FPGA-Based Computational Accelerators

描述(由申请人提供)：对更具成本效益、更灵活、更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线。我们建议通过用低成本、基于现场可编程门阵列的计算协处理器来增强PC来解决这个问题；也就是说，使用在成本和易用性上与图形卡相似的插件板。拟议的硬件由主流商品组件组成。待开发的关键技术是软件环境，它将不仅对用户，而且对大多数应用程序开发人员完全隐藏底层硬件。预计的交付成果是各种应用程序在PC上的速度提高100到1000倍，以及一个供普通程序员创建新应用程序的环境。将建议的系统与目前的替代系统进行比较：PC功能不是很强大；小型集群既不是非常强大，也不容易使用；大型集群和超级计算机非常昂贵，很难使用；专用硬件昂贵，缺乏灵活性，适用性有限。基本的创新是使用现场可编程门阵列来加速基础广泛的用户可定义的应用。技术创新基于两个因素：电路的现场可编程性和计算系列内特性的共性。这些支持基于模板和可调用库的系统，使用户无需硬件知识即可访问高效的硬件设计。预期的广泛影响是台式机可用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。具体的适用领域不仅是序列处理，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.