Hierarchical Computation in the SPMD Programming Model

Kamil, Amir; Yelick, Katherine

doi:10.1007/978-3-319-09967-5_1

Cited by 12 publications

(12 citation statements)

References 13 publications

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“…Recent work in Titanium has replaced the flat SPMD model with the more hierarchical Recursive Single-Program, Multiple-Data (RSPMD) model [40]. This model groups together data and execution contexts into teams that are arranged in hierarchical structures, which match the structure of recursive and compositional algorithms and emerging hierarchical architectures.…”

Section: State Of the Artmentioning

confidence: 99%

Trends in Data Locality Abstractions for HPC Systems

Unat

Dubey

Hoefler

et al. 2017

IEEE Trans. Parallel Distrib. Syst.

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Abstract-The cost of data movement has always been an important concern in high performance computing (HPC) systems. It has now become the dominant factor in terms of both energy consumption and performance. Support for expression of data locality has been explored in the past, but those efforts have had only modest success in being adopted in HPC applications for various reasons. However, with the increasing complexity of the memory hierarchy and higher parallelism in emerging HPC systems, locality management has acquired a new urgency. Developers can no longer limit themselves to low-level solutions and ignore the potential for productivity and performance portability obtained by using locality abstractions. Fortunately, the trend emerging in recent literature on the topic alleviates many of the concerns that got in the way of their adoption by application developers. Data locality abstractions are available in the forms of libraries, data structures, languages and runtime systems; a common theme is increasing productivity without sacrificing performance. This paper examines these trends and identifies commonalities that can combine various locality concepts to develop a comprehensive approach to expressing and managing data locality on future large-scale high-performance computing systems.

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Section: State Of the Artmentioning

confidence: 99%

Trends in Data Locality Abstractions for HPC Systems

Unat

Dubey

Hoefler

et al. 2017

IEEE Trans. Parallel Distrib. Syst.

Self Cite

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“…Figure 2 illustrates this behaviour. Figure 1 shows the same function scan( ) being executed over different data files, characterizing a single program multiple data (SPMD) model [34]. As ClamAV does not use any parallel processing, every call to the function scan( ) of the inspection step is sequentially executed.…”

Section: Designing a Parallel Version Of Clamav With Openmpmentioning

confidence: 99%

Endpoint Security in Networks: An OpenMP Approach for Increasing Malware Detection Speed

et al. 2017

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Increasingly sophisticated antivirus (AV) software and the growing amount and complexity of malware demand more processing power from personal computers, specifically from the central processor unit (CPU). This paper conducted performance tests with Clam AntiVirus (ClamAV) and improved its performance through parallel processing on multiple cores using the Open Multi-Processing (OpenMP) library. All the tests used the same dataset constituted of 1.33 GB of data distributed among 2766 files of different sizes. The new parallel version of ClamAV implemented in our work achieved an execution time around 62% lower than the original software version, reaching a speedup of 2.6 times faster. The main contribution of this work is to propose and implement a new version of the ClamAV antivirus using parallel processing with OpenMP, easily portable to a variety of hardware platforms and operating systems.

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“…The simplest operation to create a new team is team.split(n), which creates n new teams, each with an approximately equal number of units. Teams are used to represent hierarchical configurations that arise in the hardware topology or may come from the algorithmic design of the application [24]. To reflect the machine topology, an equalsized split will generally be inadequate.…”

Section: E Teamsmentioning

confidence: 99%

DASH: A C++ PGAS Library for Distributed Data Structures and Parallel Algorithms

Fuerlinger

Fuchs

Kowalewski

2016

2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference On

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Abstract-We present DASH, a C++ template library that offers distributed data structures and parallel algorithms and implements a compiler-free PGAS (partitioned global address space) approach. DASH offers many productivity and performance features such as global-view data structures, efficient support for the owner-computes model, flexible multidimensional data distribution schemes and inter-operability with STL (standard template library) algorithms. DASH also features a flexible representation of the parallel target machine and allows the exploitation of several hierarchically organized levels of locality through a concept of Teams. We evaluate DASH on a number of benchmark applications and we port a scientific proxy application using the MPI two-sided model to DASH. We find that DASH offers excellent productivity and performance and demonstrate scalability up to 9800 cores.

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Hierarchical Computation in the SPMD Programming Model

Cited by 12 publications

References 13 publications

Trends in Data Locality Abstractions for HPC Systems

Trends in Data Locality Abstractions for HPC Systems

Endpoint Security in Networks: An OpenMP Approach for Increasing Malware Detection Speed

DASH: A C++ PGAS Library for Distributed Data Structures and Parallel Algorithms

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