Ganesh Swami

I know some of my readers are deeply interested in high performance computing and computational physics: this is a post for them. The conference I had mentioned in my previous post was the GROMACS Workshop on Advanced Simulation Methods.

Gromacs is a high performance simulation engine primarily for solving Newtonian dynamics (it also does normal mode analysis, structure minimization and mixed molecular mechanics-quantum mechanics simulations.) It was an industry leader in terms of raw single processor performance for many years, until Desmond from D.E. Shaw Research took over with their super-scalable algorithms (I’ve written about this before.) With Gromacs 4.0, they’ve fixed the scalability problems and with a variety of other algorithmic fixes, they are the top dog once again. Disclaimer: these are all claims by relevant parties and I have not verified them myself, though I’d love to do so unencumbered. Though the Gromacs 4.0 paper is published, I’ll only be writing about it when the actual product is released.

The focus of the workshop was on algorithms, though there were some applications too. I’m sure an applications person would have felt out of place, but I felt I had something to contribute in almost every topic that was discussed. I’m archiving the list of topics here for posterity:

• The new domain decomposition parallelization in Gromacs 4.0, with some tips & tricks to get the most out of your hardware
• Different methods to perform free energy calculations. Slow-growth, perturbations, Bennett Acceptance Ratio. Which protocol is most efficient, and what new things will be in Gromacs 4.0?
• QM/MM. How do you mix Quantum Mechanics with Gromacs?
• Virtual sites for hydrogen motion removal and long time-steps
• Membrane protein simulations
• Replica exchange, and extracting kinetic data from it
• Local pressure extensions to Gromacs
• Gromacs source code walk-through

The take home message: strong coupling between various pieces of the algorithm is anti-thesis to parallel scalability. The CPU industry seems to have hit a brick wall in terms of improving raw computational speed: the future is in multi-core. Therefore, remove the coupling with better algorithms and you are on your way to highly scalable and by definition superbly fast algorithms.

The timestep used in an integrator while solving a set of equations inherently determines the speed of the algorithm. Big timesteps will make the algorithm unstable as you your trajectory will not be able to follow the phase space manifold accurately (as a side note Euler-type integrators also become unstable as you make the timestep smaller, but this is the least of your worries with a non-symplectic integrator.) The Nyquist theorem determines the sampling rate, so removing fast (or high frequency) degrees of motion such as hydrogen bond vibrations with constraints on them is required for a big timestep. Usual constraints algorithms are coupled leading to undesirable non-scalable algorithms. The Gromacs developers have solved this with a new constraints algorithm called P-LINCS.

I was at the BC Technology Impact Awards ceremony last week representing my company Zymeworks. Zymeworks was nominated for the most promising pre-commercial technology company, but unfortunately we didn’t win. The award in this category went to Lignol Energy Corp., a clean tech company.

The organizers had tiled one wall of the Banquet Hall with a 100 feet screen. They had calibrated multiple projectors to blend the edges. Pretty impressive. You can get this technology from a couple of companies: here’s one.

Abebooks.com, an online market for new and used books also won an award. I’ve been using this website for the past couple of years to get used textbooks. Highly recommended.

John MacDonald, the founder of MDA (the ‘M’ in MDA) and of Day4Energy Inc. got the Person of the Year award. It’s truly an honor to be in the same room with the accomplished!

Not. Dead. Yet.

A couple of months back, I got a chance to go to Stanford for a conference. I took the opportunity to visit a couple of my friends in the area. It was a blast!

Though I doubt if they’ll continue to remain my friends: I put almost 400 miles (ugh…metric units) on his car in two days! Living in Mountain View, we visited San Francisco twice, Berkeley once, Stanford twice (or thrice?) We went around almost all major tech companies in the valley.

I’ve heard about a lot of tech companies being in the same area, but I didn’t expect them to be so densely concentrated. As my friend says, “you can change your job without really changing where you park your car!” So true.

One of the things I really wanted to do is to have authentic Mexican cuisine (none of that Taco Bell crap.) We went to two places: La Fiesta in Mountain View and another one I can’t recall in San Francisco (it’s by the beach.) My friend remarked that he has had better, but I’ll have to start somewhere. I’ll be in San Diego and Austin (i.e., closer to the Mexican border) later this year, and I guess I can re-fulfill that wish again.

One of the things that really irked me was how often people pull out their car. Here in Vancouver, I always walk a couple of blocks to the corner grocery store or for breakfast or anything for that matter. For breakfast, we pulled out the car, drove on the freeway, and landed at House of Pancakes. For lunch, we pulled out the car, drove on the freeway, and landed at a restaurant. For buying a razor at Safeway, we pulled….you get the picture.