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12th International Workshop on OpenCL and SYCL, IWOCL 2024

作者： Alpay, Aksel Heuveline, Vincent Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg University Germany

ISBN: (纸本)9798400717901

Expressing data parallel programs using C++ standard parallelism is attractive not only due to the simplicity of the model, but also due to its highly idiomatic nature. This programming model, commonly referred to as stdpar, can also be used for accelerator programming by offloading calls to standard algorithms, and is supported by multiple vendors, such as NVIDIA with nvc++, AMD with roc-stdpar, and Intel with the new ICPX -fsycl-pstl-offload flag. We present AdaptiveCpp stdpar, a novel stdpar implementation that is part of the AdaptiveCpp SYCL implementation. AdaptiveCpp stdpar is the very first stdpar implementation to demonstrate performance across GPUs from Intel, NVIDIA and AMD, and allows users to start developing applications using C++ standard algorithms, and then progressively move to SYCL as more control is needed. We find that our solution outperforms all vendor stdpar compilers on HPC GPUs in the majority of tested applications, in some configurations by up to an order of magnitude. Furthermore, we show how AdaptiveCpp outperforms nvc++ in a latency-bound code for all tested problem sizes by up to 80% on NVIDIA A100 due to novel optimizations. Our stdpar implementation deviates from existing implementations by relying on a tighter integration with compiler and runtime, including e.g. dedicated optimization passes to elide synchronization, automatically prefetching required allocations, and an offloading heuristic. © 2024 Owner/Author.

关键词： Optimization

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Efficient Tree-based Parallel Algorithms for N-Body Simulations Using C++ Standard Parallelism

Efficient Tree-based Parallel Algorithms for N-Body Simulati...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Cassell, Thomas Lane Deakin, Tom Alpay, Aksel Heuveline, Vincent Gadeschi, Gonzalo Brito University of Bristol School of Computer Science BristolBS8 1UB United Kingdom Heidelberg University Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg Germany NVIDIA GPU Architecture Munich Germany

ISBN: (纸本)9798350355543

The Barnes-Hut approximation for N-body simulations reduces the time complexity of the naive all-pairs approach from O(N2) to O(N log N) by hierarchically aggregating nearby particles into single entities using a tree data structure. This inherently irregular algorithm poses substantial challenges for performance portable implementations on multi-core CPUs and GPUs. We introduce two portable fully-parallel Barnes-Hut implementation strategies that trade-off different levels of GPU support for performance: an unbalanced concurrent octree, and a balanced bounding volume hierarchy sorted by a Hilbert spacefilling curve. We implement these algorithms in portable ISO C++ using parallel algorithms and concurrency primitives like atomics. The results demonstrate competitive performance on a range of CPUs and GPUs. Additionally, they highlight the effectiveness of the par execution policy for highly concurrent irregular algorithms, outperforming par_unseq on CPUs and GPUs with Independent Thread Scheduling. © 2024 IEEE.

关键词： n/a

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Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems

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Communications in Computational Physics 2023年第2期33卷 596-627页

作者： Sidi Wu Aiqing Zhu Yifa Tang Benzhuo Lu State Key Laboratory of Scientific and Engineering Computing National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China.

With the remarkable empirical success of neural networks across diverse scientific disciplines,rigorous error and convergence analysis are also being developed and ***,there has been little theoretical work focusing on neural networks in solving interface *** this paper,we perform a convergence analysis of physics-informed neural networks(PINNs)for solving second-order elliptic interface ***,we consider PINNs with domain decomposition technologies and introduce gradient-enhanced strategies on the interfaces to deal with boundary and interface jump *** is shown that the neural network sequence obtained by minimizing a Lipschitz regularized loss function converges to the unique solution to the interface problem in H2 as the number of samples *** experiments are provided to demonstrate our theoretical analysis.

关键词： Elliptic interface problems generalization errors convergence analysis neural networks.

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Compiler-aided nd-range parallel-for implementations on CPU in hipSYCL 22

Compiler-aided nd-range parallel-for implementations on CPU ...

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2022 International Workshop on OpenCL, IWOCL 2022

作者： Meyer, Joachim Alpay, Aksel Fröning, Holger Heuveline, Vincent Compiler Design Lab Saarland Informatics Campus Saarland University Germany Computing Systems Group Institute of Computer Engineering Heidelberg University Germany Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg University Germany

ISBN: (纸本)9781450396585

With heterogeneous programming continuously on the rise, performance portability is still to be improved. SYCL provides the nd-range parallel-for paradigm for writing data-parallel kernels. This model allows barriers for group-local synchronization, similar to CUDA and OpenCL kernels. GPUs provide efficient means to model this, but on CPUs the necessary forward-progress guarantees require the use of many (lightweight) threads in library-only SYCL implementations, rendering the nd-range parallel-for unacceptably inefficient. By adopting two compiler-based approaches solving this, the present work improves the performance of the nd-range parallel-for in hipSYCL for CPUs by up to multiple orders of magnitude on various CPU architectures. The two alternatives are compared with regard to their functional correctness and performance. By upstreaming one of the variants, hipSYCL is the first SYCL implementation to provide a well performing nd-range parallel-for on CPU, without requiring an available OpenCL runtime. © 2022 Owner/Author.

关键词： Program compilers

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Preconditioning for a Phase-Field Model with Application to Morphology Evolution in Organic Semiconductors

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Communications in Computational Physics 2023年第6期33卷 1-17页

作者： Kai Bergermann Carsten Deibel Roland Herzog Roderick C.I.MacKenzie Jan-Frederik Pietschmann Martin Stoll Technische Universitat Chemnitz Faculty of Mathematics09107 ChemnitzGermany Technische Universitat Chemnitz Department of Physics09126 ChemnitzGermany Interdisciplinary Center for Scientific Computing Heidelberg University69120 HeidelbergGermany Durham University Department of EngineeringDurhamUK

The Cahn–Hilliard equations are a versatile model for describing the evolution of complex *** this paper we present a computational pipeline for the numerical solution of a ternary phase-field model for describing the nanomorphology of donor–acceptor semiconductor blends used in organic photovoltaic *** model consists of two coupled fourth-order partial differential equations that are discretized using a finite element *** order to solve the resulting large-scale linear systems efficiently,we propose a preconditioning strategy that is based on efficient approximations of the Schur-complement of a saddle point *** show that this approach performs robustly with respect to variations in the discretization ***,we outline that the computed morphologies can be used for the computation of charge generation,recombination,and transport in organic solar cells.

关键词： Preconditioning phase-field models organic solar cells Cahn-Hilliard finite element analysis

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Adaptive energy-preserving algorithms for guiding center system

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Plasma Science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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Efficient Tree-based Parallel Algorithms for N-Body Simulations Using C++ Standard Parallelism 24

Efficient Tree-based Parallel Algorithms for N-Body Simulati...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Thomas Lane Cassell Tom Deakin Aksel Alpay Vincent Heuveline Gonzalo Brito Gadeschi School of Computer Science University of Bristol Bristol UK Engineering Mathematics and Computing Lab Interdisciplinary Center for Scientific Computing Heidelberg University Heidelberg Germany GPU Architecture NVIDIA Munich Germany

ISBN: (纸本)9798350355543

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Efficient Tree-based Parallel Algorithms for N-Body Simulations Using C++ Standard Parallelism

Efficient Tree-based Parallel Algorithms for N-Body Simulati...

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High Performance computing, Networking, Storage and Analysis, SC-W: Workshops of the International Conference for

ISBN: (数字)9798350355543

ISBN: (纸本)9798350355550

The Barnes-Hut approximation for N-body simulations reduces the time complexity of the naive all-pairs approach from O(N 2 ) to O(N log N) by hierarchically aggregating nearby particles into single entities using a tree data structure. This inherently irregular algorithm poses substantial challenges for performance portable implementations on multi-core CPUs and GPUs. We introduce two portable fully-parallel Barnes-Hut implementation strategies that trade-off different levels of GPU support for performance: an unbalanced concurrent octree, and a balanced bounding volume hierarchy sorted by a Hilbert spacefilling curve. We implement these algorithms in portable ISO C++ using parallel algorithms and concurrency primitives like atomics. The results demonstrate competitive performance on a range of CPUs and GPUs. Additionally, they highlight the effectiveness of the par execution policy for highly concurrent irregular algorithms, outperforming par_unseq on CPUs and GPUs with Independent Thread Scheduling.

关键词： Performance evaluation ISO Standards Software algorithms Octrees Graphics processing units C++ languages Scheduling Hardware Parallel algorithms Standards

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Benchmarking and Extending SYCL Hierarchical Parallelism 2

Benchmarking and Extending SYCL Hierarchical Parallelism

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2nd Workshop on Hierarchical Parallelism for Exascale computing, HiPar 2021

作者： Deakin, Tom McIntosh-Smith, Simon Alpay, Aksel Heuveline, Vincent University of Bristol Department of Computer Science Bristol United Kingdom Universität Heidelberg Engineering Mathematics and Computing Lab and Interdisciplinary Center for Scientific Computing Heidelberg Germany

ISBN: (纸本)9781665411325

SYCL is an open-standard, parallel programming model for programming heterogeneous devices from Khronos. It allows single-source programming of diverse attached devices in a cross-platform manner in modern C++. SYCL provides different layers of parallel abstractions, including Same Instruction Multiple Thread (SIMT) kernels, data-parallel loop concurrency and hierarchical parallelism. We discuss Scoped Parallelism as an extension to the existing Hierarchical Parallelism in SYCL, and highlight the advantages and disadvantages of these models from the perspective of the programmer and an implementer of SYCL. In this paper, we compare writing benchmark programs using SIMT kernel, hierarchical parallelism and scoped parallelism paradigms, and present results running on a high-performance CPU and GPU. © 2021 IEEE.

关键词： Benchmarking

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Multi-View Clustering Integrating Anchor Attribute and Structural Information

SSRN

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SSRN 2024年

作者： Li, Xuetong Zhang, Xiao-Dong Funded Key Lab of Scientific and Engineering Computing Shanghai Center for Applied Mathematics Shanghai Jiao Tong University Shanghai200240 China

Constructing the similarity matrix is a pivotal step in multi-view clustering, serving as the foundation for subsequent clustering processes. Most multi-view clustering algorithms focus on computing similarity matrices based solely on attribute information, often overlooking the significance of inter-node interaction structures. This paper introduces a novel multi-view clustering algorithm, abbreviated as AAS. This algorithm not only integrates information across multiple views but also combines the attribute information of nodes with their structural interactions, thereby enabling the similarity matrix to more accurately reflect actual classifications. Furthermore, the complexity of the algorithm is simplified through the utilization of anchor method, and a more flexible and representative method for selecting anchors is proposed. Ultimately, the process of solving the similarity matrix and clustering is integrated into a unified optimization framework. Experiments conducted on datasets generated by the improved Attribute SBM method, and comparative analyses with eight other algorithms, demonstrate the effectiveness and superiority of the proposed method. © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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