Paradigm | array, functional |
---|---|
Family | ML |
Designed by | Troels Henriksen, Cosmin Oancea, Martin Elsman |
Developer | University of Copenhagen [1] |
First appeared | 2014 |
Typing discipline | inferred, static, strong, Hindley–Milner, uniqueness, dependent |
OS | cross-platform |
License | ISC |
Website | futhark-lang |
Influenced by | |
APL, Haskell, NESL, Standard ML |
Futhark is a multi-paradigm, high-level, functional, data parallel, array programming language. It is a dialect of the language ML, originally developed at UCPH Department of Computer Science (DIKU) as part of the HIPERFIT project. [2] It focuses on enabling data parallel programs written in a functional style to be executed with high performance on massively parallel hardware, especially graphics processing units (GPUs). Futhark is strongly inspired by NESL, and its implementation uses a variant of the flattening transformation, but imposes constraints on how parallelism can be expressed in order to enable more aggressive compiler optimisations. In particular, irregular nested data parallelism is not supported. [3] It is free and open-source software released under an ISC license.
Futhark is a language in the ML family, with an indentation-insensitive syntax derived from OCaml, Standard ML, and Haskell. The type system is based on a Hindley–Milner type system with a variety of extensions, such as uniqueness types and size-dependent types. Futhark is not intended as a general-purpose programming language for writing full applications, but is instead focused on writing compute kernels (not always the same as a GPU kernel) which are then invoked from applications written in conventional languages. [4]
Futhark is named after the first six letters of the Runic alphabet. [5] : 2
The following program computes the dot product of two vectors containing double-precision numbers.
defdotprodxsys=f64.sum(map2(*)xsys))
It can also be equivalently written with explicit type annotations as follows.
defdotprod[n](xs:[n]f64)(ys:[n]f64):f64=f64.sum(map2(*)xsys))
This makes the size-dependent types explicit: this function can only be invoked with two arrays of the same size, and the type checker will reject any program where this cannot be statically determined.
The following program performs matrix multiplication, using the definition of dot product above.
defmatmul[n][m][p](A:[n][m]f64)(B:[m][p]f64):[n][p]f64=map(\A_row->map(\B_col->dotprodA_rowB_col)(transposeB))A
This shows how the types enforce that the function is only invoked with matrices of compatible size. Also, it is an example of nested data parallelism.
In computer science, merge sort is an efficient, general-purpose, and comparison-based sorting algorithm. Most implementations produce a stable sort, which means that the relative order of equal elements is the same in the input and output. Merge sort is a divide-and-conquer algorithm that was invented by John von Neumann in 1945. A detailed description and analysis of bottom-up merge sort appeared in a report by Goldstine and von Neumann as early as 1948.
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This article describes the features in the programming language Haskell.
Haskell is a general-purpose, statically-typed, purely functional programming language with type inference and lazy evaluation. Designed for teaching, research, and industrial applications, Haskell has pioneered several programming language features such as type classes, which enable type-safe operator overloading, and monadic input/output (IO). It is named after logician Haskell Curry. Haskell's main implementation is the Glasgow Haskell Compiler (GHC).
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Developed at DIKU