High Performance Computing in the Cloud is viable in numerous use cases.
Common to all successful use cases for cloud-based HPC is the ability embrace latency.
Not surprisingly then, early successes were achieved with embarrassingly parallel HPC
applications involving minimal amounts of data - in other words, there was little or
no latency to be hidden.
Over the fullness of time, however, the HPC-cloud community has become increasingly
adept in its ability to 'hide' latency and, in the process, support increasingly more
sophisticated HPC use cases in public and private clouds.
Real-world use cases will illustrate aspects of these sophistications for hiding latency in accounting for
large volumes of data, the need to pass messages between simultaneously executing components
of distributed-memory parallel applications, as well as (processing) workflows/pipelines.
Finally, the impact of containerizing HPC for the cloud will be considered through the
relatively recent creation of the Cloud Native Computing Foundation.
What you will learn:
A very brief taxonomy of clouds
Commoditized cloud applications
Latency: The gating factor for HPC-in-the-Cloud
Embarrassingly parallel HPC applications
The impact of large data volumes
The impact of execution time
Distributed-memory parallel HPC applications - MPI and beyond
The impact of containerization
The past, present and future viability of HPC in the cloud
Speaker:Ian Lumb, System Architect, Univa Corporation. As an HPC specialist, Ian Lumb has spent about two decades at the global intersection of IT and science.
Ian received his B.Sc. from Montreal's McGill University, and then an M.Sc. from York University in Toronto.
Although his undergraduate and graduate studies emphasized geophysics, Ian's current interests include workload
orchestration and container optimization for HPC to Big Data Analytics in clusters and clouds.
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