Performance portability on EARTH: a case study across several parallel architectures

Zhu, Weirong; Niu, Yanwei; Gao, Guang R.

doi:10.1007/s10586-007-0011-1

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…There are numerous definitions of performance portability, e.g. [34,22,29,28]. In this paper, we adopt the definition from [30]: the performance portability P of an application a that solves a problem p correctly on all platforms in a given set H is given by,…”

Section: Performance Portability Metricsmentioning

confidence: 99%

Achieving near native runtime performance and cross-platform performance portability for random number generation through SYCL interoperability

Pascuzzi,

Goli

2021

Preprint

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High-performance computing (HPC) is a major driver accelerating scientific research and discovery, from quantum simulations to medical therapeutics. The growing number of new HPC systems coming online are being furnished with various hardware components, engineered by competing industry entities, each having their own architectures and platforms to be supported. While the increasing availability of these resources is in many cases pivotal to successful science, even the largest collaborations lack the computational expertise required for maximal exploitation of current hardware capabilities. The need to maintain multiple platform-specific codebases further complicates matters, potentially adding a constraint on the number of machines that can be utilized. Fortunately, numerous programming models are under development that aim to facilitate software solutions for heterogeneous computing. In particular is SYCL, an open standard, C++-based single-source programming paradigm. Among SYCL's features is interoperability, a mechanism through which applications and third-party libraries coordinate sharing data and execute collaboratively. In this paper, we leverage the SYCL programming model to demonstrate cross-platform performance portability across heterogeneous resources. We detail our NVIDIA and AMD random number generator extensions to the oneMKL open-source interfaces library. Performance portability is measured relative to platformspecific baseline applications executed on four major hardware platforms using two different compilers supporting SYCL. The utility of our extensions are exemplified in a real-world setting via a high-energy physics simulation application. We show the performance of implementations that capitalize on SYCL interoperability are at par with native implementations, attesting to the cross-platform performance portability of a SYCL-based approach to scientific codes.

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Section: Performance Portability Metricsmentioning

confidence: 99%

Achieving near native runtime performance and cross-platform performance portability for random number generation through SYCL interoperability

Pascuzzi,

Goli

2021

Preprint

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“…Definition 3 [34] P n P (b → t) = Sn t Sn b × 100% for program P, base system b, target system t and speed-up on n nodes S n . This definition is clearly objective and measurable, but comparison of these measurements is complicated (and may not be meaningful) because the scaling behavior of two applications does not necessarily reflect their absolute performance.…”

Section: Definitionmentioning

confidence: 99%

Implications of a metric for performance portability

Pennycook

Sewall

Lee

2019

Future Generation Computer Systems

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The term "performance portability" has been informally used in computing to refer to a variety of notions which generally include: 1) the ability to run one application across multiple hardware platforms; and 2) achieving some notional level of performance on these platforms. However, there has been a noticeable lack of consensus on the precise meaning of the term, and authors' conclusions regarding their success (or failure) to achieve performance portability have thus been subjective. Comparing one approach to performance portability with another has generally been marked with vague claims and verbose, qualitative explanation of the comparison. This paper presents a concise definition for performance portability, along with a simple metric that accurately captures the performance and portability of an application across different platforms. The utility of this metric is then demonstrated with a retroactive application to previous work.

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“…One similarity in all definitions was the intuition that a performance portable code should be able to run on a variety of machines. There have been many different approaches to solve this, including compiler directives such as OpenACC [24] and OpenMP, languages designed for performance portability such as Chapel [25] and PetaBricks [26], execution models such as EARTH [27], and using embedded domain specific languages such as OPS [6] and OP2 [28]. Template libraries have also been used to add performance portability to an application, examples of which include Kokkos [7] and RAJA [8].…”

Section: P Erformance Portabilitymentioning

confidence: 99%

Achieving Performance Portability for a Heat Conduction Solver Mini-Application on Modern Multi-core Systems

Kirk

Mudalige

Reguly

et al. 2017

2017 IEEE International Conference on Cluster Computing (CLUSTER)

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Abstract-Modernizing production-grade, often legacy applications to take advantage of modern multi-core and many-core architectures can be a difficult and costly undertaking. This is especially true currently, as it is unclear which architectures will dominate future systems. The complexity of these codes can mean that parallelisation for a given architecture requires significant re-engineering. One way to assess the benefit of such an exercise would be to use mini-applications that are representative of the legacy programs.In this paper, we investigate different implementations of TeaLeaf, a mini-application from the Mantevo suite that solves the linear heat conduction equation. TeaLeaf has been ported to use many parallel programming models, including OpenMP, CUDA and MPI among others. It has also been re-engineered to use the OPS embedded DSL and template libraries Kokkos and RAJA. We use these different implementations to assess the performance portability of each technique on modern multi-core systems.While manually parallelising the application targeting and optimizing for each platform gives the best performance, this has the obvious disadvantage that it requires the creation of different versions for each and every platform of interest. Frameworks such as OPS, Kokkos and RAJA can produce executables of the program automatically that achieve comparable portability. Based on a recently developed performance portability metric, our results show that OPS and RAJA achieve an application performance portability score of 71% and 77% respectively for this application.

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Performance portability on EARTH: a case study across several parallel architectures

Cited by 9 publications

References 30 publications

Achieving near native runtime performance and cross-platform performance portability for random number generation through SYCL interoperability

Achieving near native runtime performance and cross-platform performance portability for random number generation through SYCL interoperability

Implications of a metric for performance portability

Achieving Performance Portability for a Heat Conduction Solver Mini-Application on Modern Multi-core Systems

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