Design and initial performance of a high-level unstructured mesh framework on heterogeneous parallel systems

Mudalige, Gihan R.; Giles, Michael B.; Thiyagalingam, Jeyarajan; Reguly, István Z.; Bertolli, Carlo; Kelly, Paul H. J.; Trefethen, Anne

doi:10.1016/j.parco.2013.09.004

Cited by 24 publications

(25 citation statements)

References 31 publications

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“…Race conditions that occur during shared-memory execution on both CPUs (OpenMP) and GPUs (CUDA) are handled through multiple levels of coloring while for the distributed memory (MPI) parallelization, an owner-compute model [12] similar to that used in OPlus is implemented. More details on the various parallelization strategies and their performance implications can be found in previous papers [8], [9], [10], [12], and will be discussed in more detail in the subsequent sections, as we describe optimizations applied to them.…”

Section: Development and Code Generation With Op2mentioning

confidence: 99%

“…The generated code and the OP2 platform specific back-end libraries are highly optimized utilizing the best low-level features of a target architecture to make an OP2 application achieve high performance including high computational efficiency and minimized memory traffic. In previous works, we have presented OP2's design and development [8], [9] and its performance on heterogeneous systems [10], [12] on simpler benchmarks, and demonstrated considerable performance gains on a diverse set of hardware.…”

Section: Introductionmentioning

confidence: 99%

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Acceleration of a Full-Scale Industrial CFD Application with OP2

Reguly

Mudalige

Bertolli

et al. 2016

IEEE Trans. Parallel Distrib. Syst.

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Hydra is a full-scale industrial CFD application used for the design of turbomachinery at Rolls Royce plc. It consists of over 300 parallel loops with a code base exceeding 50K lines and is capable of performing complex simulations over highly detailed unstructured mesh geometries. Unlike simpler structured-mesh applications, which feature high speed-ups when accelerated by modern processor architectures, such as multi-core and many-core processor systems, Hydra presents major challenges in data organization and movement that need to be overcome for continued high performance on emerging platforms. We present research in achieving this goal through the OP2 domain-specific high-level framework. OP2 targets the domain of unstructured mesh problems and follows the design of an active library using source-to-source translation and compilation to generate multiple parallel implementations from a single high-level application source for execution on a range of back-end hardware platforms. We chart the conversion of Hydra from its original hand-tuned production version to one that utilizes OP2, and map out the key difficulties encountered in the process. To our knowledge this research presents the first application of such a high-level framework to a full scale production code. Specifically we show (1) how different parallel implementations can be achieved with an active library framework, even for a highly complicated industrial application such as Hydra, and (2) how different optimizations targeting contrasting parallel architectures can be applied to the whole application, seamlessly, reducing developer effort and increasing code longevity. Performance results demonstrate that not only the same runtime performance as that of the hand-tuned original production code could be achieved, but it can be significantly improved on conventional processor systems. Additionally, we achieve further acceleration by exploiting many-core parallelism, particularly on GPU systems. Our results provide evidence of how high-level frameworks such as OP2 enable portability across a wide range of contrasting platforms and their significant utility in achieving near-optimal performance without the intervention of the application programmer.

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Section: Development and Code Generation With Op2mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Acceleration of a Full-Scale Industrial CFD Application with OP2

Reguly

Mudalige

Bertolli

et al. 2016

IEEE Trans. Parallel Distrib. Syst.

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“…With OP2 we demonstrated that both developer productivity as well as near-optimal performance could be achieved on a wide range of parallel hardware. Research published as a result of this work includes a number of performance analysis studies on standard CFD benchmark applications [23] as well as a full industrial-scale application from the production work-load at Rolls-Royce plc. [28].…”

Section: Opsmentioning

confidence: 99%

“…The build process to obtain a parallel executable as illustrated in Figure 4 follows that of OP2's code generation process [23]. The API calls in the application are parsed by the OPS source-to-source translator which will produce a modified main program and back-end specific code.…”

Section: Porting Cloverleaf To Opsmentioning

confidence: 99%

Performance Analysis of a High-Level Abstractions-Based Hydrocode on Future Computing Systems

Mudalige

Reguly

Giles

et al. 2015

Lecture Notes in Computer Science

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Abstract. In this paper we present research on applying a domain specific high-level abstractions (HLA) development strategy with the aim to "future-proof" a key class of high performance computing (HPC) applications that simulate hydrodynamics computations at AWE plc. We build on an existing high-level abstraction framework, OPS, that is being developed for the solution of multi-block structured mesh-based applications at the University of Oxford. OPS uses an "active library" approach where a single application code written using the OPS API can be transformed into different highly optimized parallel implementations which can then be linked against the appropriate parallel library enabling execution on different back-end hardware platforms. The target application in this work is the CloverLeaf mini-app from Sandia National Laboratory's Mantevo suite of codes that consists of algorithms of interest from hydrodynamics workloads. Specifically, we present (1) the lessons learnt in re-engineering an industrial representative hydro-dynamics application to utilize the OPS high-level framework and subsequent code generation to obtain a range of parallel implementations, and (2) the performance of the auto-generated OPS versions of CloverLeaf compared to that of the performance of the hand-coded original CloverLeaf implementations on a range of platforms. Benchmarked systems include Intel multi-core CPUs and NVIDIA GPUs, the Archer (Cray XC30) CPU cluster and the Titan (Cray XK7) GPU cluster with different parallelizations (OpenMP, OpenACC, CUDA, OpenCL and MPI). Our results show that the development of parallel HPC applications using a high-level framework such as OPS is no more time consuming nor difficult than writing a one-off parallel program targeting only a single parallel implementation. However the OPS strategy pays off with a highly maintainable single application source, through which multiple parallelizations can be realized, without compromising performance portability on a range of parallel systems.

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“…HeDCS is generally used in scientific and commercial applications including real time safety -critical application [2]. They are adopted by many High Performance Computing (HPC) system designers and vendors [3]. Different architectures for heterogeneous computing consist of mainframe with integrated vector unit, vector processors, attached processors, Multiprocessor and Multi computing systems and special purpose architectures [4].Heterogeneity can be proposed in two types, namely, Function-level heterogeneity and Performance -level heterogeneity [5].In function -level heterogeneity, systems combine generalpurpose processors with special-purpose processors, such as vector units, floating -point co -processors, and input/output processors.…”

Section: Introduction 11heterogeneous Systemsmentioning

confidence: 99%

Dynamic Task Scheduling Methods in Heterogeneous Systems: A Survey

Amalarethinam¹,

Josphin²

2015

IJCA

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Heterogeneous systems create unlimited opportunities and challenges in the fields of parallel processing, design of algorithms, and partitioning and mapping of parallel tasks. In mapping of parallel tasks, scheduling plays an important role. The scheduling decisions are based on the dynamic parameters that may change during run time. The goal of scheduling is to utilize all the processors with minimum execution time by proper allocation of tasks to the processors. Task scheduling achieves high performance in the heterogeneous systems. A Parallel application can be represented by a Directed Acyclic Graph (DAG), which represents the dependency among tasks, based on their execution time and communication time. Previous studies reveal that many researchers have designed efficient scheduling algorithms with different parameters on multiprocessor systems. This paper investigates different aspects in scheduling and issues in various levels of the heterogeneous systems.

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Design and initial performance of a high-level unstructured mesh framework on heterogeneous parallel systems

Cited by 24 publications

References 31 publications

Acceleration of a Full-Scale Industrial CFD Application with OP2

Acceleration of a Full-Scale Industrial CFD Application with OP2

Performance Analysis of a High-Level Abstractions-Based Hydrocode on Future Computing Systems

Dynamic Task Scheduling Methods in Heterogeneous Systems: A Survey

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