Deterministic CPU multithreading: supernodal Cholesky, parallel elements, LBM blocks (#50)

* parallel: deterministic thread pool helpers for the solvers (fixed partitions, ordered results)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmAttHhUZTokgPeEJia8sm

* solver: supernodal left-looking Cholesky, multithreaded with a fixed operation order

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmAttHhUZTokgPeEJia8sm

* solver: parallel element matrices, stresses, Lanczos block solves and products (sums in the sequential order)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmAttHhUZTokgPeEJia8sm

* explicit: element updates in parallel, nodal forces added in element order (bit-identical to the sequential loop)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmAttHhUZTokgPeEJia8sm

* lbm: collision and streaming by fixed cell blocks in parallel, wall forces in link order (bit-identical)

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmAttHhUZTokgPeEJia8sm

* engine: threads setting for the solve commands (session default from MODELCRAFT_THREADS or the cores); bit-identity test on 1, 2 and 7 threads

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmAttHhUZTokgPeEJia8sm

* docs: rayon threads in ADR 0001, the parallel crate, supernodal Cholesky

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NmAttHhUZTokgPeEJia8sm

---------

Co-authored-by: jyviko <2501648+jyviko@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
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@@ -15,8 +15,11 @@
//! von Mises. Frequency analysis ([`eigen`]) runs shift-invert block Lanczos on the same
//! kind of factorization.
//!
//! Everything is sequential and in a fixed order, so the same model gives the same numbers bit
//! for bit; sizes and iterations are capped by [`Limits`].
//! Element matrices and stresses, the factorization's subtrees and dense tiles, the Lanczos
//! block solves and matrix products run on threads ([`modelcraft_parallel`]), but every number
//! is computed by one task in an order fixed by the model alone and sums are taken in a fixed
//! order, so the same model gives the same numbers bit for bit on any number of threads and on
//! every platform; sizes and iterations are capped by [`Limits`].
#![deny(clippy::unwrap_used, clippy::expect_used, clippy::panic, clippy::unimplemented, clippy::todo, clippy::unreachable)]
#![forbid(unsafe_code)]
// Matrix kernels read as the textbook formulas with index loops.