Choosing Threads or Processes for Python Trading Simulations
Summary
The document compares Python threading and multiprocessing for improving simulation performance, with Monte Carlo pricing and strategy backtests as relevant examples. It explains that CPython’s Global Interpreter Lock limits CPU-bound Python threads to one thread executing Python bytecode at a time, so threading is more useful for tasks that spend time waiting on network or other input/output operations. The article illustrates this distinction with a CPU-heavy list-generation example.
Multiprocessing starts separate operating-system processes, each with its own interpreter and lock, allowing CPU work to run across cores. The sample timings show lower wall-clock time for the process-based example, while the article cautions against treating that result as a general speedup guarantee. Process startup and data movement add overhead, and hardware, cache behavior, and the portion of work that can be parallelized affect gains. The guidance is most applicable to independent simulation tasks with limited shared state; it does not provide a broad benchmark across trading workloads or Python implementations.
Key ideas
- In CPython, the Global Interpreter Lock limits CPU-bound gains from Python threads.
- Threads can improve throughput when tasks are mainly waiting on input/output.
- Separate processes can use multiple cores for CPU-bound work because each has its own interpreter.
- Interprocess startup and data transfer can reduce multiprocessing benefits.
- Observed speedups depend on the workload and hardware, and should not be generalized from a toy example.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.