Abstract:
Abstract: Aiming to the problem that high-performance computing centers， especially small and medium-sized computing centers， are unable to undertake large-scale computing jobs due to the decentralization of heterogeneous computing resources， this paper designs and implements a heterogeneous computing resource integration and scheduling platform to realize the unified management of heterogeneous computing resources such as X86， ARM and so on， as well as collaborative computing. The platform adopts a layered fusion scheduling architecture， utilizes cluster manager server （CMS） and job manager client （JMC） to dynamically monitor the resource status， and realizes collaborative parallel computing among heterogeneous computing nodes with the help of job scheduler （JS）. Through the master-slave JMC process collaboration and MPI reduction mechanism， cross-architecture data synchronization at the physical machine level is achieved， and parallel execution of a single job on heterogeneous computing nodes at the physical machine level is realized for the first time. To address the long-tail delay effects and synchronization overhead caused by performance imbalances in heterogeneous clusters， this paper proposes a deadline-constrained minimal resource algorithm （DCMR）， which minimizes computational resource consumption while ensuring job completion deadlines are met. Test results show that the platform has almost no loss of computing performance in heterogeneous environments， and the DCMR algorithm effectively improves the utilization efficiency of heterogeneous computing resources， providing a reliable system solution to deal with heterogeneous computing environments.

Key words: Key words: heterogeneous computing resource, resource scheduling, MPI, physical machine, small and medium-sized computing centers