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Spire is a numeric library for Scala which is intended to be generic, fast, and precise.

Using features such as specialization, macros, type classes, and implicits, Spire works had to defy conventional wisdom around performance and precision trade-offs. A major goal is to allow developers to write efficient numeric code without having to "bake in" particular numeric representations. In most cases, generic implementations using Spire's specialized type classes perform identically to corresponding direct implementations.

Spire is provided to you as free software under the MIT license.

Set up

Spire currently relies heavily on macros introduced Scala 2.10.0, as well as many improvements to specialization. The 0.3.0 release of Spire is expected shortly after the final Scala 2.10.0 release.

Until then, Spire 0.3.0-M4 (which requires Scala-2.10.0-RC2) is available.

To get started with SBT, simply add the following to your build.sbt file:

scalaVersion := "2.10.0-RC2"

libraryDependencies += "org.spire-math" % "spire_2.10.0-RC2" % "0.3.0-M4"

Number Types

In addition to supporting all of Scala's built-in number types, Spire introduces several new ones, all of which can be found in spire.math:

  • Rational fractions of integers with perfect precision
  • Complex[A] point on the complex plane
  • Real lazily-computed, arbitrary precision number type
  • SafeLong fast, overflow-proof integer type
  • Interval[A] arithmetic on open, closed, and unbound intervals
  • Number boxed type supporting a traditional numeric tower

Type Classes

Spire provides type classes to support the a wide range of unary and binary operations on numbers. The type classes are specialized, do no boxing, and use implicits to provide convenient infix syntax.

The general-purpose type classes can be found in spire.math and consist of:

  • Numeric[A] all number types, makes "best effort" to support operators
  • Fractional[A] fractional number types, where / is true division
  • Integral[A] integral number types, where / is floor division
  • Eq[A] types that can be compared for equality
  • Order[A] types that can be compared and ordered
  • Trig[A] types that support trigonometric functions

Some of the general-purpose type classes are built in terms of a set of more fundamental type classes defined in spire.algebra. Many of these correspond to concepts from abstract algebra:

  • Semigroup[A] types with an associtive binary operator
  • Monoid[A] semigroups who have an identity element
  • Group[A] monoids that have an inverse operator
  • Rig[A] types that form monoids under + and \*
  • Ring[A] types that form a group under + and a monoid under \*
  • EuclideanRing[A] rings with quotients and remainders (euclidean division)
  • Field[A] euclidean rings with multiplicative inverses
  • Signed[A] types that have a sign (negative, zero, positive)
  • NRoot[A] types that support k-roots, logs, and fractional powers

In addition to the type classes themselves, spire.implicits defines many implicits which provide unary and infix operators for the type classes. The easiest way to use these is via a wildcard import of spire.implicits._.


Using string interpolation and macros, Spire provides convenient syntax for number types. These macros are evaluated at compile-time, and any errors they encounter will occur at compile-time.

For example:

import spire.syntax._

// bytes and shorts
val x = b"100" // without type annotation!
val y = h"999"
val mask = b"255" // unsigned constant converted to signed (-1)

// rationals
val n1 = r"1/3"
val n2 = r"1599/115866" // simplified at compile-time to 13/942

// representations of the number 23
val a = x2"10111" // binary
val b = x8"27" // octal
val c = x16"17" // hex

// SI notation for large numbers
import // .us and .eu also available

val w = i"1 944 234 123" // Int
val x = j"89 234 614 123 234 772" // Long
val y = big"123 234 435 456 567 678 234 123 112 234 345" // BigInt
val z = dec"1 234 456 789.123456789098765" // BigDecimal

Spire also provides a loop macro called cfor whose syntax bears a slight resemblance to a traditional for-loop from C or Java. This macro expands to a tail-recursive function, which will inline literal function arguments.

The macro can be nested in itself and compares favorably with other looping constructs in Scala such as for and while:

import spire.syntax._

// print numbers 1 through 10
cfor(0)(_ < 10, _ + 1) { i =>

// naive sorting algorithm
def selectionSort(ns: Array[Int]) {
  val limit = ns.length -1
  cfor(0)(_ < limit, _ + 1) { i =>
    var k = i
    val n = ns(i)
    cfor(i + 1)(_ <= limit, _ + 1) {
      j => if (ns(j) < ns(k)) k = j
    ns(i) = ns(k)
    ns(k) = n


Since Spire provides a specialized ordering type class, it makes sense that it also provides its own sorting and selection methods. These methods are defined on arrays and occur in-place, mutating the array. Other collections can take advantage of sorting by converting to an array, sorting, and converting back (which is what the Scala collections framework already does in most cases).

Sorting methods can be found in the spire.math.Sorting object. They are:

  • quickSort fastest, nlog(n), not stable with potential n^2 worst-case
  • mergeSort also fast, nlog(n), stable but allocates extra temporary space
  • insertionSort n^2 but stable and fast for small arrays
  • sort alias for quickSort

Both mergeSort and quickSort delegate to insertionSort when dealing with arrays (or slices) below a certain length. So, it would be more accurate to describe them as hybrid sorts.

Selection methods can be found in an analagous spire.math.Selection object. Given an array and an index k these methods put the kth largest element at position k, ensuring that all preceeding elements are less-than or equal-to, and all succeeding elements are greater-than or equal-to, the kth element.

There are two methods defined:

  • quickSelect usually faster, not stable, potentially bad worst-case
  • linearSelect usually slower, but with guaranteed linear complexity
  • select alias for quickSelect


In addition, Spire provides many other methods which are "missing" from java.Math (and scala.math), such as:

  • log(BigDecimal): BigDecimal
  • exp(BigDecimal): BigDecimal
  • pow(BigDecimal): BigDecimal
  • pow(Long): Long
  • gcd(Long, Long): Long


In addition to unit tests, Spire comes with a relatively fleshed-out set of micro-benchmarks written against Caliper. To run the benchmarks from within SBT, change to the benchmark subproject and then run to see a list of benchmarks:

$ sbt
[info] Set current project to spire (in build file:/Users/erik/w/spire/)
> project benchmark
[info] Set current project to benchmark (in build file:/Users/erik/w/spire/)
> run

Multiple main classes detected, select one to run:

 [1] spire.benchmark.AnyValAddBenchmarks
 [2] spire.benchmark.AnyValSubtractBenchmarks
 [3] spire.benchmark.AddBenchmarks
 [4] spire.benchmark.GcdBenchmarks
 [5] spire.benchmark.RationalBenchmarks
 [6] spire.benchmark.JuliaBenchmarks
 [7] spire.benchmark.ComplexAddBenchmarks
 [8] spire.benchmark.CForBenchmarks
 [9] spire.benchmark.SelectionBenchmarks
 [10] spire.benchmark.Mo5Benchmarks
 [11] spire.benchmark.SortingBenchmarks
 [12] spire.benchmark.ScalaVsSpireBenchmarks
 [13] spire.benchmark.MaybeAddBenchmarks

If you plan to contribute to Spire, please make sure to run the relevant benchmarks to be sure that your changes don't impact performance. Benchmarks usually include comparisons against equivalent Scala or Java classes to try to measure relative as well as absolute performance.


Code is offered as-is, with no implied warranty of any kind. Comments, criticisms, and/or praise are welcome, especially from numerical analysts! ;)

Copyright 2011-2012 Erik Osheim, Tom Switzer

The MIT software license is attached in the COPYING file.

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