As I touched on when I was musing about copy and paste, it never ceases to amaze me how much compute power has increased since I started doing modeling 15 years ago. Back then, I could minimize a few hundred atoms with classical mechanics and watch the geometry update every few seconds after each iteration. Now, I would do a few thousand molecules of the same size in a few seconds.
Of course, as computers get faster, the sorts of calculations that customers want to run also get larger and more “realistic.” The explosive growth of multi-core architectures has lead to an increased focus on parallelization strategies to make the most efficient use of the CPU power. Traditionally, the main focus with high performance codes has been on getting them to run in fine-grained parallel as efficiently as possible, scaling a single job over 100’s of CPU’s. The issue here is that, if you are only studying a medium sized system, you will quickly get to a point where communication between nodes costs far more than the time in the CPU, leading to poor performance.
The solution, if you are doing multiple calculations, is to run multiple calculations in a coarse-grained, or embarrassingly parallel, approach. In this mode, you run an individual calculation on a single CPU but occupy all your CPU’s with multiple calculations. Materials Studio will do this but it can be monotonous, although you can use queuing systems to get somewhere here. The other issue is that when you start doing many calculations, you also get lots of data back to analyze. Now you not only need a way to submit and control all the jobs, but also to automatically get the results you want.
Luckily, the Materials Studio Collection in Pipeline Pilot has both of these functionalities. You can use the internal architecture to submit jobs in both fine and coarse grained parallel (or a mixture of the two if you have a nice cluster!). You can also use the reporting tools to extract the data you want and display it in a report.
A great example of this is working with classical simulations of polymers for calculation of solubility parameters. Here, you need to build several different cells to sample phase space and then run identical calculations on each cell. You can quickly develop the workflow and then use the coarse-grained parallel options to submit a calculation to each CPU. When the calculations are finished, a report of the solubility parameter can be easily generated automatically.
So, whilst there is a definite need for an expert modeling client like Materials Studio, the Materials Studio Collection really does free you up to focus on problem solving (using Materials Studio!), not waiting for calculations to finish and then having to monotonously create reports!