The acceleration of molecular dynamics (MD) simulations using high-performance reconfigurable computing (HPRC) has been much studied. Given the intense competition from multicore and GPUs, there is now a question whether MD on HPRC can be competitive. We concentrate here on the MD kernel computation: determining the short-range force between particle pairs. In one part of the study, we systematically explore the design space of the force pipeline with respect to arithmetic algorithm, arithmetic mode, precision, and various other optimizations. We examine simplifications and find that some have little effect on simulation quality. In the other part, we present the first FPGA study of the filtering of particle pairs with nearly zero mutual force, a standard optimization in MD codes. There are several innovations, including a novel partitioning of the particle space, and new methods for filtering and mapping work onto the pipelines. As a consequence, highly efficient filtering can be implemented with only a small fraction of the FPGA’s resources. Overall, we find that, for an Altera Stratix-III EP3ES260, 8 force pipelines running at nearly 200 MHz can fit on the FPGA, and that they can perform at 95% efficiency. This results in an 80-fold per core speed-up for the short-range force, which is likely to make FPGAs highly competitive for MD.

Field-programmable gate arrays are widely considered as accelerators for compute-intensive applications. A critical phase of FPGA application development is finding and mapping to the appropriate computing model. FPGA computing enables models with highly flexible fine-grained parallelism and associative operations such as broadcast and collective response. Several case studies demonstrate the effectiveness of using these computing models in developing FPGA applications for molecular modeling.

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Recent work in the FPGA acceleration of molecular dynamics simulation has shown that including on-the-fly neighbor list calculation (particle filtering) in the device has the potential for an 80× per core speed-up over the CPU-based reference code and so to make the approach competitive with other computing technologies. In this paper we report on progress and challenges in advancing this work towards the creation of a production system, especially one capable of running on a large-scale system such as the Novo-G. The current version consists of an FPGAaccelerated NAMD-lite running on a PC with a Gidel PROCStar III. The most important implementation issues include software integration, handling exclusion, and modifying the force pipeline. In the last of these we have added support for Particle-MeshEwald and augmented the Lennard-Jones calculation with a switching function. In experiments, we find that energy stability so far appears to be acceptable, but that longer simulations are needed. Due primarily to the added complexity of the force pipelines, performance is somewhat diminished from the previous study; we find, however, that porting to a newer (existing) device will more than compensate for this loss.

The acceleration of molecular dynamics (MD) simulations using high performance reconfigurable computing (HPRC) has been much studied. Given the intense competition from multicore and GPUs, there has been a question whether MD on HPRC can be competitive. We concentrate here on the MD kernel computation: determining the short-range force between particle pairs. In particular, we present the first FPGA study on the filtering of particle pairs with nearly zero mutual force, a standard optimization in MD codes. There are several innovations, including a novel partitioning of the particle space, and new methods for filtering and mapping work onto the pipelines. As a consequence, highly efficient filtering can be implemented with only a small fraction of the FPGA's resources. Overall, we find that, for an Altera Stratix-III EP3ES260, 8 force pipelines running at 200MHz can fit on the FPGA, and that they can perform at 95% efficiency. This results in a 80-fold per core speed-up for the short-range force, which is likely to make FPGAs highly competitive for MD.