Quaff: efficient C++ design for parallel skeletons

Falcou, Joel; Sérot, Jocelyn; Chateau, Thierry; Lapresté, Jean-Thierry

doi:10.1016/j.parco.2006.06.001

Cited by 55 publications

(33 citation statements)

References 8 publications

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“…As a rst step we plan to implement search skeletons in CTSL and analyse the overheads. We will exploit low overhead methods like C++ template metaprogramming [10].…”

Section: Future Workmentioning

confidence: 99%

Towards Generic Scalable Parallel Combinatorial Search

Archibald

Maier

Stewart³

et al. 2017

Proceedings of the International Workshop on Parallel Symbolic Computation

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Combinatorial search problems in mathematics, e.g. in nite geometry, are notoriously hard; a state-of-the-art backtracking search algorithm can easily take months to solve a single problem. ere is clearly demand for parallel combinatorial search algorithms scaling to hundreds of cores and beyond. However, backtracking combinatorial searches are challenging to parallelise due to their sensitivity to search order and due to the their irregularly shaped search trees. Moreover, scaling parallel search to hundreds of cores generally requires highly specialist parallel programming expertise.is paper proposes a generic scalable framework for solving hard combinatorial problems. Key elements are distributed memory task parallelism (to achieve scale), work stealing (to cope with irregularity), and generic algorithmic skeletons for combinatorial search (to reduce the parallelism expertise required). We outline two implementations: a mature Haskell Tree Search Library (HTSL) based around algorithmic skeletons and a prototype C++ Tree Search Library (CTSL) that uses hand coded applications.Experiments on maximum clique problems and on a problem in nite geometry, the search for spreads in H(4, 2 2 ), show that (1) CTSL consistently outperforms HTSL on sequential runs, and (2) both libraries scale to 200 cores, e.g. speeding up spreads search by a factor of 81 (HTSL) and 60 (CTSL), respectively. is demonstrates the potential of our generic framework for scaling parallel combinatorial search to large distributed memory platforms.

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“…As a rst step we plan to implement search skeletons in CTSL and analyse the overheads. We will exploit low overhead methods like C++ template metaprogramming [10].…”

Section: Future Workmentioning

confidence: 99%

Towards Generic Scalable Parallel Combinatorial Search

Archibald

Maier

Stewart³

et al. 2017

Proceedings of the International Workshop on Parallel Symbolic Computation

View full text Add to dashboard Cite

show abstract

“…There are only a few algorithmic skeleton libraries which try to address real-time requirements: A Parallel Skeleton Library for Embedded Multicores [34], QUAFF [14] and SkiPPER [53]. The first one is written for C++ and uses templates and inheritance.…”

Section: Algorithmic Skeletonsmentioning

confidence: 99%

A Parallelization Approach for Hard Real-Time Systems and Its Application on Two Industrial Programs

Frieb

Jahr

Ozaktas

et al. 2016

Int J Parallel Prog

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Applications in industry often have grown and improved over many years. Since their performance demands increase, they also need to benefit from the availability of multi-core processors. However, a reimplementation from scratch and even a restructuring of these industrial applications is very expensive, often due to high certification efforts. Therefore, a strategy for a systematic parallelization of legacy code is needed. We present a parallelization approach for hard real-time systems, which ensures a high reusage of legacy code and preserves timing analysability. To show its applicability, we apply it on the core algorithm of an avionics application as well as on the control program of a large construction machine. We create models of the legacy programs showing the potential of parallelism, optimize them and change the source codes accordingly. The parallelized applications are placed on a predictable multi-core processor with up to 18 cores. For evaluation, we compare the worst case execution times and their speedups. Furthermore, we analyse limitations coming up at the parallelization process.

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“…eSkel focuses on nesting of skeletons and provides two nesting modes, transient mode where skeleton objects are destroyed immediately after invocation, and a more lasting persistent mode. Quaff [94] mainly provides farm and pipeline skeletons. It reduces the runtime overhead by performing most of the skeleton instantiations and optimizations at compile-time, using the C++ template meta-programming [2].…”

Section: Skeleton Framework For Multicore Cpu Systems and Mpi-clustersmentioning

confidence: 99%

Performance-aware component composition for GPU-based systems

Dastgeer¹

2014

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This thesis adresses issues associated with efficiently programming modern heterogeneous GPU-based systems, containing multicore CPUs and one or more programmable Graphics Processing Units (GPUs). We use ideas from component-based programming to address programming, performance and portability issues of these heterogeneous systems. Specifically, we present three approaches that all use the idea of having multiple implementations for each computation; performance is achieved/retained either a) by selecting a suitable implementation for each computation on a given platform or b) by dividing the computation work across different implementations running on CPU and GPU devices in parallel.In the first approach, we work on a skeleton programming library (SkePU) that provides high-level abstraction while making intelligent implementation selection decisions underneath either before or during the actual program execution. In the second approach, we develop a composition tool that parses extra information (metadata) from XML files, makes certain decisions offline, and, in the end, generates code for making the final decisions at runtime. The third approach is a framework that uses source-code annotations and program analysis to generate code for the runtime library to make the selection decision at runtime. With a generic performance modeling API alongside program analysis capabilities, it supports online tuning as well as complex program transformations.These approaches differ in terms of genericity, intrusiveness, capabilities and knowledge about the program source-code; however, they all demonstrate usefulness of component programming techniques for programming GPU-based systems. With experimental evaluation, we demonstrate how all three approaches, although different in their own way, provide good performance on different GPU-based systems for a variety of applications.This work has been supported by two EU FP7 projects (PEP-PHER, EXCESS) and by SeRC. Populärvetenskaplig sammanfattningAtt få varje generation av datorer att fungera snabbareär viktigt för samhä-llets utveckling och tillväxt. Traditionellt hade de flesta datorer bara en general-purpose processor (den så kallade CPU:n) som bara kunde exekvera en beräkningsuppgift i taget. Under det senasteårtiondet har dock flerkärniga och mångkärniga processorer blivit vanliga, och datorer har också blivit mer heterogena. Ett modernt datorsystem innehåller vanligtvis flerän en CPU, tillsammans med specialprocessorer såsom grafikprocessorer (GPU:er) som ar anpassade för att kunna exekvera vissa typer av beräkningar effektivarë an CPU:er. Vi kallar ett sådant system med en eller flera GPU:er för ett GPU-baserat system. GPU:er i sådana system har sitt eget separata minne, och för att kunna köra en beräkning på en GPU så behöver man vanligtvis flytta all indata till GPU:ns minne och sedan hämta tillbaka resultatet när beräkningenär klar.Programmeringen av GPU-baserade systemär icke-trivialt av flera anledningar: (1) CPU:er och GPU:er kräver olika programmeringsexper...

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Quaff: efficient C++ design for parallel skeletons

Cited by 55 publications

References 8 publications

Towards Generic Scalable Parallel Combinatorial Search

Towards Generic Scalable Parallel Combinatorial Search

A Parallelization Approach for Hard Real-Time Systems and Its Application on Two Industrial Programs

Performance-aware component composition for GPU-based systems

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