physics_in_parallel-4.0.0-alpha.1 has been yanked.

Physics in Parallel

ALPHA / BREAKING UPDATE

4.0.0-alpha.1 is an unstable, clean-slate rewrite. It is not compatible with PiP 3.x. Public APIs and serialized representations may change without migration support before 4.0.0. Pin the exact alpha version and do not treat alpha data formats as archival.

Physics in Parallel (PiP) provides reusable multicore numerical building blocks for simulations: backend-agnostic tensors, spatial sampling, lattices, pair generation, and ready-to-use particle models.

Installation

During the alpha, pin the exact release:

[dependencies]
physics_in_parallel = "=4.0.0-alpha.1"

PiP requires Rust 1.97 or newer.

Breaking Changes From 3.x

PiP 4.0 intentionally provides no compatibility aliases, migration wrappers, or readers for 3.x data. The main changes are:

Tensor<T>, Matrix<T>, and VectorList<T> are backend-agnostic public types with explicitly selected dense or sparse storage.
Random operations take ResolvedRng; optional seeds and implicit entropy fallbacks are gone.
Numerical foundations and ready-made physical models use independent prelude::basic and prelude::models imports.
One process-wide set_max_threads setting replaces per-object worker controls. PiP uses the caller's active Rayon pool.
Direct validated Serde is the conversion boundary. PiP no longer owns file, output-directory, reducer, checkpoint, or JSON-string policy.
PiP constructors accept semantic runtime values. Applications own grouped configuration types and workflow adapters.

Thread-pool setup is the application's responsibility. PiP uses the active Rayon pool and never creates a pool. Configure one shared pool before starting concurrent work, especially when using a workflow runner, or the process may oversubscribe the machine. Call set_max_threads once at startup to cap worker participation by any single PiP method.

API Layers

Domain roots are authoritative: math, rng, space, models, and threading. Three independent preludes are available for convenience:

use physics_in_parallel::prelude::basic::*;
use physics_in_parallel::prelude::models::*;
use physics_in_parallel::prelude::advanced::*;

The basic prelude contains numerical, random, spatial, and execution tools. The models prelude contains particle state and physical behavior. The advanced prelude is opt-in and exposes raw storage and generic engines. No prelude imports another, and domain roots remain available for explicit imports.

Quick Start

use physics_in_parallel::prelude::basic::{
    ResolvedRng, RngMethod, VectorSamplingMethod, set_max_threads,
};
use physics_in_parallel::prelude::models::{
    ParticleSelection, Spring, SpringNetwork, create_template, randomize_r,
};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    set_max_threads(Some(4))?;

    let mut particles = create_template(2, 2)?;
    let rng = ResolvedRng::new(42, RngMethod::IndexedSplitMix64);
    randomize_r(
        &mut particles,
        VectorSamplingMethod::Uniform { low: -1.0, high: 1.0 },
        rng,
    )?;

    let mut network = SpringNetwork::new();
    network.insert((0, 1), Spring::new(1.0, 0.5, None)?)?;
    network.apply(&mut particles, ParticleSelection::AliveOnly)?;
    Ok(())
}

Run the complete example with cargo run --example basic_particle.

Universal Containers

Tensor<T>, Matrix<T>, and VectorList<T> each hide a dense or sparse representation behind one public mathematical type. Both representations are fundamental; callers choose explicitly through dense-value or sparse-entry constructors.

storage_kind, make_dense, and make_sparse control representation.
PiP never changes representation implicitly.
Allocating operations preserve the receiver's representation.
*_into operations preserve the caller-selected output representation.
Direct Serde preserves representation.
Basic access uses coordinates and returns values.
Flat indices, contiguous slices, and stored-entry traversal require the advanced RawStorage trait.

Sparse storage does not make every operation sparse-cost. Logical values(), general mapping, and operations that produce many nonzero values can require time or memory proportional to the full logical size. Read each method's # Complexity and # Result storage sections before choosing a representation.

Randomness

Every public stochastic API accepts a fully specified ResolvedRng. PiP has no optional seed, hidden default method, or implicit entropy fallback.

let reproducible = ResolvedRng::new(7, RngMethod::ChaCha12);
let nondeterministic = ResolvedRng::from_entropy(RngMethod::ChaCha12);

Indexed operations are schedule-independent. Long-lived stochastic objects retain the resolved RNG and any counter needed for reproducible continuation. When PiP is used with a workflow system, the application adapts the workflow's purpose-derived seed into ResolvedRng; PiP does not depend on or know about that workflow.

Models

PhysObj is canonical particle state. Typed attribute access returns universal VectorList values, and invalid state is reported through ParticleStateError. Integrators and thermostats require Send; immutable behaviors that may be shared across workers require Send + Sync.

Spring and PowerLawDecay are validated immutable values. Their networks use particle pairs as identity, grow the minimum endpoint bound automatically, and return the previous law on replacement. Bulk insertion is transactional. Network deserialization rejects duplicate pairs.

Serialization

Direct validated Serde is PiP's only conversion contract. PiP does not provide JSON-string helpers, payload APIs, file writers, reducers, output directories, or checkpoint schedules. Applications and workflow systems own persistence and aggregation policy.

API details live in the crate's rustdoc. Read the advanced API guide before using lower-level facilities, and see the example guide for the checked example inventory.

physics_in_parallel 4.0.0-alpha.1