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

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

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.