Our Objectives:
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.
Main Results of ComPat:
ComPat has delivered a design of three Multiscale Computing Patterns (MCP), implemented MCP algorithms and software, and tested this software on a Pan-European Experimental Execution Environment operated by the project. ComPat delivered an integrated software stack, relying on QCG, to execute a range of multiscale applications on the EEE, using the MCP algorithms and software. This resulted both in a proof of concept of the vision of MCPs, as well as detailed performance measurement of multiscale applications executed with the MCP algorithms and software. ComPat has strongly disseminated these results in papers, conferences, workshops, schools, and a webinar, as well as by creating a set of videos to explain the major developments in ComPat.
