The present invention relates to the field of
computer technology, and in particular to a parallel
data processing method based on multiple devices. The content includes: calculating the performance matrix of the computing device and the complexity matrix of the task, and combining them to generate a dynamic
task mapping matrix; based on the dynamic
task mapping matrix, performing dynamic task allocation, and then calculating the
data dependency between the device and the task, and performing
data synchronization between the devices to obtain the
data synchronization rate between the devices; generating the
global optimal task allocation through distributed adaptive iterative optimization. It solves the problem that the traditional method does not fully consider the differences in performance between devices, network delays and storage capabilities, resulting in waste of computing resources and low overall
system efficiency; ignores the changes in task complexity over time and the
impact of fluctuations in device performance, resulting in extended task
processing time; fails to reasonably adjust the
data synchronization rate, which easily leads to
data transmission bottlenecks and
network congestion, seriously affecting the
parallel processing performance of the
system.