UWSpace is currently experiencing technical difficulties resulting from its recent migration to a new version of its software. These technical issues are not affecting the submission and browse features of the site. UWaterloo community members may continue submitting items to UWSpace. We apologize for the inconvenience, and are actively working to resolve these technical issues.
 

Optimizing MPI Collective Operations for Cloud Deployments

Thumbnail Image

Files

AlSader_Zuhair.pdf (939.75 KB)

Date

2020-01-24

Authors

AlSader, Zuhair

Journal Title

Journal ISSN

Volume Title

Publisher

University of Waterloo

Abstract

Cloud infrastructures are increasingly being adopted as a platform for high performance computing (HPC) science and engineering applications. For HPC applications, the Message-Passing Interface (MPI) is widely-used. Among MPI operations, collective operations are the most I/O intensive and performance critical. However, classical MPI implementations are inefficient on cloud infrastructures because they are implemented at the application layer using network-oblivious communication patterns. These patterns do not differentiate between local or cross-rack communication and hence do not exploit the inherent locality between processes collocated on the same node or the same rack of nodes. Consequently, they can suffer from high network overheads when communicating across racks. In this thesis, we present COOL, a simple and generic approach for Message-Passing Interface (MPI) collective operations. COOL enables highly efficient designs for collective operations in the cloud. We then present a system design based on COOL that describes how to implement frequently used collective operations. Our design efficiently uses the intra-rack network while significantly reducing cross-rack communication, thus improving application performance and scalability. We use software-defined networking capabilities to build more efficient network paths for I/O intensive collective operations. Our analytic evaluation shows that our design significantly reduces the network overhead across racks. Furthermore, when compared with OpenMPI and MPICH, our design reduces the latency of collective operations by a factor of log N, where N is the total number of processes, decreases the number of exchanged messages by a factor of N and reduces the network load by up to an order of magnitude. These significant improvements come at the cost of a small increase in the computation load on a few processes.

Description

Keywords

mpi, message passing, collective operations, software defined networks, hierarchy

LC Keywords

Software-defined networking (Computer network technology)

Citation

URI

http://hdl.handle.net/10012/15581

Collections

Theses
Computer Science