Parallel simulation is usually presented as a supercomputing topic, wrapped in clusters, schedulers, and specialized languages. Parallel Simulation Experiments on a Personal Computer argues the opposite: the multicore laptop or desktop you already own is enough to run serious simulation experiments, quickly and reproducibly, in both R and Python.
The book is built around sim_template, a compact, extensible program presented in matching R and Python versions, that distributes a simulation across worker processes while giving each worker its own high-quality random number stream. From that foundation the book works through complete, honest examples, with timing tables, missteps, and diagnostics included, showing not just how to parallelize a simulation but how to trust the numbers that come back.
Key Features:

• Reproducible parallel random number streams built on the PCG64 generator, via dqrng in R and NumPy's SeedSequence in Python
• Two full case studies: bootstrap interval estimation for a segmented regression change point in R, and a SIRD epidemic model with inter-process migration in Python
• Practical chapters on counting cores, memory pressure, benchmarking properly, load balancing, debugging parallel code, and reproducibility
• A look beyond the personal computer (GPUs, cloud instances, and clusters) and at working alongside AI coding assistants
• Optional appendices explaining how random number generators work, from congruential generators and RANDU to PCG64

Written for applied statisticians, data scientists, and graduate students who use simulation in their work, the book assumes only basic statistics and some familiarity with R or Python. No parallel computing background is required.