMO: What is supercomputing/parallel computing and why is it so important to scientific research today?
GK: Supercomputing is truly difficult to define. The reason is that with the rapid advances in computer technology, the bar for an entry-level supercomputer keeps getting higher every year. Indeed, the supercomputer of two decades ago, is less powerful than the iPhone today. So, I prefer a definition that a supercomputer is whatever yields performance that is much faster (say, by an order-of-magnitude or more) over a high-end workstation-class single-processor today, and involves a parallel computing model. I find this is a much more practical definition, and one that is more relevant to an application scientist.
Supercomputing is important to scientific research today because over the past decade computer simulation has joined experiment and theory as a third leg in almost every area of science and engineering research. Piggy backing on Moore’s Law, the capabilities of simulation have advanced very rapidly and continue to do so even today. Moreover, because today’s supercomputers are being built using commodity parts via the cluster approach, they are rather low-cost and fairly well standardized at this stage.
Next: How scientific computing has changed.