Domain-specific programming frameworks are usually effective to simplify the development of large-scale applications on supercomputers. This paper introduces a programming framework named JAUMIN for large-scale numerical applications on unstructured meshes. Based on this framework, serial codes are only required to be written for large-scale parallel application suitable for modern supercomputers with tens of thousands of CPU cores.
Summary High‐Performance Computing (HPC) systems and Computational Fluid Dynamics (CFD) have made significant progress in recent years; however, as the basis of the large‐scale parallel computing, the massive grid generation of billions of cells has become a bottleneck problem. In this study, a parallel grid generation technique is proposed to generate large‐scale mixed grids with arbitrary cell types and scales. The basic idea of our method is analogous to the global mesh refinement technique. An initial coarse grid with arbitrary cell types is regarded as a background mesh which is partitioned into subzones, and subzones are assigned onto different CPU cores. After the cells and faces in each subzone are split, the inserted new points of the solid wall are projected onto the original CAD entities to preserve the geometry accurately. Finally, the tangled cells caused by the projection in the boundary layer are untangled by a local Radial Basis Function mesh deformation technique. Furthermore, a parallel partition approach and an efficient wall distance computing technique for massive grids are developed also to shorten the preprocessing time. The tests show that the preprocessing efficiency has been increased by two or three orders compared with traditional methods. Billions of grids are generated for the AIAA JSM high‐lift model and the Chinese CHN‐T1 transport model to test the ability of the parallel grid generation technique. The maximum scale up to 19 billion mixed elements is generated using 16 384 CPU cores in parallel, and the mesh quality is acceptable for CFD simulations.
This paper designs a tangible programming tool, E-Block, for children aged 5 to 9 to experience the preliminary understanding of programming by building blocks. With embedded artificial intelligence, the tool defines the programming blocks with the sensors as the input and enables children to write programs to complete the tasks in the computer. The symbol on the programming block's surface is used to help children understanding the function of each block. The sequence information is transferred to computer by microcomputers and then translated into semantic information. The system applies wireless and infrared technologies and provides user with feedbacks on both screen and programming blocks. Preliminary user studies using observation and user interview methods are shown for E-Block's prototype. The test results prove that E-Block is attractive to children and easy to learn and use. The project also highlights potential advantages of using single chip microcomputer (SCM) technology to develop tangible programming tools for children. Hindawi Publishing Corporation
Fine‐grained lock is frequently used to mitigate lock contention in the multithreaded program running on a shared‐memory multicore processor. However, a concurrent program based on the fine‐grained lock is hard to write, especially for beginners in the concurrent programming course. How to help participants learn fine‐grained lock has become increasingly important and urgent. To this end, this paper presents a novel refactoring‐based approach to enhance the learning effectiveness of fine‐grained locks. Two refactoring tools are introduced to provide illustrating examples for participants by converting original coarse‐grained locks into fine‐grained ones automatically. Learning effectiveness and limitations are discussed when refactoring tools are applied. We evaluate students' outcomes with two benchmarks and compare their performance in Fall 2018 with those in Fall 2019. We also conduct experiments on students' outcomes by dividing them into two groups (A and B) in a controlled classroom where participants in group A learn the fine‐grained locks with the help of refactoring tools while those in group B do not access these tools. Evaluation of the results when they have been taught with the refactoring‐based approach reveals a significant improvement in the students' learning.
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