Automatic generation of a parallel sorting algorithm

Garber, B.A.; Hoeflinger, D.; Li, Xiaoming; Garzarán, María Jesús; Padua, David

doi:10.1109/ipdps.2008.4536400

Cited by 5 publications

(6 citation statements)

References 13 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Algorithms are first generated for small knapsack instances and then compared to algorithms computed on larger test sets. In [19] automatic generation of parallel sorting algorithms is presented by combination of known sorting algorithms to obtain a best performing algorithm for a certain input. The well known art gallery problem (AGP) is discussed by [41], where an iterative algorithm is developed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal deterministic algorithm generation

2018

View full text Add to dashboard Cite

A formulation for the automated generation of algorithms via mathematical programming (optimization) is proposed. The formulation is based on the concept of optimizing within a parameterized family of algorithms, or equivalently a family of functions describing the algorithmic steps. The optimization variables are the parameters-within this family of algorithms-that encode algorithm design: the computational steps of which the selected algorithms consist. The objective function of the optimization problem encodes the merit function of the algorithm, e.g., the computational cost (possibly also including a cost component for memory requirements) of the algorithm execution. The constraints of the optimization problem ensure convergence of the algorithm, i.e., solution of the problem at hand. The formulation is described prototypically for algorithms used in solving nonlinear equations and in performing unconstrained optimization; the parametrized algorithm family considered is that of monomials in function and derivative evaluation (including negative powers). A prototype implementation in GAMS is provided along with illustrative results demonstrating cases for which well-known algorithms are shown to be optimal. The formulation is a 123J Glob Optim mixed-integer nonlinear program. To overcome the multimodality arising from nonconvexity in the optimization problem, a combination of brute force and general-purpose deterministic global algorithms is employed to guarantee the optimality of the algorithm devised. We then discuss several directions towards which this methodology can be extended, their scope and limitations.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Algorithms are first generated for small knapsack instances and then compared to algorithms computed on larger test sets. In [19] work is presented on automatic generation of parallel sorting algorithms. They combine known sorting algorithms to obtain a best performing algorithm for a certain input.…”

Section: Introductionmentioning

confidence: 99%

Optimal deterministic algorithm generation

2018

View full text Add to dashboard Cite

show abstract

“…There have been many proposals for sorting integers on multicore machines including GPUs. These include traditional distribution-specific algorithms such as radix-sort [3,10,22,24], or variants and derivative algorithms of it that use fewer rounds of its baseline count-sort implementation whenever more information about the range of key values is available [6,34]. Other proposals include algorithms that use specialized hardware or software features of a particular multicore architecture [4,6,19,22].…”

Section: Overviewmentioning

confidence: 99%

Integer sorting on multicores: some (experiments and) observations

Gerbessiotis

2017

Preprint

View full text Add to dashboard Cite

There have been many proposals for sorting integers on multicores/GPUs that include radixsort and its variants or other approaches that exploit specialized hardware features of a particular multicore architecture. Comparison-based algorithms have also been used. Network-based algorithms have also been used with primary example Batcher's bitonic sorting algorithm. Although such a latter approach is theoretically "inefficient", if there are few keys to sort, it can lead to better running times as it has low overhead and is simple to implement.In this work we perform an experimental study of integer sorting on multicore processors using not only multithreading but also multiprocessing parallel programming approaches. Our implementations work under Open MPI, MulticoreBSP, and BSPlib. We have implemented serial and parallel radix-sort for various radixes and also some previously little explored or unexplored variants of bitonic-sort and odd-even transposition sort.We offer our observations on a performance evaluation using the MBSP model of such algorithm implementations on multiple platforms and architectures and multiple programming libraries. If we can conclude anything is that modeling their performance by taking into consideration architecture dependent features such as the structure and characteristics of multiple memory hierarchies is difficult and more often than not unsuccessful or unreliable. However we can still draw some very simple conclusions using traditional architecture independent parallel modeling.

show abstract

“…To some degree this is affected by the overhead imposed by the high-level library used in the programming effort. We can still draw however some reliable conclusions and reason about the performance of these implementations using the MBSP model, thus making MBSP useful and usable.Integer sorting on multicores and GPUs can be realized by traditional distribution-specific algorithms such as radix-sort [3,12,25,28], or variants of it that use fewer rounds of the baseline count-sort implementation provided additional information about key values is available [6,39].Other approaches include algorithms that use specialized hardware or software features of a particular multicore architecture [4,6,22,25]. Comparison-based algorithms have also been used with some obvious tweaks: use of deterministic regular sampling sorting [34] that utilizes serial radix-sort for local sorting [8,9,10] or use other methods for local sorting [38,3,5,6,22].…”

mentioning

confidence: 99%

“…Integer sorting on multicores and GPUs can be realized by traditional distribution-specific algorithms such as radix-sort [3,12,25,28], or variants of it that use fewer rounds of the baseline count-sort implementation provided additional information about key values is available [6,39].…”

mentioning

confidence: 99%

A Study of Integer Sorting on Multicores

Gerbessiotis

2018

Parallel Process. Lett.

View full text Add to dashboard Cite

Integer sorting on multicores and GPUs can be realized by a variety of approaches that include variants of distribution-based methods such as radix-sort, comparison-oriented algorithms such as deterministic regular sampling and random sampling parallel sorting, and network-based algorithms such as Batcher's bitonic sorting algorithm.In this work we present an experimental study of integer sorting on multicore processors.We have implemented serial and parallel radix-sort for various radixes, deterministic regular oversampling and random oversampling parallel sorting, and also some previously little explored or unexplored variants of bitonic-sort and odd-even transposition sort.The study uses multithreading and multiprocessing parallel programming libraries with the C language implementations working under Open MPI, MulticoreBSP, and BSPlib utilizing the same source code.A secondary objective is to attempt to model the performance of these algorithm implementations under the MBSP (Multi-memory BSP) model. We first provide some general high-level observations on the performance of these implementations. If we can conclude anything is that accurate prediction of performance by taking into consideration architecture dependent features such as the structure and characteristics of multiple memory hierarchies is difficult and more often than not untenable. To some degree this is affected by the overhead imposed by the high-level library used in the programming effort. We can still draw however some reliable conclusions and reason about the performance of these implementations using the MBSP model, thus making MBSP useful and usable.Integer sorting on multicores and GPUs can be realized by traditional distribution-specific algorithms such as radix-sort [3,12,25,28], or variants of it that use fewer rounds of the baseline count-sort implementation provided additional information about key values is available [6,39].Other approaches include algorithms that use specialized hardware or software features of a particular multicore architecture [4,6,22,25]. Comparison-based algorithms have also been used with some obvious tweaks: use of deterministic regular sampling sorting [34] that utilizes serial radix-sort for local sorting [8,9,10] or use other methods for local sorting [38,3,5,6,22]. Network-based algorithms such as Batcher's [1] bitonic sorting [23,3,30,31,5] have also been utilized. In particualar, bitonic sorting is a low programming overhead algorithm and thus more suitable for GPU and few-core architectures, is simple to implement, and quite fast when few keys are to be sorted, even if its theoretical performance is suboptimal.In this work we perform an experimental study of integer sorting on multicore processors using multithreading and multiprocessing based libraries that facilitate parallel programming. Our implementations need only recompilation of the same C language source to work under Open MPI [29], MulticoreBSP [36], and a multi-processing and out of maintenace library, BSPlib [19].Towards this we have impl...

show abstract

Automatic generation of a parallel sorting algorithm

Cited by 5 publications

References 13 publications

Optimal deterministic algorithm generation

Optimal deterministic algorithm generation

Integer sorting on multicores: some (experiments and) observations

A Study of Integer Sorting on Multicores

Contact Info

Product

Resources

About