Since the network booting time overhead is high in supercomputer system, the idea that the network boot distribution algorithm is one of the main factors affecting the network boot performance and the main direction of optimizing network boot performance was proposed. Firstly, the main factors affecting large-scale network boot performance were analyzed. Secondly, combined with a typical supercomputer system, the network boot data flow topologies of Supernode Cyclic Distribution Algorithm (SCDA) and Board Cyclic Distribution Algorithm (BCDA) were analyzed. Finally, the pressure of above two algorithms on each network path branch and the available network performance were quantitatively analyzed. It can be seen that the bandwidth performance of BCDA is 1-20 times of that of SCDA. Theoretical analysis and model deduction show that the finer-grained mapping algorithm between compute nodes and boot servers can make as many boot servers as possible be used while boot some resources, reducing the premature competition for partial network resources and improving network boot performance.

Bernstein’s Batch-factor algorithm can test B-smoothness of a lot of integers in a short time. But this method costs so much memory that it’s widely used in theory analyses but rarely used in practice. Based on splitting product of primes into pieces, a hierarchical batch-factor algorithm cloud framework was proposed to solve this problem. This hierarchical framework made the development clear and easy, and could be easily moved to other architectures; Cloud computing framework borrowed from MapReduce made use of services provided by cloud clients such as distribute memory, share memory and message to carry out mapping of splitting-primes batch factor algorithm, which solved the great cost of Bernstein’s method. Experiments show that, this framework is with good scalability and can be adapted to different sizes batch factor in which the scale of prime product varies from 1.5GB to 192GB, which enhances the usefulness of the algorithm significantly.