High-performance and scalable communication subsystems for exascale computing

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High-Performance Computing (HPC) is the key to many scientific discoveries and engineering innovations. It is used to tackle computationally-intensive problems in fields as diverse as drug discovery, modeling of global climate system, seismic processing for oil and gas, green energy, genomics and bioinformatics, and astrophysics. Scientific/engineering simulations are mainly written with the Message-Passing Interface (MPI) library. Parallel processes in these simulations compute on their local data while extensively communicating with each other through the MPI library. Communication adversely affects the performance and scalability of MPI applications running on HPC clusters. With the availability of multi-core and soon many-core architectures offering increasing parallelism at all levels, HPC clusters consisting of hundreds of thousands of nodes with millions of cores and complex network topologies are poised to break the Exaflops (10^18 floating point operations per second) barrier in the coming years. With the emergence of such highly hierarchical clusters, MPI has to be optimized for performance and scalability in order to cope with the ever-increasing demands of large-scale simulations. The proposed research is highly original and innovative in the sense that it addresses key issues in MPI, by including topology-awareness for process mapping, by incorporating dedicated queues for partner processes, by quality of service provisioning partner/non-partner traffic, and by developing fiber-based asynchronous progression techniques. The outcome of this research will be relevant to various sectors in Canada, including Environment Canada, Compute Canada, Canada Genome Sciences Centre, oil and gas industries, and ultimately the Canadian public at large. It is expected that the findings from this research will have significant impact on the target community, and that it will lead to new directions for future research. The proposed research is ideal for training HQP in that it has a strong foundation that translates immediately into practical applications and implementations. There is a high demand for graduates in HPC and networking, and the HQP trained will be well positioned to compete for jobs in academia and industry.