Acceleration of Biomolecular Simulations using FPGA-based Reconfigurable Computing

A paradigm shift is occurring in the way compute-intensive scientific applications are developed. Thanks to advancements in commercially viable hybrid architectures for High-Performance Computing (HPC), the focus has shifted from improving performance by merely scaling algorithms on von Neumann computing nodes to fully exploiting additional computational capabilities provided by accelerators such as FPGAs (Field Programmable Gate Arrays) and GPGPUs (General Purpose Graphical Processing Units). Computational chemists use Molecular Dynamics (MD) simulations like LAMMPS (Large Scale Atomic Molecular Massively Parallel Systems) and NAMD (NAnoscale Molecular Dynamics) to simulate biomolecular behaviour such as protein folding and small molecule docking to proteins. MD simulations are computationally complex n-body problems, which are time consuming to simulate in biologically relevant scales. Executing such simulations in best available HPC environments is critical for scientific advancements in the field. Thus, as HPC technology evolves, there is a need to update classical biomolecular simulation applications like LAMMPS to better suit the architecture. In this work, we modify LAMMPS (a classical molecular dynamics simulation program developed for CPU-only clusters) to execute on a reconfigurable computer system, SRC-7 H MAP. The SRC-7 H MAP consists of two Altera FPGA logic chips interfaced to a dual-core Intel Xeon processor. Users can benefit by offloading most compute-intensive tasks of the application to the FPGA logic. This work explores the challenges involved in effectively adapting a production level application code optimized for von Neumann architecture, to an FPGA-based hybrid architecture. We have successfully accelerated the non-bonded force computations, the most compute-intensive module in LAMMPS for biomolecular simulations, by 5.0x over a single 3.0 GHz Xeon processor. This performance includes the data transfer overheads and function calling overheads. Further, using the accelerated non-bonded force computations function, we achieve an overall application speed-up of 2.0x to 2.4

Tackling Exascale Software Challenges in Molecular Dynamics Simulations with GROMACS

GROMACS is a widely used package for biomolecular simulation, and over the last two decades it has evolved from small-scale efficiency to advanced heterogeneous acceleration and multi-level parallelism targeting some of the largest supercomputers in the world. Here, we describe some of the ways we have been able to realize this through the use of parallelization on all levels, combined with a constant focus on absolute performance. Release 4.6 of GROMACS uses SIMD acceleration on a wide range of architectures, GPU offloading acceleration, and both OpenMP and MPI parallelism within and between nodes, respectively. The recent work on acceleration made it necessary to revisit the fundamental algorithms of molecular simulation, including the concept of neighborsearching, and we discuss the present and future challenges we see for exascale simulation - in particular a very fine-grained task parallelism. We also discuss the software management, code peer review and continuous integration testing required for a project of this complexity.Comment: EASC 2014 conference proceedin

Reactive Programming of Simulations in Physics

We consider the Reactive Programming (RP) approach to simulate physical systems. The choice of RP is motivated by the fact that RP genuinely offers logical parallelism, instantaneously broadcast events, and dynamic creation/destruction of parallel components and events. To illustrate our approach, we consider the implementation of a system of Molecular Dynamics, in the context of Java with the Java3D library for 3D visualisation

Harvesting graphics power for MD simulations

We discuss an implementation of molecular dynamics (MD) simulations on a graphic processing unit (GPU) in the NVIDIA CUDA language. We tested our code on a modern GPU, the NVIDIA GeForce 8800 GTX. Results for two MD algorithms suitable for short-ranged and long-ranged interactions, and a congruential shift random number generator are presented. The performance of the GPU's is compared to their main processor counterpart. We achieve speedups of up to 80, 40 and 150 fold, respectively. With newest generation of GPU's one can run standard MD simulations at 10^7 flops/$.Comment: 12 pages, 5 figures. Submitted to Mol. Si