Explore machine learning concepts related to Parallelism. Clear explanations and practical insights.
Deep dive into the CUDA context object: control vs data plane, inventory (memory, modules, streams, events, graphs), push/pop/setCurrent stacks, primary retain/release, flags and limits, isolation, cost, and traps.
A CUDA stream is an in-order queue of GPU ops. Overlap H→D, kernels, and D→H across streams — and avoid the default-stream trap that serializes everything.
Decision map for CUDA: a context is per-process GPU state, a stream is an in-order queue inside a context, and MPS shares one context across processes. Pick the layer that matches the problem.
Flynn's Classification explained — SISD, SIMD, MISD, MIMD with interactive architecture explorer, SIMD evolution from MMX to AMX, branch divergence visualization, and workload-architecture throughput comparison.
Why exclusive CUDA contexts leave SMs idle under multi-process load, how MPS multiplexes clients through a shared context, thread percentage caps, and when to pick exclusive, MPS, or MIG.