Ideal parallelism is like the holy grail of computing, right? On paper, it’s flawless, but in reality, we’re stuck with trade-offs.
I’ve worked with MPI and OpenMP, and even with optimized code, you hit walls—sync delays, memory bottlenecks, you name it.
That said, frameworks like TensorFlow and PyTorch do a decent job hiding the mess under the hood. They’re not *ideal*, but they get close for specific workloads.
Quantum computing? Maybe someday, but right now it’s more hype than help for this problem.
I’ve worked with MPI and OpenMP, and even with optimized code, you hit walls—sync delays, memory bottlenecks, you name it.
That said, frameworks like TensorFlow and PyTorch do a decent job hiding the mess under the hood. They’re not *ideal*, but they get close for specific workloads.
Quantum computing? Maybe someday, but right now it’s more hype than help for this problem.