diff --git a/nalgebra-sparse/src/ops/mod.rs b/nalgebra-sparse/src/ops/mod.rs index 2857533f..4b51d2ee 100644 --- a/nalgebra-sparse/src/ops/mod.rs +++ b/nalgebra-sparse/src/ops/mod.rs @@ -1,15 +1,128 @@ //! Sparse matrix arithmetic operations. //! -//! TODO: Explain that users should prefer to use std ops unless they need to get more performance +//! This module contains a number of routines for sparse matrix arithmetic. These routines are +//! primarily intended for "expert usage". Most users should prefer to use standard +//! `std::ops` operations for simple and readable code when possible. The routines provided here +//! offer more control over allocation, and allow fusing some low-level operations for higher +//! performance. //! //! The available operations are organized by backend. Currently, only the [`serial`] backend //! is available. In the future, backends that expose parallel operations may become available. +//! All `std::ops` implementations will remain single-threaded and powered by the +//! `serial` backend. //! //! Many routines are able to implicitly transpose matrices involved in the operation. //! For example, the routine [`spadd_csr_prealloc`](serial::spadd_csr_prealloc) performs the //! operation `C <- beta * C + alpha * op(A)`. Here `op(A)` indicates that the matrix `A` can //! either be used as-is or transposed. The notation `op(A)` is represented in code by the //! [`Op`] enum. +//! +//! # Available `std::ops` implementations +//! +//! ## Binary operators +//! +//! The below table summarizes the currently supported binary operators between matrices. +//! In general, binary operators between sparse matrices are only supported if both matrices +//! are stored in the same format. All supported binary operators are implemented for +//! all four combinations of values and references. +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//! +//!
LHS (down) \ RHS (right)COOCSRCSCDense
COO
CSR+ - **
CSC+ - **
Dense+ - *
+//! +//! As can be seen from the table, only `CSR * Dense` and `CSC * Dense` are supported. +//! The other way around, i.e. `Dense * CSR` and `Dense * CSC` are not implemented. +//! +//! Additionally, [CsrMatrix](`crate::csr::CsrMatrix`) and [CooMatrix](`crate::coo::CooMatrix`) +//! support multiplication with scalars, in addition to division by a scalar. +//! Note that only `Matrix * Scalar` works in a generic context, although `Scalar * Matrix` +//! has been implemented for many of the built-in arithmetic types. This is due to a fundamental +//! restriction of the Rust type system. Therefore, in generic code you will need to always place +//! the matrix on the left-hand side of the multiplication. +//! +//! ## Unary operators +//! +//! The following table lists currently supported unary operators. +//! +//! | Format | AddAssign\ | MulAssign\ | MulAssign\ | Neg | +//! | -------- | ----------------- | ----------------- | ------------------- | ------ | +//! | COO | | | | | +//! | CSR | | | x | x | +//! | CSC | | | x | x | +//! | +//! # Example usage +//! +//! For example, consider the case where you want to compute the expression +//! `C <- 3.0 * C + 2.0 * A^T * B`, where `A`, `B`, `C` are matrices and `A^T` is the transpose +//! of `A`. The simplest way to write this is: +//! +//! ```rust +//! # use nalgebra_sparse::csr::CsrMatrix; +//! # let a = CsrMatrix::identity(10); let b = CsrMatrix::identity(10); +//! # let mut c = CsrMatrix::identity(10); +//! c = 3.0 * c + 2.0 * a.transpose() * b; +//! ``` +//! This is simple and straightforward to read, and therefore the recommended way to implement +//! it. However, if you have determined that this is a performance bottleneck of your application, +//! it may be possible to speed things up. First, let's see what's going on here. The `std` +//! operations are evaluated eagerly. This means that the following steps take place: +//! +//! 1. Evaluate `let c_temp = 3.0 * c`. This requires scaling all values of the matrix. +//! 2. Evaluate `let a_t = a.transpose()` into a new temporary matrix. +//! 3. Evaluate `let a_t_b = a_t * b` into a new temporary matrix. +//! 4. Evaluate `let a_t_b_scaled = 2.0 * a_t_b`. This requires scaling all values of the matrix. +//! 5. Evaluate `c = c_temp + a_t_b_scaled`. +//! +//! An alternative way to implement this expression (here using CSR matrices) is: +//! +//! ```rust +//! # use nalgebra_sparse::csr::CsrMatrix; +//! # let a = CsrMatrix::identity(10); let b = CsrMatrix::identity(10); +//! # let mut c = CsrMatrix::identity(10); +//! use nalgebra_sparse::ops::{Op, serial::spmm_csr_prealloc}; +//! +//! // Evaluate the expression `c <- 3.0 * c + 2.0 * a^T * b +//! spmm_csr_prealloc(3.0, &mut c, 2.0, Op::Transpose(&a), Op::NoOp(&b)) +//! .expect("We assume that the pattern of C is able to accommodate the result."); +//! ``` +//! Compared to the simpler example, this snippet is harder to read, but it calls a single +//! computational kernel that avoids many of the intermediate steps listed out before. Therefore +//! directly calling kernels may sometimes lead to better performance. However, this should +//! always be verified by performance profiling! mod impl_std_ops; pub mod serial;