As you are probably aware, CUDA 7 was officially released during the 2015 GPU Technology Conference. For this Spotlight I took a few minutes to pick the brain of an early adopter of CUDA 7 to see how his work benefits from the new C++11 support.
I interviewed Yu-Hang Tang, a Ph.D. candidate in the Division of Applied Mathematics at Brown University in Providence, Rhode Island.
What breakthrough project is currently taking up all of your brain’s time?
At this moment we are finalizing a particle-based simulator for the in silico investigation of microfluidic devices used in cancer diagnostic. The code enables us to predict the behavior of cancer cells as well as blood cells in various microfluidic channels. It could significantly speed up the process of microfluidic device design, which is usually time-consuming due to the large amount of trial-and-error experiments.
We will release the work by end of April and I will be happy to talk about more details by that time.
Tell me a bit about your GPU Computing background.
I started programming on the GeForce GTX 460 GPUs using OpenCL since 2010, and in 2012 I shifted entirely to CUDA C++.
Right now, I use mostly Kepler GPUs with high double-precision floating-point performance. I have been focused on accelerating particle-based simulations including All-Atom Molecular Dynamics (AAMD), Dissipative Particle Dynamics (DPD) and Smoothed Particle Hydrodynamics (SPH).
In fact, I have developed an entire GPU package (our USERMESO package), for the LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) particle simulator for DPD and SPH simulations. The package achieves 20x to 30x speed up on a single K20 GPU over 16 AMD CPU cores on a Cray XK7 compute node.
How has GPU computing impacted your research?
Our USERMESO package allows us to simulate DPD systems containing several millions of particles for millions of time steps on a daily basis during the study of the self-assembly behavior of amphiphilic polymers. The multi-compartment multi-walled vesicle, or simply think of it as a miniature cell, as Figure 1 shows, is only observable at a spatial-temporal scale that is tens of times larger, and tens of times longer than that covered by typical contemporary DPD simulations. With the USERMESO code we can perform such simulations daily with just 16 GPUs!