CSR: Small: Towards Realizing Cloud HPC: An Adaptive Programming Model for Accelerator-based Clusters

CSR：小：迈向实现云 HPC：基于加速器的集群的自适应编程模型

• 批准号: 1016793
• 负责人: Ali Butt
• 金额: $ 40.98万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016793&HistoricalAwards=false
• 关键词: CSR; Small; Towards; Realizing; Cloud

High-End Computing systems, such as cloud computing setups, are increasingly employing many-core compute resources and computational accelerators, e.g., GPUs and IBM Cell processors, for high performance. However, the use of such components results in a performance and communication mismatch, which in turn makes large-scale systems with heterogeneous resources difficult to design, build and program. Moreover, the increased data demand of modern advanced applications, coupled with the asymmetry between computation speed and data transmission speed, threaten the benefits of employing accelerators in such setups.This project addresses the above problems by designing a flexible, scalable, and easy-to-use programming model, AMOCA. AMOCA supports innovative workload distribution techniques, which enables it to be used toward scaling modern scientific and enterprise applications on high-end asymmetric clouds comprising heterogeneous accelerator-type compute nodes. Moreover, AMOCA utilizes component-capability matching and adaptive inter-component data transfers for parallel programming models, automatically handles heterogeneous resources, and auto-tunes the model parameters to the specific instance of resources on which it is run.AMOCA lays the foundation for adapting the cloud computing paradigm for HPC, creates open source and transformative technologies for scalable any-core system architectures, and is expected to improve the efficiency and performance of advanced applications in a broad range of disciplines that perform simulation-based experimentation including computational physics, biology, and chemistry. AMOCA employs an integrated research and education approach for training both undergraduate and graduate researchers, especially from underrepresented groups. The training will instill critical system development skills and increase the use of accelerator-based clouds in HPC.

高端计算系统（例如云计算设置）越来越多地采用多核计算资源和计算加速器，例如GPU和IBM Cell处理器，以提供高性能。 但是，这种组件的使用导致性能和通信不匹配，这又使具有异质资源的大规模系统难以设计，构建和编程。 此外，现代高级应用程序的数据需求增加，再加上计算速度和数据传输速度之间的不对称性，威胁到在此类设置中使用加速器的好处。该项目通过设计灵活，可扩展且易于的，可解决上述问题 - 使用编程模型，Amoca。 AMOCA支持创新的工作负载分配技术，该技术可用于在包括异质加速器型计算节点的高端不对称云上扩展现代科学和企业应用程序。此外，AMOCA利用了可行编程模型的组件能力匹配和自适应间组件数据传输，自动处理异质资源，并将模型参数自动为特定的资源来源。Amocalay.amoca lays amoca lays the amoca lays th HPC的云计算范式，为可扩展的任何核心系统体系结构创建开源和变革性技术，并有望在广泛的学科中提高高级应用程序的效率和性能，这些学科进行基于模拟的实验，包括计算物理，生物学，生物学，生物学，和化学。 AMOCA采用综合研究和教育方法来培训本科和研究生研究人员，尤其是来自代表性不足的群体。该培训将灌输关键的系统开发技能，并增加HPC中基于加速器的云的使用。