SkePU 3: Portable High-Level Programming of Heterogeneous Systems and HPC Clusters

Ernstsson, August; Ahlqvist, Johan; Zouzoula, Stavroula; Keßler, Christoph

doi:10.1007/s10766-021-00704-3

Cited by 18 publications

(25 citation statements)

References 21 publications

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“…SkePU [55,59,62] is a multi-backend skeleton programming framework for heterogeneous parallel systems with a C++11-based interface. A SkePU program defines user functions which act as the operators applied in the skeleton algorithms.…”

Section: Basic Constructsmentioning

confidence: 99%

“…The SkePU container set is recently [59] extended with tensors, which are higher-dimensionality data-containers, completing the picture in of SkePU, and as such the container set is restrained to cover up to four dimensions.…”

Section: Smart Data-containersmentioning

confidence: 99%

“…skepu::Random usage can be combined with most other SkePU features, with a notable exception being dynamic scheduling for multi-core execution introduced [59] in SkePU 3.…”

Section: Prng Streams and Skeleton Invocationsmentioning

confidence: 99%

“…The results in this section were first published in the paper SkePU 3: Portable High-Level Programming of Heterogeneous Systems and HPC Clusters [59].…”

Section: Application Benchmarks Of Skepumentioning

confidence: 99%

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Pattern-based Programming Abstractions for Heterogeneous Parallel Computing

Ernstsson

2022

Linköping Studies in Science and Technology. Dissertations

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Contemporary computer architectures utilize wide multi-core processors, accelerators such as GPUs, and clustering of individual computers into complex large-scale systems. These hardware trends are prevalent across computers of all sizes, from the largest supercomputers down to the smallest mobile phones. While these innovations provide high peak computing performance, software developers find it increasingly difficult to effectively target all the processing resources without expert knowledge in parallelization, heterogeneous computing, communication, synchronization, and so on. To ensure that software can keep up with the development of hardware architectures, advanced high-level programming environments and frameworks are needed to bridge the programmability gap. In addition, as the industry is trending towards increased vertical integration of software development stacks, vendor lock-in presents a risk of coupling software projects to proprietary technologies. Combined with problems of technical debt in large-scale software systems, it is clear that portability and open source are desirable properties of high-level parallel programming environments. One example of a programming framework fulfilling the above criteria is SkePU, a framework for high-level data-parallel pattern programming consisting of a compiler toolchain, programming interface, and run-time system.The work presented in this thesis proposes a design of the pattern-centric skeleton programming model of the SkePU framework based on modern C++ with variadic template metaprogramming and state-of-the-art compiler technology. The design enables further flexibility, expressivity, and portability and gives rise to several new performance optimization techniques. The focus lies on a strong set of programming abstractions: providing new and extended patterns, improving the data access locality of existing ones, and using both static and dynamic knowledge about program flow. The work combines novel programming interfaces and implementations with practical evaluation on synthetic and real-world applications. Several contributions are results from international collaborations in application-framework co-design: a single-source parallelization approach of skeleton programs on heterogeneous clusters, an extension mechanism for inserting platform-optimized code variants in high-level skeleton programs, and an integrated abstraction for portable parallel deterministic random number generation. The work places a strong emphasis on programmability aspects to make heterogeneous parallel computing accessible to non-experts, while also providing sufficient performance and interface familiarity for the high-performance computing community. vi First and foremost I want to thank my supervisor at Linköping University (LiU), Professor Christoph Kessler, for continued and tireless efforts, experienced supervision, and caring friendship. Also thanks to my secondary supervisor José Daniel García Sánchez, professor at University Carlos III of Madrid and member of the ISO C+...

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Section: Basic Constructsmentioning

confidence: 99%

Section: Smart Data-containersmentioning

confidence: 99%

“…skepu::Random usage can be combined with most other SkePU features, with a notable exception being dynamic scheduling for multi-core execution introduced [59] in SkePU 3.…”

Section: Prng Streams and Skeleton Invocationsmentioning

confidence: 99%

“…The results in this section were first published in the paper SkePU 3: Portable High-Level Programming of Heterogeneous Systems and HPC Clusters [59].…”

Section: Application Benchmarks Of Skepumentioning

confidence: 99%

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Pattern-based Programming Abstractions for Heterogeneous Parallel Computing

Ernstsson

2022

Linköping Studies in Science and Technology. Dissertations

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“…SkePU [9] is a pattern-based high-level programming model for transparent program execution on heterogeneous parallel computing systems. A key feature of SkePU is that, in general, the selection of the backend, and thus, the execution platform for a skeleton-based function call need not be determined statically, i.e.…”

Section: Introductionmentioning

confidence: 99%

A Deterministic Portable Parallel Pseudo-Random Number Generator for Pattern-Based Programming of Heterogeneous Parallel Systems

Ernstsson

Vandenbergen

Keller

et al. 2022

Int J Parallel Prog

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SkePU is a pattern-based high-level programming model for transparent program execution on heterogeneous parallel computing systems. A key feature of SkePU is that, in general, the selection of the execution platform for a skeleton-based function call need not be determined statically. On single-node systems, SkePU can select among CPU, multithreaded CPU, single or multi-GPU execution. Many scientific applications use pseudo-random number generators (PRNGs) as part of the computation. In the interest of correctness and debugging, deterministic parallel execution is a desirable property, which however requires a deterministically parallelized pseudo-random number generator. We present the API and implementation of a deterministic, portable parallel PRNG extension to SkePU that is scalable by design and exhibits the same behavior regardless where and with how many resources it is executed. We evaluate it with four probabilistic applications and show that the PRNG enables scalability on both multi-core CPU and GPU resources, and hence supports the universal portability of SkePU code even in the presence of PRNG calls, while source code complexity is reduced.

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Distributed Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

Herrmann

Kuchen

2023

Int J Parallel Prog

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Contemporary HPC hardware typically provides several levels of parallelism, e.g. multiple nodes, each having multiple cores (possibly with vectorization) and accelerators. Efficiently programming such systems usually requires skills in combining several low-level frameworks such as MPI, OpenMP, and CUDA. This overburdens programmers without substantial parallel programming skills. One way to overcome this problem and to abstract from details of parallel programming is to use algorithmic skeletons. In the present paper, we evaluate the multi-node, multi-CPU and multi-GPU implementation of the most essential skeletons Map, Reduce, and Zip. Our main contribution is a discussion of the efficiency of using multiple parallelization levels and the consideration of which fine-tune settings should be offered to the user.

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SkePU 3: Portable High-Level Programming of Heterogeneous Systems and HPC Clusters

Cited by 18 publications

References 21 publications

Pattern-based Programming Abstractions for Heterogeneous Parallel Computing

Pattern-based Programming Abstractions for Heterogeneous Parallel Computing

A Deterministic Portable Parallel Pseudo-Random Number Generator for Pattern-Based Programming of Heterogeneous Parallel Systems

Distributed Calculations with Algorithmic Skeletons for Heterogeneous Computing Environments

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