FPGA-Based Computational Accelerators

描述（由申请人提供）：对更具成本效益，灵活和方便的高性能计算的需求是生物信息学和计算生物学不同领域的共同主题。我们建议通过增加低成本的，基于FPGA的，计算协处理器的PC来解决这个问题，也就是说，与插件板类似的成本和易用性的图形卡。拟议的硬件由主流商品部件组成。要开发的关键技术是软件环境，它将完全隐藏底层硬件，不仅对用户，而且对大多数应用程序开发人员。预计交付的是一个100至1000倍的速度比PC的各种应用程序，以及一个环境，为创造新的应用程序的普通程序员。将拟议的系统与当前的替代方案进行比较：PC不是很强大;小型集群既不是很强大，也不容易使用;大型集群和超级计算机非常昂贵，难以使用;专用硬件昂贵，不灵活，适用性有限。基本的创新是使用FPGA来加速基础广泛的用户可定义应用。技术创新基于两个因素：电路的现场可编程性和计算家族内的特性共性。这些使系统能够基于模板和可调用库，使用户能够访问高效的硬件设计，而无需硬件知识。预期的广泛影响是台式计算机计算能力的质的提高。从长远来看，PC集群可能会出现类似的增长。具体的应用领域不仅包括序列处理，还包括微阵列数据分析、分子相互作用建模、遗传网络建模等。

DESCRIPTION (provided by applicant): The need for more cost-effective, flexible, and convenient high-performance computing is a common thread across diverse areas of Bioinfomatics and Computational Biology. We propose to address this problem by augmenting PCs with low-cost, FPGA-based, computational coprocessors; that is, with plug-in boards similar in cost and ease of use to graphics cards. The proposed hardware consists of mainstream commodity components. The key technology to be developed is the software environment, which will completely hide the underlying hardware not only from users, but also from most application developers. The projected deliverable is a factor of 100 to 1000 speed-up over a PC for a wide variety of applications, together with an environment for the creation of new applications by ordinary programmers. To compare the proposed system with current alternatives: PCs are not very powerful; small clusters are neither very powerful, nor easy to use; large clusters and supercomputers are very expensive and hard to use; and specialized hardware is expensive, inflexible, and has limited applicability. The basic innovation is the use of FPGAs to accelerate broad-based user-definable applications. The technical innovation is based on two factors: the field programmability of the circuits and the commonality of characteristics within families of computations. These enable a system based on templates and callable libraries, giving the user access to efficient hardware designs without requiring hardware knowledge. The expected broad impact is a qualitative increase in computing capability available at the desktop. The longer term may see a similar increase with respect to PC clusters. Specific areas of applicability are not only sequence processing, but also microarray data analysis, modeling molecular interactions, modeling genetic networks, and many others.