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A shared memory parallel multi-mesh fast marching method for re-distancing

ADVANCES IN COMPUTATIONAL MATHEMATICS 2019年第4期45卷 2029-2045页

作者： Diamantopoulos, Georgios Hossinger, Andreas Selberherr, Siegfried Weinbub, Josef TU Wien Christian Doppler Lab High Performance TCAD Inst Microelect Vienna Austria Silvaco Europe Ltd St Ives Cambs England TU Wien Inst Microelect Vienna Austria

A common problem arising in expanding front simulations is to restore the signed distance field property of a discretized domain (i.e., a mesh), by calculating the minimum distance of mesh points to an interface. This problem is referred to as re-distancing and a widely used method for its solution is the fast marching method (FMM). In many cases, a particular high accuracy in specific regions around the interface is required. There, meshes with a finer resolution are defined in the regions of interest, enabling the problem to be solved locally with a higher accuracy. Additionally, this gives rise to coarse-grained parallelization, as such meshes can be re-distanced in parallel. An efficient parallelization approach, however, has to deal with interface-sharing meshes, load-balancing issues, and must offer reasonable parallel efficiency for narrow band and full band re-distancing. We present a parallel multi-mesh FMM to tackle these challenges: Interface-sharing meshes are resolved using a synchronized data exchanges strategy. Parallelization is introduced by applying a pool of tasks concept, implemented using OpenMP tasks. Meshes are processed by OpenMP tasks as soon as threads become available, efficiently balancing out the computational load of unequally sized meshes over the entire computation. Our investigations cover parallel performance of full and narrow band re-distancing. The resulting algorithm shows a good parallel efficiency, if the problem consists of significantly more meshes than the available processor cores.

关键词： Fast marching method shared memory parallelism Eikonal equation Re-distancing

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Towards Scalable Unstructured Mesh Computations on shared memory Many-Cores 24

Towards Scalable Unstructured Mesh Computations on Shared Me...

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29th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP)

作者： Qiu, Haozhong Xu, Chuanfu Fang, Jianbin Deng, Liang Zhang, Jian Dai, Zhe Wang, Qingsong Ding, Yue Chen, Shizhao Che, Yonggang Liu, Jie Natl Univ Def Technol Coll Comp Sci & Technol Changsha Hunan Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Coll Comp Sci & Technol Changsha Hunan Peoples R China China Aerodynam Res & Dev Ctr Mianyang Sichuan Peoples R China

ISBN: (纸本)9798400704352

Due to data conflicts or data dependences, exploiting shared memory parallelism on unstructured mesh applications is highly challenging. The prior approaches are neither general nor scalable on emerging many-core processors. This paper presents a general and scalable shared memory approach for unstructured mesh computations. We recursively divide and reorder an unstructured mesh to construct a task dependency tree (TDT), where massive parallelism is exposed and data conflicts as well as data dependences are respected. We propose two recursion strategies to support popular programming models on both CPUs and GPUs for TDT. We evaluate our approach by applying it to an industrial unstructured Computational Fluid Dynamics (CFD) software. Experimental results show that our approach significantly outperforms the prior shared memory approaches, delivering up to 8.1x performance improvement over the engineer-tuned implementations.

关键词： Unstructured mesh data conflicts data dependences shared memory parallelism task dependency tree

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Flexible control structures for parallelism in OpenMP

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CONCURRENCY-PRACTICE AND EXPERIENCE 2000年第12期12卷 1219-1239页

作者： Shah, S Haab, G Petersen, P Throop, J Kuck & Associates Inc Champaign IL 61820 USA

OpenMP cannot handle some very common programming idioms like recursive control and list or tree data structures. We present the workqueuing model and show it as a natural, flexible, and easy to use extension of OpenMP that is available as a commercial product. A detailed description of workqueuing is presented, together with performance results and pointers to the source code used. Copyright (C) 2000 John Whey & Sons, Ltd.

关键词： shared memory parallelism symmetric multiprocessing OpenMP irregular parallelism multithreading POSIX threads hierarchical decomposition task queue workqueuing

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Parallelization of a distributed ecohydrological model

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ENVIRONMENTAL MODELLING & SOFTWARE 2018年 101卷 51-63页

作者： Liu, Ning Shaikh, Mohsin Ahmed Kala, Jatin Harper, Richard J. Dell, Bernard Liu, Shirong Sun, Ge Murdoch Univ Sch Vet & Life Sci South St Murdoch WA 6150 Australia Chinese Acad Forestry Beijing 10091 Peoples R China Pawsey Supercomp Ctr Perth WA Australia US Forest Serv USDA Southern Res Stn Eastern Forest Environm Threat Assessment Ctr Raleigh NC 27606 USA

WaSSI-C is an ecohydrological model which couples water and carbon cycles with water use efficiency (WUE) derived from global eddy flux observations. However, a significant limitation of the WaSSI-C model is that it only runs serially. High resolution simulations at a large scale are therefore computationally expensive and cause a run-time memory burden. Using distributed (MPI) and shared (OpenMP) memory parallelism techniques, we revised the original model as dWaSSI-C. We showed that using MPI was effective in reducing the computational run-time and memory use. Two experiments were carried out to simulate water and carbon fluxes over the Australian continent to test the sensitivity of the parallelized model to input data-sets of different spatial resolutions, as well as to WUE parameters for different vegetation types. These simulations were completed within minutes using dWaSSI-C, whereas they would not have been possible with the serial version. The dWaSSI-C model was able to simulate the seasonal dynamics of gross ecosystem productivity (GEP) reasonably well when compared to observations at four eddy flux sites. Sensitivity analysis showed that simulated GEP was more sensitive to WUE during the summer compared to winter in Australia, and woody savannas and grasslands showed higher sensitivity than evergreen broadleaf forests and shrublands. Although our results are model-specific, the parallelization approach can be adopted in other similar ecosystem models for large scale applications. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： High performance computing Ecohydrological modeling Distributed memory parallelism shared memory parallelism Water and carbon fluxes

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PASQUAL: Parallel Techniques for Next Generation Genome Sequence Assembly

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2013年第5期24卷 977-986页

作者： Liu, Xing Pande, Pushkar R. Meyerhenke, Henning Bader, David A. Georgia Inst Technol Sch Computat Sci & Engn Coll Comp Atlanta GA 30309 USA Georgia Inst Technol Coll Comp Sch Computat Sci & Engn Newark CA 94560 USA Karlsruhe Inst Technol KIT Inst Theoret Informat D-76128 Karlsruhe Germany Georgia Inst Technol Sch Computat Sci & Engn Coll Comp Atlanta GA 30332 USA

The study of genomes has been revolutionized by sequencing machines that output many short overlapping substrings (called reads). The task of sequence assembly in practice is to reconstruct long contiguous genome subsequences from the reads. With Next Generation Sequencing (NGS) technologies, assembly software needs to be more accurate, faster, and more memory-efficient due to the problem complexity and the size of the data sets. In this paper, we develop parallel algorithms and compressed data structures to address several computational challenges of NGS assembly. We demonstrate how commonly available multicore architectures can be efficiently utilized for sequence assembly. In all stages (indexing input strings, string graph construction and simplification, extraction of contiguous subsequences) of our software PASQUAL, we use shared-memory parallelism to speed up the assembly process. In our experiments with data of up to 6.8 billion base pairs, we demonstrate that PASQUAL generally delivers the best tradeoff between speed, memory consumption, and solution quality. On synthetic and real data sets PASQUAL scales well on our test machine with 40 CPU cores with increasing number of threads. Given enough cores, PASQUAL is fastest in our comparison.

关键词： Parallel algorithms de novo sequence assembly parallel suffix array construction shared memory parallelism high-performance bioinformatics

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Two approximation algorithms for bipartite matching on multicore architectures

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2015年 85卷 62-78页

作者： Dufosse, Fanny Kaya, Kamer Ucar, Bora Nord Europe Inria Lille F-59650 Villeneuve Dascq France Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Univ Lyon INRIA UCBL LIPUMR5668CNRSENS Lyon Lyon France

We propose two heuristics for the bipartite matching problem that are amenable to shared-memory parallelization. The first heuristic is very intriguing from a parallelization perspective. It has no significant algorithmic synchronization overhead and no conflict resolution is needed across threads. We show that this heuristic has an approximation ratio of around 0.632 under some common conditions. The second heuristic is designed to obtain a larger matching by employing the well-known Karp-Sipser heuristic on a judiciously chosen subgraph of the original graph. We show that the Karp-Sipser heuristic always finds a maximum cardinality matching in the chosen subgraph. Although the Karp-Sipser heuristic is hard to parallelize for general graphs, we exploit the structure of the selected subgraphs to propose a specialized implementation which demonstrates very good scalability. We prove that this second heuristic has an approximation guarantee of around 0.866 under the same conditions as in the first algorithm. We discuss parallel implementations of the proposed heuristics on a multicore architecture. Experimental results, for demonstrating speed-ups and verifying the theoretical results in practice, are provided. (C) 2015 Elsevier Inc. All rights reserved.

关键词： shared memory parallelism Matching Bipartite graphs Approximation algorithm

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Reservoir Echo State Network for Classification of Multivariate Time Series 30

Reservoir Echo State Network for Classification of Multivari...

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30th International Conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Purkayastha, Basab Bijoy Barma, Shovan Indian Inst Technol Guwahati Dept Phys Gauhati India Indian Inst Informat Technol Guwahati Dept ECE Gauhati India

ISBN: (纸本)9798350383782;9798350383799

Multivariate time series (MTS) classification has been tackled using various methods, including Reservoir Computing (RC), which generates efficient vectorized representations like reservoir state (RS). RS shines when handling extensive classes or training sets but demands longer processing and substantial memory. Addressing this, in this study we present the Parallel Reservoir Echo State Network (PR-ESN), an optimized parallel training and evaluation algorithm rooted in the ESN principle. It leverages both CPU-shared memory and parallel distributed memory architecture to efficiently capture reservoir state's optimal model space representation, addressing computational challenges in MTS analysis. Distinguishing itself from previous works, PR-ESN combines distributed parallel processing at the network level and shared memory multiprocessing at the node level. This results in reduced memory requirements and faster processing, making it a significant contribution to the field. Key features include PR-ESN's distributed training and evaluation, shared memory parallelization, and MSR concatenation for comprehensive analysis of distributed model space representations. Testing on real-world MTS and benchmark ECG data proves PR-ESN-based classifiers achieve superior accuracy and faster processing times with optimal memory usage. Testing on real-world MTS and benchmark ECG data proves PR-ESN-based classifiers achieve superior accuracy and faster processing times with optimal memory usage.

关键词： shared memory parallelism Distributed memory parallelism Echo State Network Reservoir Computing Parallel Computing Multivariate Time Series Classification

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Frightening Small Children and Disconcerting Grown-ups: Concurrency in the Linux Kernel 18

Frightening Small Children and Disconcerting Grown-ups: Conc...

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23rd International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)

作者： Alglave, Jade Maranget, Luc McKenney, Paul E. Parri, Andrea Stern, Alan UCL London England Microsoft Res Cambridge England Inria Paris Paris France IBM Corp Armonk NY 10504 USA Oregon State Univ Corvallis OR 97331 USA Scuola Super Sant Anna Pisa Italy Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781450349116

Concurrency in the Linux kernel can be a contentious topic. The Linux kernel mailing list features numerous discussions related to consistency models, including those of the more than 30 CPU architectures supported by the kernel and that of the kernel itself. How are Linux programs supposed to behave? Do they behave correctly on exotic hardware? A formal model can help address such questions. Better yet, an executable model allows programmers to experiment with the model to develop their intuition. Thus we offer a model written in the cat language, making it not only formal, but also executable by the herd simulator. We tested our model against hardware and refined it in consultation with maintainers. Finally, we formalised the fundamental law of the Read-Copy-Update synchronisation mechanism, and proved that one of its implementations satisfies this law.

关键词： memory model formal models shared memory parallelism linux

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Advising OpenMP Parallelization via A Graph-Based Approach with Transformers 19th

Advising OpenMP Parallelization via A Graph-Based Approach w...

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19th International Workshop on OpenMP (IWOMP)

作者： Kadosh, Tal Schneider, Nadav Hasabnis, Niranjan Mattson, Timothy Pinter, Yuval Oren, Gal Ben Gurion Univ Negev Dept Comp Sci Beer Sheva Israel Israel Atom Energy Commiss Tel Aviv Israel Ben Gurion Univ Negev Elect & Comp Engn Dept Beer Sheva Israel Intel Labs Hillsboro OR USA Nucl Res Ctr Negev Ctr Comp Sci Negev Israel Technion Israel Inst Technol Dept Comp Sci Haifa Israel

ISBN: (纸本)9783031407437;9783031407444

There is an ever-present need for shared memory parallelization schemes to exploit the full potential of multi-core architectures. The most common parallelization API addressing this need today is OpenMP. Nevertheless, writing parallel code manually is complex and effort-intensive. Thus, many deterministic source-to-source (S2S) compilers have emerged, intending to automate the process of translating serial to parallel code. However, recent studies have shown that these compilers are impractical in many scenarios. In this work, we combine the latest advancements in the field of AI and natural language processing (NLP) with the vast amount of open-source code to address the problem of automatic parallelization. Specifically, we propose a novel approach, called OMPify, to detect and predict the OpenMP pragmas and shared-memory attributes in parallel code, given its serial version. OMPify is based on a Transformer-based model that leverages a graph-based representation of source code that exploits the inherent structure of code. We evaluated our tool by predicting the parallelization pragmas and attributes of a large corpus of (over 54,000) snippets of serial code written in C and C++ languages (Open-OMP-Plus). Our results demonstrate that OMPify outperforms existing approaches - the general-purposed and popular ChatGPT and targeted PragFormer models in terms of F1 score and accuracy. Specifically, OMPify achieves up to 90% accuracy on commonly-used OpenMP benchmark tests such as NAS, SPEC, and PolyBench. Additionally, we performed an ablation study to assess the impact of different model components and present interesting insights derived from the study. Lastly, we also explored the potential of using data augmentation and curriculum learning techniques to improve the model's robustness and generalization capabilities. The dataset and source code necessary for reproducing our results are available at https://***/Scientific-Computing- Lab- NRCN/OMPify.

关键词： NLP Code Completion OpenMP shared memory parallelism Transformers S2S Compilers Code Representations

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Parallel Inference of Phylogenetic Stands with Gentrius

Parallel Inference of Phylogenetic Stands with Gentrius

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37th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Togkousidis, Anastasis Chernomor, Olga Stamatakis, Alexandros Heidelberg Inst Theoret Studies Computat Mol Evolut Grp Heidelberg Germany Karlsruhe Inst Technol Inst Theoret Informat Karlsruhe Germany Univ Vienna Ctr Integrat Bioinformat Vienna CIBIV Max Perutz Labs Vienna Austria Med Univ Vienna Vienna Bio Ctr VBC Vienna Austria Fdn Res & Technol Hellas Inst Comp Sci Biodivers Comp Grp Iraklion Greece

ISBN: (纸本)9798350311990

Multi-locus datasets are frequently used to infer phylogenies instead of using single locus. Missing data constitute a common challenge in such datasets as they can lead to stands, that is, sets of trees that are compatible with the incomplete per-locus trees. Under many common criteria the trees from one stand have identical score. Hence, identifying stands and determining their sizes is of crucial importance for a robust phylogenetic analysis. Recently, Chernomor et al. published Gentrius, a branch-and-bound algorithm that enumerates all stand trees given a set of unrooted incomplete locus trees. Despite its efficiency, the pattern and proportion of missing data in multi-locus datasets can still induce extremely long execution times. Here, we introduce the parallel version of the Gentrius algorithm. Our parallelization deploys a thread-pooling mechanism that maintains threads that finish early in busy-wait mode, such that they can contribute to solving long-running tasks. Thereby, we substantially reduce load imbalance and attain high parallel efficiency. Our performance assessment up to 16 cores yields linear parallel speedups on both, simulated, and empirical data. The parallel version of Gentrius is available as open source code under GNU GPL at https://***/togkousa/iqtree2/tree/terragen. All data we used for our analyses, are available for download at https://***/exelixis/material/***.

关键词： phylogenetic stands phylogenetic terraces multi-locus data parallel computing shared memory parallelism

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