Physics-in-Parallel

Physics in Parallel is a scientific computing package in Rust for massive, memory-bound simulations where full vectorization isn’t feasible. It targets workloads that exceed cache and RAM budgets, need irregular access patterns, or are best served by a hybrid SoA/AoS approach with block-wise/streaming execution.

Core Goals

1. Unified Scalar Abstraction
   A single Scalar trait unifies integers, floats, and complex types with a consistent API (conjugation, norms, sqrt, finiteness).

2. Tensor Infrastructure (Hybrid-layout Friendly)

   • Parallel elementwise ops (+, -, *, /) via Rayon.
   • Tile/block iterators and zero-copy views designed to cooperate with SoA and AoS neighbors.
   • Human-readable serialization (JSON, DSL strings).
   • Multiple backends availible:
     • Dense:
       • flat-memory tensors for cache efficiency.
     • Sparse:
       • Sparse and symmetry-aware storage backed by hashset.

3. 2D Tensor Infrastructure (Matrix + VectorList)

   • Matrix:
     • A wrapper for tensor that adds linalg methods support.
     • Allow slicing and viewing by row or col.
   • VectorList:
     • A wrapper for Matrix.
     • Each col represents a vector.
     • Allow bulk operations on vectors (normalization, ...)

4. Random Tensor Generation
   Efficient, chunked generators for random vectors (uniform, Gaussian, etc.), supporting streamed/partial fills for Monte Carlo and stochastic dynamics on large states.

5. Space Provide abstraction for space representations.

   • Dense:
     • A wrapper for dense tensor. The tensor represents a square lattice with periodic boundary.
     • Each site is accessible through a cartesion coordinate.
   • Sparse:
     • A wrapper for sparse tensor. It is best for describing continuous space containing finite elements.

6. Parallelism by Default (Under Memory Constraints)

   • Rayon-powered tile-level parallelism to respect cache lines and NUMA.
   • Block-wise maps/zips, fused passes, and streaming pipelines to reduce materialization.
   • Scales from laptops to HPC nodes without requiring full in-RAM tensors.

Why Hybrid SoA/AoS?

Some physics tasks (e.g., lattice kernels with long tails, neighbor lists, event-driven updates, or out-of-core grids) don’t map cleanly to a single layout or to global vectorization. Physics in Parallel embraces:

• SoA for bandwidth-bound sweeps (norms, reductions, scalar transforms).
• AoS (or AoS-like views) where locality across coordinates simplifies kernels or boundary logic.
• Block/tiling to operate on windows that fit in cache, enabling halos/ghost cells and sliding updates.
• Streaming/randomized passes to amortize RNG and avoid full materialization.

Use Cases

• Stochastic field dynamics: diffusion + Langevin noise on very large grids using streaming tiles.
• Ecology & population dynamics: lattice models with structured, possibly long-range dispersal kernels on domains too large for global vectorization.
• General numerical physics: PDE/ODE solvers, interacting-particle systems, and kernels mixing SoA scans with AoS-style neighbor ops.