Joblib shows 32 workers but only uses one CPU core at 100 percent

Optimized a parallel processing workload in Python to address severe multicore underutilization, where a parallel processing library reported 32 workers but only utilized a single CPU core due to serialization bottlenecks.

Issue

A parallel processing task using a process-based backend reported 32 concurrent workers but only utilized one CPU core at 100% capacity. This resulted in excessive single-core usage and poor performance, despite expectations of true multicore utilization. Monitoring confirmed that other cores remained idle, causing degraded performance.

Culprit

The root cause was serialization overhead inherent in process-based parallelism. Each task required serializing shared data structures, leading to inter-process communication (IPC) bottlenecks that exceeded the actual computation time. The workload involved accessing a shared data structure, forcing serialization for every task.

Solution

Backend Switch to Threading

Replaced the process-based backend with a threading-based approach to leverage shared memory and eliminate serialization overhead. This change allowed all worker threads to access shared data directly without IPC bottlenecks.

Technical Justification

1. Shared Memory Access: Threading enables direct memory access to shared data structures, eliminating serialization costs.
2. Balanced GIL Impact: The workload involved a mix of file I/O and CPU-bound operations, allowing threads to release the Global Interpreter Lock (GIL) during I/O and leverage CPU cores during computation.
3. Performance Improvement: Eliminated serialization overhead and IPC bottlenecks, resulting in balanced CPU utilization across all cores.

Results: All 32 CPU cores were actively utilized, reducing single-core overload and achieving expected parallel processing performance. The solution maintained identical functionality while resolving the core bottleneck.