Parallelism means executing multiple computational operations at the same time instead of making one processor handle everything in sequence. In AI, that shortcut is less a luxury than a practical way to make demanding workloads feasible. section 1.8 section 10.8
Parallelism matters because it can:
- Speed up intensive AI work by dividing compatible operations across specialized hardware. section 10.8
- Support faster model training and inference, including real-time or near-real-time applications. section 10.8
- Reduce energy use and operating costs when AI workloads run on suitable accelerators. section 10.8
Classical and quantum routes
A GPU, FPGA, or ASIC can perform many matrix calculations and neural-network operations in parallel, improving AI performance. Quantum computing takes a different route: superposition lets qubits represent combinations of states, creating the potential to process many possibilities simultaneously. That potential is promising, not a magic “make every problem instant” button. section 10.8 section 10.6
