Determining Feasibility and Scalability of a Life/Medical Science Hybrid-core Based Platform

未来的高性能计算系统必须满足用户的需求，而这些需求并不能通过浮点运算等单一措施来充分定义。在生命和医学科学中尤其如此，因为这些领域的数据集非常庞大，而且计算通常不是浮点密集型的。我们将部署不同的计算机体系结构和不同的周边基础设施（硬件、软件、人员），以明确解决非pde解决社区的新兴需求，并利用弗吉尼亚理工大学Shadowfax原型系统的最新进展。我们打算创建一个可扩展的基于FPGA的集群，作为现有FPGA模块的扩展，与数据存储系统相平衡，以满足典型的生命/医学科学应用需求。我们将在三种商用处理器类型（微处理器、fpga和gpgpu）上优化和调整关键生命科学实现的各种采样，以确认该方法的性能和一般适用性，然后将系统和所有组件提供给社区进行计算并通过外展计划。具体目标是：1)优化和评估基于混合核的集群相对于标准微处理器和gpgpu的有效性，特别是针对非浮点、数据和内存密集型的生命和医学科学应用程序，并验证一组最需要的计算密集型应用程序的实用性和可扩展性，这些应用程序采样不同的算法/应用程序空间。2)创建一个安全的基于web的门户网站，生命/医学用户可以通过它来分析他们的数据。3)通过培训和实习计划传播我们的知识和经验，使用户、开发人员和系统人员能够使用原型系统或在本地复制和支持类似的系统。在这个项目中研究的新颖和可推广的高性能计算方法将潜在地证明计算类型和数据操作是某些架构的最佳匹配，因此将解决日益增长的生命/医学科学数据驱动研究社区的不同需求。如果成功的话，这里建立的计算机和技术可以加入以HPC浮点为中心的计算机网络，以响应这一不断增长的未满足需求。通过与商业合作伙伴合作，并将重点放在具体的演示应用程序上，我们将立即展示社区价值，并显著提高这些新系统的部署和扩展速度。该系统将立即提供给用户，特别是多个正在生成各种“组学”数据的生物医学研究小组，这些数据在适当系统上运行的工具的访问限制了分析的准确性和完整性，从而限制了分析的价值。通过多种渠道传播我们的定量研究结果，我们将为如何分析生物医学数据做出贡献，并建立一个基础，在此基础上设计未来的计算生产设施，以满足这一特定的社区需求。

Future HPC systems must match the needs of users, which are not adequately defined by single measures such as floating point operations. This is particularly true in the life and medical sciences where the data sets are immense and computations are frequently not floating point intensive. We will deploy a different computer architecture and different surrounding infrastructure (hardware, software, personnel) to explicitly address the emerging needs of the non-PDE solving community, and leverage recent advances made on a prototype system, Shadowfax, at Virginia Tech. We intend to create an extensible FPGA-based cluster as an expansion to existing FPGA modules that is balanced with a data storage system to address the needs that are typical of life/medical science applications. We will optimize and tune a diverse sampling of critical life science implementations on three commercially available processor types, microprocessors, FPGAs and GPGPUs, to confirm the performance and general applicability of this approach and will then make the system and all components available to the community on which to do computations and via an outreach program. The specific aims are:1) To optimize and evaluate the effectiveness of a Hybrid-core based cluster relative to standard microprocessors and GPGPUs specifically for life and medical science applications that are non-floating point, data and memory intensive and to validate the utility and scalability for a set of the most in-demand compute-intensive applications that samples diverse algorithm/application space.2) To create a secure web-based portal through which life/medical science users can analyze their data. 3) To propagate our knowledge and experience via a training and internship program that will enable users, developers and systems personnel to use the prototype system or locally replicate and support a similar system.The novel and generalize-able HPC approaches to be investigated in this project will potentially demonstrate types of computations and data manipulations are the best matches for certain architectures, thus will address the very different needs of the growing life/medical science data-driven research community. If successful, computers and techniques established here could join the established network of HPC floating point-centric computers to respond to this growing unmet need. By engaging commercial partners and focusing efforts on specific demonstration applications, we will immediately demonstrate community value and dramatically increase the speed with which these novel systems can be deployed and scaled.This system will immediately be available to users, especially a plurality of biomedical research groups who are generating a variety of '-omics' data for which access to the tools running on appropriate systems is limiting the accuracy and completeness of analysis and thus value. By propagating our quantitative findings via a number of channels, we will contribute to how biomedical data is analyzed, and establish a basis upon which future computational production facilities are designed to meet this specific community need.