# Difference between revisions of "GHC/Data Parallel Haskell"

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− | The tree is inductively defined, |
+ | The tree is inductively defined; hence, tree traversals will proceed sequentially, level by level. However, the children of each node are held in parallel arrays, and hence, may be traversed in parallel. This structure is, for example, useful in parallel adaptive algorithms based on a hierarchical decomposition, such as the Barnes-Hut algorithm for solving the ''N''-body problem as discussed in more detail in the paper [http://www.cse.unsw.edu.au/~chak/papers/PLKC08.html Harnessing the Multicores: Nested Data Parallelism in Haskell.] |

− | === Some background === |
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+ | For a general introduction to nested data parallelism and its cost model, see Blelloch's [http://www.cs.cmu.edu/~scandal/cacm/cacm2.html Programming Parallel Algorithms.] |
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⚫ | DPH has two major components: (1) the ''vectorisation transformation'' and (2) a ''generic library for flat parallel arrays''. The vectorisation transformation turns nested into flat data-parallelism and is described in detail in the paper [http://www.cse.unsw.edu.au/~chak/papers/PLKC08.html Harnessing the Multicores: Nested Data Parallelism in Haskell.] The generic array library maps flat data-parallelism to GHC's multi-threaded multicore support and is described in the paper [http://www.cse.unsw.edu.au/~chak/papers/CLPKM06.html Data Parallel Haskell: a status report]. The same topics are also covered in the slides for the two talks [http://research.microsoft.com/~simonpj/papers/ndp/NdpSlides.pdf Nested data parallelism in Haskell] and [http://dataparallel.googlegroups.com/web/UNSW%20CGO%20DP%202007.pdf Compiling nested data parallelism by program transformation]. |
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⚫ | DPH has two major components: (1) the ''vectorisation transformation'' and (2) a ''generic library for flat parallel arrays''. The vectorisation transformation turns nested into flat data-parallelism and is described in detail in the paper [http://www.cse.unsw.edu.au/~chak/papers/PLKC08.html Harnessing the Multicores: Nested Data Parallelism in Haskell.] The generic array library maps flat data-parallelism to GHC's multi-threaded multicore support and is described in the paper [http://www.cse.unsw.edu.au/~chak/papers/CLPKM06.html Data Parallel Haskell: a status report]. The same topics are also covered in the slides for the two talks [http://research.microsoft.com/~simonpj/papers/ndp/NdpSlides.pdf Nested data parallelism in Haskell] and [http://dataparallel.googlegroups.com/web/UNSW%20CGO%20DP%202007.pdf Compiling nested data parallelism by program transformation]. |
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− | For further reading, refer to this [[GHC/Data Parallel Haskell/References|collection of background papers, and pointers to other people's work]]. If you are really curious and like to know implementation details and the internals of the Data Parallel Haskell project, it is |
+ | For further reading, refer to this [[GHC/Data Parallel Haskell/References|collection of background papers, and pointers to other people's work]]. If you are really curious and like to know implementation details and the internals of the Data Parallel Haskell project, much of it is described on the GHC developer wiki on the pages covering [http://hackage.haskell.org/trac/ghc/wiki/DataParallel data parallelism] and [http://hackage.haskell.org/trac/ghc/wiki/TypeFunctions type families]. |

## Revision as of 05:48, 2 December 2008

## Contents

## Data Parallel Haskell

*Data Parallel Haskell* is the codename for an extension to the Glasgow Haskell Compiler and its libraries to support nested data parallelism with a focus to utilise multicore CPUs. Nested data parallelism extends the programming model of flat data parallelism, as known from parallel Fortran dialects, to irregular parallel computations (such as divide-and-conquer algorithms) and irregular data structures (such as sparse matrices and tree structures). An introduction to nested data parallelism in Haskell, including some examples, can be found in the paper Nepal – Nested Data-Parallelism in Haskell.

### Project status

A first *technology preview* of Data Parallel Haskell (DPH) is included in the 6.10.1 release of GHC. All major components of DPH are implemented, including code vectorisation and parallel execution on multicore systems. However, the implementation has many limitations and probably also many bugs. Major limitations include the inability to mix vectorised and non-vectorised code in a single Haskell module, the need to use a feature-deprived, special-purpose Prelude in vectorised code, and a lack of optimisations (leading to poor performance).

The purpose of this technology preview is twofold. Firstly, it gives interested early adopters the opportunity to see where the project is headed and enables them to experiment with simple DPH programs. Secondly, we hope to get user feedback that helps us to guide the project and prioritise those features that our users are most interested in.

**Disclaimer:** Data Parallel Haskell is very much **work in progress.** Some components are already usable, and we explain here how to use them. However, please be aware that APIs are still in flux and functionality may change during development.

### Where to get it

Currently, we recommend to use the implementation in GHC 6.10.1. It is available in source and binary form for many architectures. (Please use the version in the HEAD repository of GHC only if you are a GHC developer or a very experienced GHC user and if you know the current status of the DPH code – intermediate versions may well be broken while we implement major changes.)

### Overview

From a user's point of view, Data Parallel Haskell adds a new data type to Haskell –namely, *parallel arrays*– as well as operations on parallel arrays. Syntactically, parallel arrays are like lists, only that instead of square brackets `[`

and `]`

, parallel arrays use square brackets with a colon `[:`

and `:]`

. In particular, `[:e:]`

is the type of parallel arrays with elements of type `e`

; the expression `[:x, y, z:]`

denotes a three element parallel array with elements `x`

, `y`

, and `z`

; and `[:x + 1 | x <- xs:]`

represents a simple array comprehension. More sophisticated array comprehensions (including the equivalent of parallel list comprehensions) as well as enumerations and pattern matching proceed in an analog manner. Moreover, the array library of DPH defines analogs of most list operations from the Haskell prelude and the standard `List`

library (e.g., we have `lengthP`

, `sumP`

, `mapP`

, and so on).

The two main differences between lists and parallel arrays are that (1) parallel arrays are a strict data structure and (2) that they are not inductively defined. Parallel arrays are strict in that by demanding a single element, all elements of an array are demanded. Hence, all elements of a parallel array might be evaluated in parallel. To facilitate such parallel evaluation, operations on parallel arrays should treat arrays as aggregate structures that are manipulated in their entirety (instead of the inductive, element-wise processing that is the foundation of all Haskell list functions.)

As a consequence, parallel arrays are always finite, and standard functions that yield infinite lists, such as `enumFrom`

and `repeat`

, have no corresponding array operation. Moreover, parallel arrays only have an undirected fold function `foldP`

that requires an associative function as an argument – such a fold function has a parallel step complexity of O(log *n*) for arrays of length *n*. Parallel arrays also come with some aggregate operations that absent from the standard list library, such as `permuteP`

.

### An example

```
dotp :: Num a => [:a:] -> [:a:] -> a
dotp xs ys = sumP [:x * y | x <- xs | y <- ys:]
```

This code uses an array variant of parallel list comprehensions, but should otherwise be self-explanatory to any Haskell programmer.

### Designing parallel programs

Data Parallel Haskell is a high-level language to code parallel algorithms. Like plain Haskell, DPH frees the programmer from many low-level operational considerations (such as thread creation, thread synchronisation, critical sections, and deadlock avoidance). Nevertheless, the full responsibility for parallel algorithm design and many performance considerations (such as when does a computation have sufficient parallelism to make it worthwhile to exploit that parallelism) are still with the programmer.

DPH encourages a data-driven style of parallel programming and, in good Haskell tradition, puts the choice of data types first. Specifically, the choice between using lists or parallel arrays for a data structure determines whether operations on the structure will be executed sequentially or in parallel. In addition to suitably combining standard lists and parallel arrays, it is often also useful to embed parallel arrays in a user-defined inductive structure, such as the following definition of parallel rose trees:

```
data RTree a = RNode [:RTree a:]
```

The tree is inductively defined; hence, tree traversals will proceed sequentially, level by level. However, the children of each node are held in parallel arrays, and hence, may be traversed in parallel. This structure is, for example, useful in parallel adaptive algorithms based on a hierarchical decomposition, such as the Barnes-Hut algorithm for solving the *N*-body problem as discussed in more detail in the paper Harnessing the Multicores: Nested Data Parallelism in Haskell.

For a general introduction to nested data parallelism and its cost model, see Blelloch's Programming Parallel Algorithms.

### Further reading and information on the implementation

DPH has two major components: (1) the *vectorisation transformation* and (2) a *generic library for flat parallel arrays*. The vectorisation transformation turns nested into flat data-parallelism and is described in detail in the paper Harnessing the Multicores: Nested Data Parallelism in Haskell. The generic array library maps flat data-parallelism to GHC's multi-threaded multicore support and is described in the paper Data Parallel Haskell: a status report. The same topics are also covered in the slides for the two talks Nested data parallelism in Haskell and Compiling nested data parallelism by program transformation.

For further reading, refer to this collection of background papers, and pointers to other people's work. If you are really curious and like to know implementation details and the internals of the Data Parallel Haskell project, much of it is described on the GHC developer wiki on the pages covering data parallelism and type families.