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SciML Numerical Utility Libraries

ExponentialUtilities.jl: Faster Matrix Exponentials

ExponentialUtilities.jl is a library for efficient computation of matrix exponentials. While Julia has a built-in exp(A) method, ExponentialUtilities.jl offers many features around this to improve performance in scientific contexts, including:

  • Faster methods for (non-allocating) matrix exponentials via exponential!
  • Methods for computing matrix exponential that are generic to number types and arrays (i.e. GPUs)
  • Methods for computing Arnoldi iterations on Krylov subspaces
  • Direct computation of exp(t*A)*v, i.e. exponentiation of a matrix times a vector, without computing the matrix exponential
  • Direct computation of ϕ_m(t*A)*v operations, where ϕ_0(z) = exp(z) and ϕ_(k+1)(z) = (ϕ_k(z) - 1) / z

ExponentialUtilities.jl includes complex adaptive time stepping techniques such as KIOPS in order to perform these calculations in a fast and numerically-stable way.

QuasiMonteCarlo.jl: Fast Quasi-Random Number Generation

QuasiMonteCarlo.jl is a library for fast generation of low discrepancy Quasi-Monte Carlo samples, using methods like:

  • GridSample(dx) where the grid is given by lb:dx[i]:ub in the ith direction.
  • UniformSample for uniformly distributed random numbers.
  • SobolSample for the Sobol sequence.
  • LatinHypercubeSample for a Latin Hypercube.
  • LatticeRuleSample for a randomly-shifted rank-1 lattice rule.
  • LowDiscrepancySample(base) where base[i] is the base in the ith direction.
  • GoldenSample for a Golden Ratio sequence.
  • KroneckerSample(alpha, s0) for a Kronecker sequence, where alpha is a length-d vector of irrational numbers (often sqrt(d)) and s0 is a length-d seed vector (often 0).
  • SectionSample(x0, sampler) where sampler is any sampler above and x0 is a vector of either NaN for a free dimension or some scalar for a constrained dimension.

DataInterpolations.jl: One-Dimensional Interpolations

DataInterpolations.jl is a library of one-dimensional interpolation schemes which are composable with automatic differentiation and the SciML ecosystem. It includes direct interpolation methods and regression techniques for handling noisy data. Its methods include:

  • ConstantInterpolation(u,t) - A piecewise constant interpolation.

  • LinearInterpolation(u,t) - A linear interpolation.

  • QuadraticInterpolation(u,t) - A quadratic interpolation.

  • LagrangeInterpolation(u,t,n) - A Lagrange interpolation of order n.

  • QuadraticSpline(u,t) - A quadratic spline interpolation.

  • CubicSpline(u,t) - A cubic spline interpolation.

  • BSplineInterpolation(u,t,d,pVec,knotVec) - An interpolation B-spline. This is a B-spline which hits each of the data points. The argument choices are:

    • d - degree of B-spline
    • pVec - Symbol to Parameters Vector, pVec = :Uniform for uniform spaced parameters and pVec = :ArcLen for parameters generated by chord length method.
    • knotVec - Symbol to Knot Vector, knotVec = :Uniform for uniform knot vector, knotVec = :Average for average spaced knot vector.

  • BSplineApprox(u,t,d,h,pVec,knotVec) - A regression B-spline which smooths the fitting curve. The argument choices are the same as the BSplineInterpolation, with the additional parameter h<length(t) which is the number of control points to use, with smaller h indicating more smoothing.

  • Curvefit(u,t,m,p,alg) - An interpolation which is done by fitting a user-given functional form m(t,p) where p is the vector of parameters. The user's input p is an initial value for a least-square fitting, alg is the algorithm choice used to optimize the cost function (sum of squared deviations) via Optim.jl and optimal ps are used in the interpolation.

These interpolations match the SciML interfaces and have direct support for packages like ModelingToolkit.jl.

PoissonRandom.jl: Fast Poisson Random Number Generation

PoissonRandom.jl is just fast Poisson random number generation for Poisson processes, like chemical master equations.

PreallocationTools.jl: Write Non-Allocating Code Easier

PreallocationTools.jl is a library of tools for writing non-allocating code that interacts well with advanced features like automatic differentiation and symbolics.

RuntimeGeneratedFunctions.jl: Efficient Staged Programming in Julia

RuntimeGeneratedFunctions.jl allows for staged programming in Julia, compiling functions at runtime with full optimizations. This is used by many libraries such as ModelingToolkit.jl to allow for runtime code generation for improved performance.

EllipsisNotation.jl: Implementation of Ellipsis Array Slicing

EllipsisNotation.jl defines the ellipsis array slicing notation for Julia. It uses .. as a catch-all for “all dimensions”, allowing for indexing like [..,1] to mean [:,:,:,1] on four dimensional arrays, in a way that is generic to the number of dimensions in the underlying array.

Third-Party Libraries to Note

Distributions.jl: Representations of Probability Distributions

Distributions.jl is a library for defining distributions in Julia. It's used all throughout the SciML libraries for specifications of probability distributions.

Note

For full compatibility with automatic differentiation, see DistributionsAD.jl

FFTW.jl: Fastest Fourier Transformation in the West

FFTW.jl is the preferred library for fast Fourier Transformations on the CPU.

SpecialFunctions.jl: Implementations of Mathematical Special Functions

SpecialFunctions.jl is a library of implementations of special functions, like Bessel functions and error functions (erf). This library is compatible with automatic differentiation.

LoopVectorization.jl: Automated Loop Accelerator

LoopVectorization.jl is a library which provides the @turbo and @tturbo macros for accelerating the computation of loops. This can be used to accelerating the model functions sent to the equation solvers, for example, accelerating handwritten PDE discretizations.

Polyester.jl: Cheap Threads

Polyester.jl is a cheaper version of threads for Julia, which use a set pool of threads for lower overhead. Note that Polyester does not compose with the standard Julia composable threading infrastructure, and thus one must take care not to compose two levels of Polyester, as this will oversubscribe the computation and lead to performance degradation. Many SciML solvers have options to use Polyester for threading to achieve the top performance.

Tullio.jl: Fast Tensor Calculations and Einstein Notation

Tullio.jl is a library for fast tensor calculations with Einstein notation. It allows for defining operations which are compatible with automatic differentiation, GPUs, and more.

ParallelStencil.jl: High-Level Code for Parallelized Stencil Computations

ParallelStencil.jl is a library for writing high-level code for parallelized stencil computations. It is compatible with SciML equation solvers and is thus a good way to generate GPU and distributed parallel model code.

Julia Utilities

StaticCompiler.jl

StaticCompiler.jl is a package for generating static binaries from Julia code. It only supports a subset of Julia, so not all equation solver algorithms are compatible with StaticCompiler.jl.

PackageCompiler.jl

PackageCompiler.jl is a package for generating shared libraries from Julia code. It builds the entirety of Julia by bundling a system image with the Julia runtime. It thus builds complete binaries that can hold all the functionality of SciML. Furthermore, it can also be used to generate new system images to decrease startup times and remove JIT-compilation from SciML usage.