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death and gravity: Process���Thread���Pool���Executor: when I���/���O becomes CPU-bound
- When doing I/O bound tasks in parallel, increasing the number of threads in ThreadPoolExecutor may not always result in faster performance, as CPU utilization can become a bottleneck.
- Using ProcessPoolExecutor can be an alternative, but it may use more memory and not always provide significant performance gains.
- The article introduces the concept of a ProcessThreadPoolExecutor that spreads work transparently across processes and threads to maximize performance.
- By implementing a custom executor that combines ProcessPoolExecutor and ThreadPoolExecutor, the article demonstrates improved performance with better utilization of resources.
- The ProcessThreadPoolExecutor efficiently handles work distribution across processes and threads, offering a balance between performance and memory usage.
- The article also discusses the challenges of handling process failures and ensuring proper cleanup of resources in a multi-process, multi-threaded environment.
- The experimental free threading support in Python 3.13 shows promising performance improvements without the complexities involved in custom executor implementations.
- Overall, the article presents practical solutions for optimizing CPU-bound workloads by intelligently utilizing processes and threads to achieve better performance.
- The ProcessThreadPoolExecutor design showcased in the article offers a valuable approach to addressing performance bottlenecks in CPU-bound tasks.
- By combining the benefits of process-based parallelism with thread-based concurrency, the custom executor provides a flexible and efficient solution for workload optimization.
- The article highlights the importance of balancing CPU utilization, memory usage, and handling process failures when optimizing parallel processing tasks for enhanced performance.
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