Periodic Reporting for period 1 - Exa-FireFlows (Exascale framework for supporting high-fidelity simulations of multiphase reacting flows in complex geometries)
Reporting period: 2019-06-01 to 2021-05-31
The main objectives of this work are:
O1: Development of a computational framework supporting a wide range of flow simulations in complex geometries and unstructured grids.
O2: Attaining maximum performance at an intra-node level.
O3: Advancing in inter-node scalability.
O4: Validation and Integration: A demonstrator will be created for predicting emissions using high fidelity simulations in both high resolution and large domains.
Having a CPU and GPU version of the code motivated finding a way to utilize all the resources of the computing nodes concurrently. The proposed execution strategy was name co-execution. Two binaries of the same code interact in a multiple-program multiple-data execution model.
One binary was optimized to CPU execution while the other to GPU execution. The modules regarding communication between processes are the same in both binaries; therefore, the programs can communicate their information transparently. A dynamic load balancing algorithm redistributes the workload for finding the sweet spot of the execution.
The co-execution obtained an acceleration of 20% with respect to executing only using the GPUs.
The benefits of the refactorization were applied to the sectional soot model. Understanding soot phenomena is essential for reducing the soot emissions on the combustion procedures. The sectional soot model demands many computing calculations, so it quickly becomes the bottleneck of high-fidelity simulations. Transferring the learning from the flamelet model allows us to reorganize the code similarly to expose parallelism and vector operations. As a result, the overall acceleration on high-fidelity simulations with soot was accelerated more than five times. The calculation of the sources terms will be released as a library to spread the efficient utilization of this model.
Regarding the emerging technologies, a sparse matrix vector-multiplication was implemented for using the FPGAs efficiently when working with sparsity patterns arisen from unstructured CFD codes. The resulting scheme runs up to 12 times faster than the current state-of-art algorithms for general matrices.
The dissemination of the achievements and results of the proposed project was a priority to engage as many users as possible. For that purpose, the researcher participated in four international conferences. Moreover, four articles have been published in international journals or conference proceedings. Two more papers are expected to be sent for publication by the end of this year. The work was also presented in the Center of Excellence in Combustion (CoEC) monthly meetings that join the leading institution of combustion research in Europe. The researcher participated in two outreach activities to show the importance of HPC to high-school students.
The results are currently being exploited by other European institutions through the Center of Excellence in Combustion and EXCELLERAT.