Particle transport simulations using stochastic methods face significant challenges on conventional von Neumann architectures, particularly due to random branching events and irregular memory access patterns. These limitations stem from the fundamental mismatch between probabilistic algorithms and deterministic computing paradigms. To bridge the gap between architecture and algorithms, a probabilistically tunable true random number generator was developed based on spintronic and ferroelectric devices. The physical randomness of spintronic devices was leveraged to provide a physical random source for the architecture, and the throughput of random bits was enhanced through optimized control logic and writing mechanisms. Next, programmable synapses were designed based on the memristive properties of ferroelectric devices, enabling non-volatile continuous weight storage with tunable probabilities. The experimental results indicate that the proposed approach achieves performance improvements ranging from 171× to 1028× compared to a general-purpose CPU when solving a sample transport problem. Furthermore, compared to existing STT-MTJ-based (spin-transfer torque magnetic tunnel junction-based) true random number generators, the developed method not only enables tunable probability random sampling but also achieves a throughput of 303 Mb/s when generating uniformly distributed random sequences.