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

诸如云计算设置的高端计算系统越来越多地采用众核计算资源和计算加速器，例如，GPU和IBM Cell处理器，实现高性能。 然而，使用这样的组件会导致性能和通信不匹配，这反过来又使得具有异构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，加上计算速度和数据传输速度之间的不对称，威胁到在这样的setups.This项目中使用加速器的好处解决了上述问题，通过设计一个灵活的，可扩展的，易于使用的编程模型，AMOCA。 AMOCA支持创新的工作负载分配技术，使其能够用于在包含异构加速器类型计算节点的高端非对称云上扩展现代科学和企业应用程序。此外，AMOCA利用组件能力匹配和自适应组件间数据传输来实现并行编程模型，自动处理异构资源，并根据其运行的特定资源实例自动调整模型参数。AMOCA为HPC适应云计算范式奠定了基础，为可扩展的任何核心系统架构创建了开源和变革性技术，并有望提高执行基于模拟的实验的广泛学科中的高级应用的效率和性能，所述实验包括计算物理学、生物学和化学。AMOCA采用综合的研究和教育方法来培训本科生和研究生研究人员，特别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，并增加基于加速器的云在HPC中的使用。

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.