Abstract
With the continuous improvement of performance and the integration of artificial intelligence with traditional scientific computing, the scale of applications is gradually increasing, from millions to tens of millions of computing cores, which raises great challenges to achieve high scalability and efficiency of parallel applications on super-large-scale systems. Taking the exascale prototype system as an example, in this paper we first analyze the challenges of high scalability and high efficiency for parallel applications in the exascale era. To overcome these challenges, the optimization technologies used in the parallel supporting environment software on the exascale prototype system are highlighted, including the parallel operating system, input/output (I/O) optimization technology, parallel debugging technology, 10-million-core parallel algorithm, and mixed-precision method. Parallel operating systems and I/O optimization technology mainly support large-scale system scaling, while the parallel debugging technology, 10-million-core parallel algorithm, and mixed-precision method mainly enhance the efficiency of large-scale applications. Finally, the contributions to various applications running on the exascale prototype system are introduced, verifying the effectiveness of the parallel supporting environment design.
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