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.
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.
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 contribution is a new algorithm for emulating the seeding and extension phases of BLAST. This operates in a single pass through a database at streaming rate, and with no preprocessing other than loading the query string. Moreover, it emulates parameters turned to maximum possible 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 showing order of magnitude acceleration over serial reference code. A simple extension assures compatibility with NCBI BLAST.
Abstract-Molecular dynamics simulation based on discrete event simulation (DMD) is emerging as an alternative to time-step driven molecular dynamics (MD). DMD uses simplified discretized models, enabling simulations to be advanced by event, with a resulting performance increase of several orders of magnitude. Even so, DMD is compute bound. Moreover, unlike MD, causality issues make DMD difficult to scale. Here we present a microarchitecture-inspired parallel algorithm for DMD: speculative execution enables multithreading, while in-order commitment ensures correctness. Our initial not-yet optimized implementation obtains scalability for a multicore processor when running realistic simulation models.
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