OpenRBC: Redefining the Frontier of Red Blood Cell Simulations at Protein Resolution

We present a from-scratch development of OpenRBC, a coarse-grained molecular dynamics code, which is capable of performing an unprecedented in silico experiment --- simulating an entire mammal red blood cell lipid bilayer and cytoskeleton modeled by 4 million mesoscopic particles --- on a single shared memory node. To achieve this, we invented an adaptive spatial searching algorithm to accelerate the computation of short-range pairwise interactions in an extremely sparse 3D space. The algorithm is based on a Voronoi partitioning of the point cloud of coarse-grained particles, and is continuously updated over the course of the simulation. The algorithm enables the construction of a lattice-free cell list, i.e. the key spatial searching data structure in our code, in $O(N)$ time and space space with cells whose position and shape adapts automatically to the local density and curvature. The code implements NUMA/NUCA-aware OpenMP parallelization and achieves perfect scaling with up to hundreds of hardware threads. The code outperforms a legacy solver by more than 8 times in time-to-solution and more than 20 times in problem size, thus providing a new venue for probing the cytomechanics of red blood cells.