Parallel execution

Parallel execution splits a single computational workload into smaller, independent tasks processed simultaneously across multiple CPU cores or machine nodes to slash overall runtime.

To bypass the physical limits of single-core clock speeds, parallel execution divides a program's instructions into concurrent threads or processes. By distributing these tasks across multi-core processors or distributed clusters (using frameworks like OpenMP for shared memory or MPI for message passing), systems execute calculations in tandem rather than waiting in a sequential queue. This approach is the backbone of modern high-performance computing (HPC), enabling teams to accelerate heavy workloads (such as training 100-billion-parameter machine learning models or running complex physics simulations) from weeks of sequential processing down to hours of coordinated execution.

https://hpc.llnl.gov/documentation/tutorials/introduction-parallel-computing-tutorial