Runtime support for multicore Haskell

Marlow, Simon; Jones, Simon Peyton; Singh, Satnam

doi:10.1145/1631687.1596563

Cited by 43 publications

(26 citation statements)

References 25 publications

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“…When run on a multicore, an instance may comprise multiple Haskell Execution Contexts (HECs) which share the same heap, i.e. the standard shared-memory parallel Haskell implementation of GpH, GHC-SMP (Marlow et al, 2009). These instances are connected to form a collaborating parallel system with a distributed heap, as shown in Figure 10.…”

Section: Paean Thread Managementmentioning

confidence: 99%

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PAEAN: Portable and scalable runtime support for parallel Haskell dialects

Berthold

Loidl²,

Hammond

2016

J. Funct. Prog.

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Over time, several competing approaches to parallel Haskell programming have emerged. Different approaches support parallelism at various different scales, ranging from small multicores to massively parallel high-performance computing systems. They also provide varying degrees of control, ranging from completely implicit approaches to ones providing full programmer control. Most current designs assume a shared memory model at the programmer, implementation and hardware levels. This is, however, becoming increasingly divorced from the reality at the hardware level. It also imposes significant unwanted runtime overheads in the form of garbage collection synchronisation etc. What is needed is an easy way to abstract over the implementation and hardware levels, while presenting a simple parallelism model to the programmer.The PAEAN (PArallEl shAred Nothing) runtime system design aims to provide a portable and high-level shared-nothing implementation platform for parallel Haskell dialects. It abstracts over major issues such as work distribution and data serialisation, consolidating existing, successful designs into a single framework. It also provides an optional virtual shared-memory programming abstraction for (possibly) shared-nothing parallel machines, such as modern multicore/manycore architectures or cluster/cloud computing systems. It builds on, unifies, and extends, existing well-developed support for shared-memory parallelism that is provided by the widely-used GHC Haskell compiler. This paper summarises the state-of-the-art in shared-nothing parallel Haskell implementations, introduces the PAEAN abstractions, shows how they can be used to implement three distinct parallel Haskell dialects, and demonstrates that good scalability can be obtained on recent parallel machines. * Corresponding author. Reported work performed while at the University of Copenhagen (DIKU).

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Section: Paean Thread Managementmentioning

confidence: 99%

“…The standard GHC runtime system provides built-in support for parallelism (Marlow et al, 2009), but only on physically shared-memory systems. Drawing on experience with e.g.…”

Section: Built-in Parallelism Support In Ghc: Ghc-smpmentioning

confidence: 99%

PAEAN: Portable and scalable runtime support for parallel Haskell dialects

Berthold

Loidl²,

Hammond

2016

J. Funct. Prog.

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“…GpH extends Haskell with two basic primitives to enable semi-explicit parallelism: par for parallel composition and pseq for sequential composition [51,34,35]. The par primitive "sparks" its first argument, i.e.…”

Section: Gph: Glasgow Parallel Haskellmentioning

confidence: 99%

Parallel Haskell implementations of the N‐body problem

Totoo

Loidl

2013

Concurrency and Computation

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SUMMARYThis paper provides an assessment of high-level parallel programming models for multi-core programming by implementing two versions of the n-body problem. We compare three different parallel programming models based on parallel Haskell, differing in the ways how potential parallelism is identified and managed. We assess the performance of each implementation, discuss the sequential and parallel tuning steps leading to the final versions, and draw general conclusions on the suitability of high-level parallel programming models for multi-core programming. We achieve speed-ups of up to 7.2 for the all-pairs algorithm and up to 6.5 for the Barnes-Hut algorithm on an 8-core machine.

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“…The argument to rpar is called a spark. The runtime collects sparks in a pool and uses this as a source of work to do when there are spare processors available, using a technique called work stealing [7]. Sparks may be evaluated at some point in the future, or they might not -it all depends on whether there is spare processor capacity available.…”

Section: Basic Parallelism: the Eval Monadmentioning

confidence: 99%

Parallel and Concurrent Programming in Haskell

Marlow

2012

Central European Functional Programming School

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Abstract. Haskell provides a rich set of abstractions for parallel and concurrent programming. This tutorial covers the basic concepts involved in writing parallel and concurrent programs in Haskell, and takes a deliberately practical approach: most of the examples are real Haskell programs that you can compile, run, measure, modify and experiment with. We cover parallel programming with the Eval monad, Evaluation Strategies, and the Par monad. On the concurrent side, we cover threads, MVars, asynchronous exceptions, Software Transactional Memory, the Foreign Function Interface, and briefly look at the construction of high-speed network servers in Haskell.

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Runtime support for multicore Haskell

Cited by 43 publications

References 25 publications

PAEAN: Portable and scalable runtime support for parallel Haskell dialects

PAEAN: Portable and scalable runtime support for parallel Haskell dialects

Parallel Haskell implementations of the N‐body problem

Parallel and Concurrent Programming in Haskell

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