Crate rusty_green_kernel[][src]

Welcome to rusty-green-kernel. This crate contains routine for the evaluation of sums of the form

$$f(\mathbf{x}_i) = \sum_jg(\mathbf{x}_i, \mathbf{y}_j)c_j$$

and the corresponding gradients

$$\nabla_{\mathbf{x}}f(\mathbf{x}_i) = \sum_j\nabla_{\mathbf{x}}g(\mathbf{x}_i, \mathbf{y}_j)c_j.$$ The following kernels are supported.

  • The Laplace kernel: $g(\mathbf{x}, \mathbf{y}) = \frac{1}{4\pi|\mathbf{x} - \mathbf{y}|}$.
  • The Helmholtz kernel: $g(\mathbf{x}, \mathbf{y}) = \frac{e^{ik|\mathbf{x} - \mathbf{y}|}}{4\pi|\mathbf{x} - \mathbf{y}|}$
  • The modified Helmholtz kernel:$g(\mathbf{x}, \mathbf{y}) = \frac{e^{-\omega|\mathbf{x} - \mathbf{y}|}}{4\pi|\mathbf{x} - \mathbf{y}|}$

Within the library the $\mathbf{x}_i$ are named targets and the $\mathbf{y}_j$ are named sources. We use the convention that $g(\mathbf{x}_i, \mathbf{y}_j) := 0$, whenever $\mathbf{x}_i = \mathbf{y}_j$.

The library provides a Rust API, C API, and Python API.

Installation hints

The performance of the library strongly depends on being compiled with the right parameters for the underlying CPU. Almost any modern CPU supports AVX2 and FMA. To activate these features compile with

export RUSTFLAGS="-C target-feature=+avx2,+fma" 
cargo build --release

The activated compiler features can also be tested with cargo rustc -- --print cfg.

To compile and install the Python module make sure that the wanted Python virtual environment is active. The installation is performed using maturin, which is available from Pypi and conda-forge.

After compiling the library as described above use

maturin develop --release -b cffi

to compile and install the Python module. It is important that the RUSTFLAGS environment variable is set as stated above. The Python module is called rusty_green_kernel.

Rust API

The sources and targets are both arrays of type ndarray<T> with T=f32 or T=f64. For M targets and N sources the sources are a (3, N) array and the targets are a (3, M) array.

To evaluate the kernel matrix of all interactions between a vector of sources and a vector of targets for the Laplace kernel use

kernel_matrix = make_laplace_evaluator(sources, targets).assemble()

To evaluate $f(\mathbf{x}_i) = \sum_jg(\mathbf{x}_i, \mathbf{y}_j)c_j$ we define the charges as ndarray of size (ncharge_vecs, nsources), where ncharge_vecs is the number of charge vectors we want to evaluate and nsources is the number of sources. For Laplace and modified Helmholtz problems charges must be of type f32 or f64 and for Helmholtz problems it must be of type Complex<f32> or Complex<f64>.

We can then evaluate the potential sum by

potential_sum = make_laplace_evaluator(sources, targets).evaluate(
        charges, EvalMode::Values, EvalMode::Value, ThreadingType::Parallel)

The result potential_sum is a real ndarray (for Laplace and modified Helmholtz) or a complex ndarray (for Helmholtz). It has the shape (ncharge_vecs, ntargets, 1). For EvalMode::Value the function only computes the values $f(\mathbf{x}_i)$. For EvalMode::ValueGrad the array potential_sum is of shape (ncharge_vecs, ntargets, 4) and returns the function values and the three components of the gradient along the most-inner dimension. The value ThreadingType::Parallel specifies that the evaluation is multithreaded. For this the Rayon library is used. For the value ThreadingType::Serial the code is executed single-threaded. The enum ThreadingType is defined in the crate rusty-kernel-tools.

Basic access to sources and targets is provided through the trait DirectEvaluatorAccessor, which is implemented by the struct DirectEvaluator. The Helmholtz kernel uses the trait ComplexDirectEvaluator and the Laplace and modified Helmholtz kernels use the trait RealDirectEvaluator.


The C API in c_api provides direct access to the functionality in a C compatible interface. All functions come in variants for f32 and f64 types. Details are explaineed in the documentation of the corresponding functions.

Python API

For details of the Python module see the Python documentation in the rusty_green_kernel module.


pub use evaluators::*;
pub use kernels::EvalMode;



This module defines C API function to access all assembly and evaluation routines.


Definitions of the supported Greens function kernels.