When it comes to parallel computing, everyone soon learns there's no free lunch. And yet, we are all being 'driven parallel' by our almost insatiable appetites for HPC and/or Big Data Analytics. Although parallel computing originally achieved mass-market awareness with the introduction of the multicore architecture CPU, many integrated core CPUs, GPGPUs and in-memory computing have significantly amplified its potential for impact. Given that our collective futures are decidedly parallel, the need for enabling technologies is escalating.
Workload management is an increasingly important enabler of parallel computing as application prototypes are placed into production and scaled. In fact, once finite pools of resources are used by multiple people, projects, and/or lines of business, workload management becomes essential.
With a perspective that focuses on enabling production parallel computing at scale via workload management, you can expect to learn:
The possibilities for enabling parallel computing via workload management will be demonstrated.
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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