Architecture independent massive parallelization of divide-and-conquer algorithms

Achatz, Klaus; Schulte, Wolfram

doi:10.1007/3-540-60117-1_7

Cited by 13 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to transform the abstract specifications of the powerlist programs to BSML concrete implementations, we have used the methodology presented in [8] that transform the divide&conquer functions into tail-recursive computations. Then we have adapted these single-control flow computations to BSML by giving an efficient definition of powerlists in OCaml based on GADTs, and by giving efficient predefined implementations for top-down and bottom-up patterns of computations defined in [8].…”

Section: Discussionmentioning

confidence: 99%

“…This leads to a framework formed by high level functions that could be used in the implementation of each specific powerlist program. More details could be found in [8].…”

Section: Powerlistsmentioning

confidence: 98%

“…In [8] Achatz and Schulte present transformation rules to parallelize divide-and-conquer (DC) algorithms over powerlists. Their goal was to derive programs for the massively data parallel model.…”

Section: Powerlistsmentioning

confidence: 99%

“…The idea presented in [8] is to transform the DC scheme expressed as a powerlist function f into a data parallel computation, based on tail-recursive computation.…”

Section: Powerlistsmentioning

confidence: 99%

“…A full framework for the development of parallel programs using powerlists should provide a way for a user to simply express his/her algorithms in a high-level way, and transformations to obtain more efficient versions of the programs. Actually using the powerlist algebraic properties, Achatz et al [8] proposed a method to do so, with the aim of running the optimised versions of powerlists programs on SIMD architectures. Their method transforms programs written using a set of input patterns (or skeletons) into equivalent programs using a set of output patterns.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Implementing Powerlists with Bulk Synchronous Parallel ML

Loulergue¹,

Niculescu²,

Tesson³

2014

2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing

View full text Add to dashboard Cite

Tools and methods able to simplify the development process of parallel software, but also to assure a high level of performance and robustness, are necessary. Powerlists and their variants are data structures that can be successfully used in a simple, provably correct, functional description of parallel programs, which are divide-and-conquer in nature. The paper presents how programs defined based on powerlists could be implemented in the functional language OCaml plus calls to the parallel functional programming library Bulk Synchronous Parallel ML. BSML functions follow the BSP model requirements, and so its advantages are introduced in OCaml parallel code. In order to write powerlist programs in BSML we provide a datatype for powerlists and a set of skeletons (higher-order functions implemented in parallel) to manipulate them. Examples are given and concrete experiments for their executions are conducted. Keywords-Parallel recursive structures; Functional parallel programming; Bulk synchronous parallelism I. CONTEXT AND MOTIVATIONThe latest developments of the computation systems lead to an increase of the requirements in using parallel computation. Still, for many years, parallel computation has been considered difficult and error-prone. This imposes using tools and methodologies able to simplify the development process of parallel software, but also to assure a high level of performance and robustness.This calls for a strongly structured form of parallelism [1], [2], [3], which should not only be based on an abstraction or model that conceals much of the complexity of parallel computation, but also provide a systematic way of developing such parallelism from specifications for practically nontrivial examples. Since correctness is very important in this context, high-level algebraic theories are appropriate to be used as fundamentals. Among them at least two seem suitable for parallel programming: the theory of lists [4] and parallel recursive structures such as powerlists [5].Powerlists and their variants are data structures that can be successfully used in a simple, provably correct, functional description of parallel programs, which are divide and conquer in nature [5], [6]. For each data structure, theories based on algebras and structural induction principles have been specified, which make them well suited to formally define recursive, data-parallel algorithms. These theories can be considered together a base for a model of parallel computation with a very high level of abstraction [7].In order to be useful, a model of parallel computation must also address very carefully issues such as efficient implementation and costs evaluation. BSP model [2] is famous especially because it provides a very accurate cost analysis, and a rigorous development that could provide robustness. From these, the idea of using BSP development methodology in the process of implementing powerlists programs came natural.Powerlist programs are defined in a very high-level way. While their divide-and-conquer nature makes them suita...

show abstract

Section: Discussionmentioning

confidence: 99%

“…This leads to a framework formed by high level functions that could be used in the implementation of each specific powerlist program. More details could be found in [8].…”

Section: Powerlistsmentioning

confidence: 98%

Section: Powerlistsmentioning

confidence: 99%

“…The idea presented in [8] is to transform the DC scheme expressed as a powerlist function f into a data parallel computation, based on tail-recursive computation.…”

Section: Powerlistsmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations