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+# Evaluation Dependency and Parallelism
+
+The deepest dividing line between parameter-search methods is not *how* they pick
+points but **whether one evaluation depends on another's result**. That single
+property — the *evaluation dependency structure* — decides how well a method
+parallelizes, how reproducible it is, and how its wall-clock time scales. [L]
+
+## Two dependency structures
+
+### Open-loop: independent evaluations
+
+In [grid](Grid-Search) and [random](Random-Search) search the entire set of
+trial points is determined *before any evaluation runs*. No point's evaluation
+needs any other point's result. Such a workload is **embarrassingly parallel**:
+little or no communication is needed between tasks, so it splits across as many
+workers as are available. [L]
+
+```mermaid
+flowchart LR
+  ENUM["Enumerate all trial points"] --> E1["Evaluate point 1"]
+  ENUM --> E2["Evaluate point 2"]
+  ENUM --> EN["Evaluate point N"]
+  E1 --> COL["Collect results"]
+  E2 --> COL
+  EN --> COL
+```
+
+With enough workers the wall-clock time approaches the cost of a *single*
+evaluation, and the result is independent of completion order — so it reproduces
+exactly regardless of scheduling. [L]
+
+### Closed-loop: dependent evaluations
+
+Every [adaptive method](Adaptive-Search) — Bayesian optimization, evolutionary
+algorithms, CMA-ES — chooses its next trial(s) *from results already seen*. That
+creates a data dependency from each step to the next: a chain that cannot be
+collapsed by adding workers. [L]
+
+```mermaid
+flowchart LR
+  INIT["Initialize batch"] --> EVAL["Evaluate batch"]
+  EVAL --> UPD["Update model or breed"]
+  UPD --> CONV{"Converged?"}
+  CONV -->|no| EVAL
+  CONV -->|yes| BEST["Return best"]
+```
+
+Parallelism is possible only *within* a batch or generation; between generations
+there is a **synchronization barrier** — every evaluation of a generation must
+finish before the next can be proposed. [L]
+
+## Why the structure bounds speedup
+
+The sequential fraction of a computation caps how much parallel hardware can
+help: **Amdahl's law** states that the speedup of a program is limited by the
+part that cannot be parallelized — even with infinitely many workers, a job that
+is 1% sequential can be sped up at most 100-fold. [L] An open-loop search has a
+near-zero sequential fraction (only the final collection step). A closed-loop
+search has an *irreducible* sequential fraction set by the number of generations:
+with $g$ sequential generations, wall-clock time is at least $g$ times the cost
+of one batch, no matter how wide each batch is. [L]
+
+| Property | Open-loop (grid / random) | Closed-loop (adaptive) |
+|---|---|---|
+| Inter-trial dependency | none | each step depends on prior results |
+| Parallelism | embarrassingly parallel | within a batch only, barriers between |
+| Wall-clock floor | one evaluation | generations times one batch |
+| Reproducibility | order-independent | needs fixed seed and evaluation order |
+| Sample efficiency | lower | higher |
+
+## Reproducibility note
+
+Independent evaluations reproduce regardless of the order in which workers finish
+them. A dependent search reproduces only if both the random seed *and* the order
+of evaluation are pinned, because a different order changes which results inform
+the next proposal. [L] This is why deterministic adaptive search takes more care
+than deterministic open-loop search.
+
+## Hybrids
+
+Population-based adaptive methods are *batch-parallel within a generation,
+sequential across generations* — they recover much of the parallel throughput of
+open-loop search while keeping the sample efficiency of adaptivity, trading a
+fixed number of barriers for it. [C]
+
+## Claim legend
+
+- **[L]** law / exact — a definition, identity, or theorem that does not vary.
+- **[C]** convention — a practitioner rule of thumb that varies by context.
+
+## References
+
+- *Embarrassingly parallel.* Wikipedia.
+  <https://en.wikipedia.org/wiki/Embarrassingly_parallel>
+- *Amdahl's law.* Wikipedia.
+  <https://en.wikipedia.org/wiki/Amdahl%27s_law>
+- *Bayesian optimization.* Wikipedia.
+  <https://en.wikipedia.org/wiki/Bayesian_optimization>
+- *Genetic algorithm.* Wikipedia.
+  <https://en.wikipedia.org/wiki/Genetic_algorithm>