Molecular Dynamics (MD) is of central importance to computational chemistry. Here we show that MD can be implemented efficiently on a COTS FPGA board, and that speedups from ¿½¢ to ¢ over a PC implementation can be obtained. Although the amount of speed-up depends on the stability required, ¢ can be obtained with virtually no detriment, and the upper end of the range is apparently viable in many cases. We sketch our FPGA implementations and describe the effects of precision on the trade-off between performance and quality of the MD simulation.

Numerous application areas, including bioinformatics and computational biology, demand increasing amounts of processing capability. In many cases, the computation cores and data types are suited to field-programmable gate arrays. The challenge is identifying the design techniques that can extract high performance potential from the FPGA fabric.Accelerating high-performance computing (HPC) applications with field-programmable gate arrays (FPGAs) can potentially deliver enormous performance. A thousand-fold parallelism is possible, especially for low-precision computations. Moreover, since control is configured into the logic itself, overhead instructions-such as array indexing and loop computationsneed not be emulated, and every operation can deliver payload.At the same time, using FPGAs presents significant challenges 1 including low operating frequency-an FPGA clocks at one-tenth that of a high-end microprocessor. Another is simply Amdahl's law: To achieve the speedup factors required for user acceptance of a new technology (preferably 50 times), 2 at least 98 percent of the target application must lend itself to substantial acceleration. As a result, HPC/FPGA application performance is unusually sensitive to the implementation's quality.The problem of achieving significant speedups on a new architecture without expending exorbitant development effort, and while retaining flexibility, portability, and maintainability, is a classic one. In this case, accelerating HPC applications with FPGAs is similar to that of porting uniprocessor applications to massively parallel processors, with two key distinctions:• FPGAs are far more different from uniprocessors than MPPs are from uniprocessors, and• the process of parallelizing code for MPPs, while challenging, is still better understood and supported than porting codes to FPGAs.Lawrence Snyder stated the three basic parameters for the MPP portability problem. 3 First, a parallel solution using P processors can improve the best sequential solution by a factor of P, at most. Second, HPC problems tend to have third-or fourth-order complexity, and so parallel computation, while essential, offers only modest benefits. Third, "the whole force of parallelism must be transferred to the problem, not converted to 'heat' of implementational overhead."Researchers have addressed the portability problem periodically over the past 30 years, with well-known approaches involving language design, optimizing compilers, emulation, software engineering tools and methods, and function and application libraries. It is generally agreed that compromises are required: Either restrict the variety of architectures or scope of application, or bound expectations of performance or ease of implementation.

Approximate string matching is fundamental to bioinformatics, and has been the subject of numerous FPGA acceleration studies. We address issues with respect to FPGA implementations of both BLAST-and dynamic-programming-(DP) based methods. Our primary contributions are two new algorithms for emulating the seeding and extension phases of BLAST. These operate in a single pass through a database at streaming rate (110 Maa/sec on a VP70 for query sizes up to 600 and 170 Maa/sec on a Virtex4 for query sizes up to 1024), and with no preprocessing other than loading the query string. Further, they use very high sensitivity with no slowdown. While current DP-based methods also operate at streaming rate, generating results can be cumbersome. We address this with a new structure for data extraction. We present results from several implementations.

FPGA-based acceleration of molecular dynamics simulations (MD) has been the subject of several recent studies. The short-range force computation, which dominates the execution time, is the primary focus. Here we combine: a high level of FPGA-specific design including cell lists, systematically determined interpolation and precision, handling of exclusion, and support for MD simulations of up to 256K particles. The target system consists of a standard PC with a 2004-era COTS FPGA board. There are several innovations: new microarchitectures for several major components, including the cell list processor and the off-chip memory controller; and a novel arithmetic mode. Extensive experimentation was required to optimize precision, interpolation order, interpolation mode, table sizes, and simulation quality. We obtain a substantial speed-up over a highly tuned production MD code.

Molecular docking is one of the primary computational methods used by pharmaceutical companies to try to reduce the cost of drug discovery. A common docking technique, used for low-resolution screening or as an intermediate step, performs a threedimensional correlation between two molecules to test for favorable interactions between them. We extend our previous work on FPGA-based docking accelerators, using reconfigurability for customization of the physical laws and geometric models that describe molecule interaction. Our approach, based on direct summation, allows straightforward combination of multiple forces and enables nonlinear force models; the latter, in particular, are incompatible with the transform-based techniques typically used. Our approach has the further advantage of supporting spatially oriented values in molecule models, as well as the detection of multiple positions representing favorable interactions. We report performance measurements on several different models of chemical behavior and show speedups of from 130× to 1100× over a PC.