Computational Constraints

Computational Constraints#

CPU Limitations

  • Clock speed plateaued (~2005)

  • Single-threaded gains diminishing

  • Parallelization is key

GPU Constraints

  • Massive parallelism for suitable workloads

  • High cost, limited availability

  • Not all algorithms benefit

Hardware Needs

  • ML: Tensor cores, TPUs

  • Simulation: High memory, fast interconnects

  • Data: High I/O bandwidth

The Challenge

  • Expensive to acquire

  • Limited availability

  • Code portability issues

Why So Slow?

  • Non-linear complexity (O(n²), O(n³))

  • Iterative convergence

  • Parameter sweeps, Monte Carlo

Impact

  • No interactive development

  • Costly failures

  • Different debugging strategies

  • Planning overhead