The invention provides a JAX-based high-performance
discrete event simulation method and
system, and the method comprises the steps: firstly completing environment configuration and initialization through an immutable
data structure, and generating an EnvParas object with fixed static parameters; secondly,
processing event logic by adopting a pure function steepv, and realizing side-effect-free functional event
processing and state iteration by combining JAX condition control primitive and lax.scan iterative loop; then, a
batch processing task is generated through job packaging, and job-level parallel acceleration is achieved on multiple GPU / TPU devices by means of jax. Jit compiling and jax. Vmap vectorization; and finally, packaging a standardized AI interactive interface, supporting external
algorithm calling and finishing result post-
processing.
Functional programming and JAX
hardware acceleration are combined, the
simulation speed is increased, meanwhile, the event dynamic accuracy is guaranteed,
reinforcement learning large-scale sampling can be seamlessly supported, and the method is suitable for AI application scenes in the field of operational research.