CCL: Introducing Cores

Fellow CCLer,

We've developed a distributed computing platform for molecular simulations, Cores, and we'd like to see how it can be of help in your work.

(Disclaimer: BinaryBio is a for-profit company founded by some of the creators and the lead developers of Gromacs. The company has an open core business model, that is, the core of Cores will be released open source.)

Briefly, Cores lets you run simulations in the most efficient way on any computational resource you give it. It is specifically created for working with complex sampling algorithms by letting the user focus on obtaining end results. Running Gromacs on top of Cores, we have achieved some interesting results and have a paper which will be presented at Super Computing 2011 in Seattle later this year (abstract below).

Here's a short description of Cores:
http://binarybio.com/en/products/cores">http://binarybio.com/en/products/cores

The next step is to build computational chemistry applications on top of Cores. We'd love to hear about the challenges you are facing when it comes to compute intensive tasks and see how Cores could be of help. 

If you're curious about how Cores could be used in your work, throw away a quick email to
shahan%binarybio.com

Best,
Shahan Lilja

P.S. Here's the abstract from a paper we're presenting at Super Computing 2011 in Seattle.

"Biomolecular simulation is a core application on supercomputers, but it is exceptionally difficult to achieve the strong scaling necessary to reach biologically relevant timescales. Here, we present a new paradigm for parallel adaptive molecular. This framework combines performance-leading molecular dynamics parallelized on three levels (SIMD, threads, and message-passing) with kinetic clustering, statistical model building and real-time result monitoring. Cores enables execution as single parallel jobs with automatic resource allocation. Even for a small protein such as villin (9,864 atoms), Cores exhibits near-linear strong scaling from 1 to 5,376 AMD cores. Starting from extended chains we observe structures 0.6Å from the native state within 30h, and achieve sufficient sampling to predict the native state without a priori knowledge after 80-90h. To match Cores’ efficiency, a classical simulation would have to exceed 50 microseconds per day, currently infeasible even with custom hardware designed for simulations."