Memory

The GPU Optimization Playbook: Architecture, Memory, and Balance Most “GPU optimization” advice is a bag of tricks: coalesce here, unroll there, add __restrict__ and pray. Tricks are the output of optimization, not the method. The method is smaller and more durable: understand the machine, find the resource that is actually saturated, and rebalance work toward the resources that are idle. Almost every GPU kernel is limited by data movement, not arithmetic. Once you internalize that, the whole catalog of techniques collapses into three questions. ...

ZeRO: Partitioning Optimizer State, Gradients, and Parameters Plain DDP is clean but wasteful. Every data-parallel rank stores the same parameters, gradients, fp32 master weights, and optimizer moments. At small scale that redundancy is convenient. At LLM scale it is the memory wall. ZeRO, the Zero Redundancy Optimizer, keeps data-parallel semantics and removes the redundant storage one category at a time. ZeRO-1 shards optimizer state. ZeRO-2 shards optimizer state and gradients. ZeRO-3 shards optimizer state, gradients, and parameters (arXiv:1910.02054). ...