Parallel Sorting for GPUs

Dehne, Frank; Zaboli, Hamidreza

doi:10.1007/978-3-319-46376-6_12

Cited by 7 publications

(12 citation statements)

References 8 publications

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“…Consider the following contextual signature of an abstract component called Sorting, representing a family of concrete components, each one representing a particular parallel implementation of a well-known sorting algorithm, such as Quicksort, Mergesort, Bitonic Sort, Heapsort, Radix Sort, and so on [49,53,54]:…”

Section: Parallel Sortingmentioning

confidence: 99%

A component-based framework for certification of components in a cloud of HPC services

Dantas

Carvalho

Barbosa

2020

Science of Computer Programming

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HPC Shelf is a proposal of a cloud computing platform to provide component-oriented services for High Performance Computing (HPC) applications. This paper presents a Verification-as-a-Service (VaaS) framework for component certification on HPC Shelf. Certification is aimed at providing higher confidence that components of parallel computing systems of HPC Shelf behave as expected according to one or more requirements expressed in their contracts. To this end, new abstractions are introduced, starting with certifier components. They are designed to inspect other components and verify them for different types of functional, non-functional and behavioral requirements. The certification framework is naturally based on parallel computing techniques to speed up verification tasks.

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Section: Parallel Sortingmentioning

confidence: 99%

A component-based framework for certification of components in a cloud of HPC services

Dantas

Carvalho

Barbosa

2020

Science of Computer Programming

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“…The algorithm of [34] was used by [8] in the context of integer sorting; initial local sorting involved radix-sort. Subsequently [9,10] utilized this approach for GPU sorting of arbitrary (not necessarily integer) keys. Similarly to the approach of [13,15] but differently, a sample of size ps is being used rather than the p(p − 1)s of [13,15].…”

Section: Related Workmentioning

confidence: 99%

“…Similarly to the approach of [13,15] but differently, a sample of size ps is being used rather than the p(p − 1)s of [13,15]. The GPU architecture's block thread size determines p and other GPU constraints dictate s. Thus effectively the implied oversampling factor of [13,15] becomes s/(p − 1) in [9,10]. [30] also performs GPU sorting by using a bitonic sorting method.…”

Section: Related Workmentioning

confidence: 99%

“…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.…”

mentioning

confidence: 99%

“…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.…”

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confidence: 99%

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A Study of Integer Sorting on Multicores

Gerbessiotis

2018

Parallel Process. Lett.

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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...

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