Middleware and workflows support Sample Clauses

Middleware and workflows support. QCG-Pilotjob benchmarks on CompBioMed systems Within CompBioMed we are working in collaboration with the QCG-Pilotjobs development team from the Poznan Supercomputing and Networking Center (PSNC) in Poland to deploy a European workflow manager named QCG Pilot Job Manager (xxxxx://xxx- xxxxxxxx.xxxxxxxxxxx.xx/xx/xxxxxxx/) on different CompBioMed HPC systems and to evaluate the overhead of such tools and the impact they could have on exascale computational campaigns. This workflow manager enables automatic handling of complicated heterogenous workflows involving large number ensembles with different types of calculations performed concurrently. This falls under the planned activities to support the applications within “Ensemble” compute pattern, which is particularly relevant especially for validation, verification and uncertainty quantification; these are increasingly becoming essential for uptake of in silico methods in clinical medicine. We are currently testing and benchmarking it on all major supercomputing centres including PSNC, LRZ, EPCC and SURF. Preliminary results are reported in Annex 11.2, where we report the overhead measured on the HPC systems ARCHER2 (EPCC) and Cartesius (SURF) during our tests. As shown, the use of a middleware such as QCG-Pilotjob, does not impact the overall performances, proving to be an excellent tool to manage large computational campaigns on HPC systems. We are currently working to extend this support to more applications and simulation scenarios. Successful implementation of such middleware on HPC clusters would substantially facilitate performing complicated workflows on HPCs at large scale, including of course exascale type of runs. LEXIS workflow CompBioMed and LEXIS, (xxxx://xxxxx-xxxxxxx.xx), are collaborating to create a workflow designed to introduce a level of resilience to exascale HPC simulations. Future exascale machines will have a very high node count. Nodes individually have a reasonable mean time to failure, or MTF; however, when one aggregates 10-100 thousand nodes together in a single system, the overall MTF is far higher. Moreover, given exascale applications will employ MPI, and a typical MPI simulation will abort if a single MPI Task fails, then a node failure will cause entire simulations to crash. Computational biomedical simulations employing these Exascale platforms may well employ time- and safety-critical simulations, where results are required at the operating table in faster than real-time....
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