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H2020

ComPat Report Summary

Project ID: 671564
Funded under: H2020-EU.1.2.2.

Periodic Reporting for period 1 - ComPat (Computing Patterns for High Performance Multiscale Computing)

Reporting period: 2015-10-01 to 2017-03-31

Summary of the context and overall objectives of the project

Our main objective is to develop generic and reusable High Performance Multiscale Computing algorithms that will enable us to tackle scientific grand challenges at the exascale. The algorithms will provide scalability, robustness, resiliency, and efficiency of multiscale applications with extreme data requirements. We will formalise three multiscale computing patterns, all of them incorporating customized algorithms for load balancing, data handling, fault tolerance and energy consumption under generic exascale application scenarios, as well as performance prediction models. We will develop and implement all algorithms required by the three multiscale computing patterns, co-designing with the anticipated characteristics of exascale machines and providing strategies for optimisation with respect to such characteristics. We will develop an application toolkit needed to instantiate the computing patterns which in turn will allow multiscale simulations to reach exascale performance. Adopting selected middleware, we will realise an Experimental Execution Environment on HPC resources available to the project. We will implement nine grand challenge applications as instantiations of the multiscale computing patterns. Their scalability and performance will be measured on available high performance computing systems and will be predicted for future exascale systems. The added value of our approach for software engineering for extreme parallelism will be demonstrated.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The main result is the design and implementation of multiscale computing patterns software and services, operational on the ComPat Experimental Execution Environment, with two multiscale computing patterns being realized and used by three different applications.
To reach this point the project has achieved a large number of results, that can be summarized as follows:
1) We have an application portfolio containing nine different applications from four domains, and all nine applications have been made ready for ComPat. These applications drive forward the research and developments within ComPat, and are representative for a wide range of multiscale applications that need HPC.
2) We have laid theoretical foundations for the concept of Multiscale Computing Patterns, in context of the Multiscale Modelling and Simulation Framework, as being motifs in task graphs. For all three patterns we have identified these generic MCP task graphs.
3) We have designed and implemented Multiscale Computing Patterns and algorithms software, and interfaced it with QCG.
4) We have instantiated Instantiated the three fast-track applications (calculating ligand-protein binding affinities, global turbulence simulations and Red Blood Cells (RBCs) with platelet transport) as two Multiscale Computing Patterns (Extreme Scaling and Replica Computing) requiring two different coupling environments (MUSCLE2 for ES and file-based data exchange/FabSim for RC) and demonstrated their operation and performance.
5) We have delivered tools to measure performance of multiscale applications running in HPC environments, both for MUSCLE2 Custom Metric and Partial Report and for Energy profiling.
6) We have designed the ComPat System Architecture and released ComPat specific QCG middleware services and tools.
7) We realized the ComPat Experimental Execution Environment, with all 3 sites (LRZ, PSNC, STFC) fully integrated.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

ComPat strongly contributes to the HPC strategy in Europe, by demonstrating new avenues for effective and energy efficient use of HPC for a potentially very wide range of multiscale applications. This will help in realization of exascale performance levels by applications developers, will influence next generation compute architectures, and will influence policies of funding organisations and service providers to create the infrastructure needed for exascale deployment and new use cases for HPC. Its current progress beyond state of the art, as summarized above, already leads to such potential impact as ComPat actively joins discussion on next HPC in Europe, ‘towards the Exascale’.

In the specific application domains, scientific results will arise from improved fidelity of simulations in applications areas. We have already seen this in the results obtained in the extreme scale simulations carried out with the Binding Affinity Calculator on SuperMuc.

ComPat is educating and training a new generation of researchers and developers with enhanced skills in what we call High Performance Multiscale Computing. We are pushing this paradigm forward, as we believe that this will be very beneficial for use of future European Exascale HPC infrastructures.

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