# [−][src]Crate triple_accel

# triple_accel

Rust edit distance routines accelerated using SIMD. Supports fast Hamming, Levenshtein, restricted Damerau-Levenshtein, etc. distance calculations and string search.

Although vectorized SIMD code allows for up to 20-30x speedups over their scalar counterparts,
the difficulty of handling platform-dependent SIMD code makes SIMD routines less attractive.
The goal of this library is to provide an easy-to-use abstraction over SIMD edit distance routines
that fall back to scalar routines if the target CPU architecture is not supported.
Additionally, all limitations and tradeoffs of the edit distance routines should be provided upfront
so the user knows exactly what to expect.
Finally, this library should lead to performance boosts on both short and longer strings, so it
can be used for a variety of tasks, from bioinformatics to natural language processing.
`triple_accel`

is very lightweight: it only has dependencies on other crates for benchmarking.
It can be built on machines without CPUs that have AVX2 or SSE4.1 support. It can also run on
machines without SIMD support by automatically using scalar alternatives.

## Features

This library provides routines for both searching for some needle string in a haystack string and calculating the edit distance between two strings. Hamming distance (mismatches only), Levenshtein distance (mismatches + gaps), and restricted Damerau-Levenshtein distance (transpositions + mismatches + gaps) are supported, along with arbitrary edit costs. This library provides a simple interface, in addition to powerful lower-level control over the edit distance calculations.

At runtime, the implementation for a certain algorithm is selected based on CPU support, going down the list:

- Vectorized implementation with 256-bit AVX vectors, if AVX2 is supported.
- Vectorized implementation with 128-bit SSE vectors, if SSE4.1 is supported.
- Scalar implementation.

Currently, vectorized SIMD implementations are only available for x86 or x86-64 CPUs. However, after compiling this library on a machine that supports those SIMD intrinsics, the library can be used on other machines. Additionally, the internal data structure for storing vectors and the bit width of the values in the vectors are selected at runtime for maximum efficiency and accuracy, given the lengths of the input strings.

## Limitations

Due to the use of SIMD intrinsics, only binary strings that are represented with `u8`

bytes
are supported. Unicode strings are not currently supported.

## Notation

Quick notation notes that will often appear in the code/documentation:

`k`

- the number of edits that are allowed`a`

and`b`

- any two strings; this is usually used for edit distance routines`needle`

and`haystack`

- any two strings; we want to search for where needle appears in haystack

## Examples

Calculating the Hamming distance (number of mismatches) between two strings is extremely simple:

use triple_accel::*; let a = b"abcd"; let b = b"abcc"; let dist = hamming(a, b); assert!(dist == 1);

By default, SIMD will be used if possible. Similarly, we can easily calculate the Levenshtein distance (character mismatches and gaps all have a cost of 1) between two strings with the following code:

use triple_accel::*; let a = b"abc"; let b = b"abcd"; let dist = levenshtein_exp(a, b); assert!(dist == 1);

This uses exponential search to estimate the number of edits between `a`

and `b`

, which makes it
more efficient than the alternative `levenshtein`

function when the number of edits between `a`

and `b`

is low.

In addition to edit distance routines, `triple_accel`

also provides search routines. These
routines return an iterator over matches that indicate where the `needle`

string matches the `haystack`

string. `triple_accel`

will attempt to maximize the length of matches that end at the same position and
remove shorter matches when some matches fully overlap.

use triple_accel::*; let needle = b"helllo"; let haystack = b"hello world"; let matches: Vec<Match> = levenshtein_search(needle, haystack).collect(); // note: start index is inclusive, end index is exclusive! assert!(matches == vec![Match{start: 0, end: 5, k: 1}]);

Sometimes, it is necessary to use the slightly lower level, but also more powerful routines
that `triple_accel`

provides. For example, it is possible to allow transpositions (character swaps)
that have a cost of 1, in addition to mismatches and gaps:

use triple_accel::levenshtein::*; let a = b"abcd"; let b = b"abdc"; let k = 2; // upper bound on allowed cost let trace_on = false; // return edit traceback? let dist = levenshtein_simd_k_with_opts(a, b, k, trace_on, RDAMERAU_COSTS); // note: dist may be None if a and b do not match within a cost of k assert!(dist.unwrap().0 == 1);

Don't let the name of the function fool you! `levenshtein_simd_k_with_opts`

will still fall back to
the scalar implementation if AVX2 or SSE4.1 support is not available. It just prefers to use SIMD
where possible.

For most common cases, the re-exported functions are enough, and the low level functions do not have to be used directly.

## Re-exports

`pub use hamming::hamming;` |

`pub use hamming::hamming_search;` |

`pub use levenshtein::levenshtein;` |

`pub use levenshtein::rdamerau;` |

`pub use levenshtein::levenshtein_exp;` |

`pub use levenshtein::rdamerau_exp;` |

`pub use levenshtein::levenshtein_search;` |

## Modules

hamming | This module provides many Hamming distance routines. |

levenshtein | This module provides many Levenshtein distance routines. |

## Structs

Edit | A struct representing a sequence of edits of the same type. |

Match | A struct that describes a single matching location. |

## Enums

EditType | An enum describing possible edit operations. |

SearchType | An enum representing whether to return all matches or just the best matches. |

## Functions

alloc_str | This creates a vector with the alignment and padding for |

fill_str | Directly copy from the a source |