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Proceedings of the 14th International Conference on Theory and Practice of Electronic Governance

作者： Anastasija Nikiforova Programming Department Innovation Laboratory University of Latvia Faculty of Computing Latvia

ISBN: (纸本)9781450390118

Open Government Data (OGD) are seen as one of the trends that can potentially benefit the economy. However, this fact is closely linked to the “value” of the OGD, i.e. the extent to which the data provided by the OGD portals are interesting, useful and valuable for their reuse, creating value for society and the economy. Here, the concept of “high-value datasets” comes, pointing to data that would create highest value to society and economy. Although an agreement in PSI directive has been reached on a list of six universal categories to be considered as high-value data sets, they are quite generic, i.e. consistent with the current world trends. While it is planned that a list of specific data sets will be defined in 2021, it is also known that this is a country-specific question where the nature of the specific data sets, which are considered to be of high value, depends on country-specific aspects and should therefore be determined by the countries themselves. This study is a first step in this direction by conducting a survey of individual users and small and medium-sized businesses (SME) of Latvia on their view regarding the current state of the data, i.e. in which extent they meet their needs, and what data might be useful for them, and how their availability would affect their willingness to use these data. The paper provides some first insights into the findings on how users assess the current overall value of available OGD, what data sets would give them or their businesses greater value, and how this would affect their willingness to use OGD in the future if these data would become available.

关键词： survey stakeholder respondent high-value data set Open government data

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CloverLeaf on Intel Multi-Core CPUs: A Case Study in Write-Allocate Evasion

CloverLeaf on Intel Multi-Core CPUs: A Case Study in Write-A...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Jan Laukemann Thomas Gruber Georg Hager Dossay Oryspayev Gerhard Wellein Erlangen National High Performance Computing Center Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Programming Models and Compilers Group Computational Science Initiative Brookhaven National Laboratory Upton NY USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

In this paper we analyze the MPI-only version of the CloverLeaf code from the SPEChpc 2021 benchmark suite on recent Intel Xeon "Ice Lake" and "Sapphire Rapids" server CPUs. We observe peculiar breakdowns in performance when the number of processes is prime. Investigating this effect, we create first-principles data traffic models for each of the stencil-like hotspot loops. With application measurements and microbenchmarks to study memory data traffic behavior, we can connect the breakdowns to SpecI2M, a new write-allocate evasion feature in current Intel CPUs. For serial and full-node cases we are able to predict the memory data volume analytically with an error of a few percent. We find that if the number of processes is prime, SpecI2M fails to work properly, which we can attribute to short inner loops emerging from the one-dimensional domain decomposition in this case. We can also rule out other possible causes of the prime number effect, such as breaking layer conditions, MPI communication overhead, and load imbalance.

关键词： Distributed processing Codes Electric breakdown Current measurement Lakes Benchmark testing Ice

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CloverLeaf on Intel Multi-Core CPUs: A Case Study in Write-Allocate Evasion

arXiv

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arXiv 2023年

作者： Laukemann, Jan Gruber, Thomas Hager, Georg Oryspayev, Dossay Wellein, Gerhard Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen National High Performance Computing Center Erlangen Germany Programming Models and Compilers Group Computational Science Initiative Brookhaven National Laboratory UptonNY United States

关键词： Sapphire

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Analysis of variable modification in the Keetch-Byram drought index using artificial neural network

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AIP Conference Proceedings 2023年第1期2508卷

作者： Yudi Sukmono Addy Suyatno Gumelar Ardhi Buana Computing and Programming Laboratory Faculty of Engineering Mulawarman University Samarinda Indonesia

The variables in the original Keetch-Byram drought index calculation method (without modification) consisted of the average annual rainfall, the difference in humidity, and the maximum temperature. In Indonesia, drought is not only caused by reduced rainfall and high maximum temperatures. Several other variables also have the potential to strengthen the accuracy of the drought index calculation, namely wind speed, atmospheric pressure, and duration of solar irradiation. The problem is whether the addition of a new variable will change the accuracy of the calculation? If the new variable has a positive impact, which new variable has a significant effect on the calculation of the drought index. The analysis of the addition of variables in this study uses the method of training and testing artificial neural networks, namely the backpropagation algorithm. The training was carried out on many original variable data, then compared with testing using new variables. The results showed that the addition of a wind speed variable and replacing one of the variables with a wind speed variable showed an increase in prediction accuracy. Prediction accuracy using the three original variables only reached 86.67%, while with the addition of one wind speed variable the accuracy increased to 91.67%. This proves that the wind speed variable is a new candidate variable that has the potential to be one of the variables that play a role in the Keetch-Byram drought index calculation model in the future.

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Scientific Data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments 5

Scientific Data Lake for High Luminosity LHC project and oth...

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5th International Conference on Particle Physics and Astrophysics, ICPPA 2020

作者： Alekseev, A. Kiryanov, A. Klimentov, A. Korchuganova, T. Mitsyn, V. Oleynik, D. Smirnov, A. Smirnov, S. Zarochentsev, A. Compiler Technology Department Ivannikov Institute for System Programming of the Ras Moscow109004 Russia Cloud Technologies and Big Data Analytics Laboratory Plekhanov Russian University of Economics Moscow117997 Russia Atlas Group Universidad Andres Bello Santiago7550196 Chile Department of Computing Resources and Technologies Nrc "kurchatov Institute"-PNPI Gatchina188300 Russia Physics Department Brookhaven National Laboratory UptonNY11973 United States Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg198504 Russia

The next phase of LHC Operations-High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-The-Art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed. © Published under licence by IOP Publishing Ltd.

关键词： Luminance

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Automatic Instrumentation Refinement for Empirical Performance Modeling

Automatic Instrumentation Refinement for Empirical Performan...

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IEEE/ACM International Workshop on programming and Performance Visualization Tools (ProTools)

作者： Jan-Patrick Lehr Alexandru Calotoiu Christian Bischof Felix Wolf Institute for Scientific Computing Technische Universität Darmstadt Darmstadt Germany Laboratory for Parallel Programming Technische Universität Darmstadt Darmstadt Germany

ISBN: (纸本)9781728160276

The analysis of runtime performance is important during the development and throughout the life cycle of HPC applications. One important objective in performance analysis is to identify regions in the code that show significant runtime increase with larger problem sizes or more processes. One approach to identify such regions is to use empirical performance modeling, i.e., building performance models based on measurements. While the modeling itself has already been streamlined and automated, the generation of the required measurements is time consuming and tedious. In this paper, we propose an approach to automatically adjust the instrumentation to reduce overhead and focus the measurements to relevant regions, i.e.,such that show increasing runtime with larger input parameters or increasing number of MPI ranks. Our approach employs Extra-P to generate performance models, which it then uses to extrapolate runtime and, finally, decide which functions should be kept for measurement. Also, the analysis expands the instrumentation, by heuristically adding functions based on static source-code features. We evaluate our approach using benchmarks from SPEC CPU 2006, SU2, and parallel MILC. The evaluation shows that our approach can filter functions of little interest and generate profiles that contain mostly relevant regions. For example, the overhead for SU2 can be improved automatically from 200% to 11% compared to filtered Score-P measurements.

关键词： Instruments Runtime Mathematical model Analytical models Atmospheric measurements Particle measurements Parallel processing

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ClangJIT: Enhancing C++ with just-in-time compilation

arXiv

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arXiv 2019年

作者： Finkel, Hal Poliakoff, David Richards, David F. Lead Compiler Technology and Programming Languages Leadership Computing Facility Argonne National Laboratory LemontIL United States Lawrence Livermore National Laboratory LivermoreCA United States

The C++ programming language is not only a keystone of the high-performance-computing ecosystem but has proven to be a successful base for portable parallel-programming frameworks. As is well known, C++ programmers use templates to specialize algorithms, thus allowing the compiler to generate highly-efficient code for specific parameters, data structures, and so on. This capability has been limited to those specializations that can be identified when the application is compiled, and in many critical cases, compiling all potentially-relevant specializations is not practical. ClangJIT provides a well-integrated C++ language extension allowing template-based specialization to occur during program execution. This capability has been implemented for use in large-scale applications, and we demonstrate that just-in-time-compilation based dynamic specialization can be integrated into applications, often requiring minimal changes (or no changes) to the applications themselves, providing significant performance improvements, programmer-productivity improvements, and decreased compilation time. Copyright © 2019, The Authors. All rights reserved.

关键词： Just in time production

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How File-access Patterns Inuence the Degree of I/O Interference between Cluster Applications

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2019年第2期6卷 29-55页

作者： Shah, Aamer Kuo, Chih-Song Nomura, Akihiro Matsuoka, Satoshi Wolf, Felix Laboratory for Parallel Programming Technische Universita•t Darmstadt Darmstadt Germany Taiwan Semiconductor Manufacturing Company Limited Hsinchu Taiwan Global Scientic Information and Computing Center Tokyo Institute of Technology Tokyo Japan Department of Mathematical and Computing Science Tokyo Institute of Technology Tokyo Japan

On large-scale clusters, tens to hundreds of applications can simultaneously access a parallel le system, leading to contention and, in its wake, to degraded application performance. In this article, we analyze the inuence of le-access patterns on the degree of interference. As it is by experience most intrusive, we focus our attention on write-write contention. We observe considerable dierences among the interference potentials of several typical write patterns. In particular, we found that if one parallel program writes large output les while another one writes small checkpointing les, then the latter is slowed down when the checkpointing les are small enough and the former is vice versa. Moreover, applications with a few processes writing large output les already can signicantly hinder applications with many processes from checkpointing small les. Such eects can seriously impact the runtime of real applications|up to a factor of ve in one instance. Our insights and measurement techniques oer an opportunity to automatically classify the interference potential between applications and to adjust scheduling decisions accordingly. © The Authors 2019. This paper is published with open access at ***

关键词： Benchmarking

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Scientific Data Lake for High Luminosity LHC project and other data-intensive particle and astro-particle physics experiments

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Journal of Physics: Conference Series 2020年第1期1690卷

作者： A Alekseev A Kiryanov A Klimentov T Korchuganova V Mitsyn D Oleynik A Smirnov S Smirnov A Zarochentsev Compiler Technology Department Ivannikov Institute for System Programming of the RAS Moscow 109004 Russia Cloud technologies and Big Data analytics laboratory Plekhanov Russian University of Economics Moscow 117997 Russia ATLAS group Universidad Andres Bello Santiago 7550196 Chile Department of computing resources and technologies NRC 'Kurchatov Institute' - PNPI 188300 Gatchina Russia Physics Department Brookhaven National Laboratory Upton NY 11973 USA Laboratory of Information Technologies Joint Institute for Nuclear Research Dubna 141980 Russia Institute of Nuclear Physics and Engineering National Research Nuclear University MEPhI Moscow 115409 Russia Laboratory of Ultra-High Energy Physics Saint Petersburg State University Saint Petersburg 198504 Russia

The next phase of LHC Operations – High Luminosity LHC (HL-LHC), which is aimed at ten-fold increase in the luminosity of proton-proton collisions at the energy of 14 TeV, is expected to start operation in 2027-2028 and will deliver an unprecedented scientific data volume of multi-exabyte scale. This amount of data has to be stored and the corresponding storage system should ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data processing model will not be able to provide the required infrastructure growth even taking into account the expected hardware technology evolution. To address this challenge the new state-of-the-art computing infrastructure technologies are now being developed and are presented here. The possibilities of application of the HL-LHC distributed data handling technique for other particle and astro-particle physics experiments dealing with large-scale data volumes like DUNE, LSST, Belle-II, JUNO, SKAO etc. are also discussed.

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Scheduling Chapel tasks with Qthreads on manycore: A tale of two schedulers 7

Scheduling Chapel tasks with Qthreads on manycore: A tale of...

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7th International Workshop on Runtime and Operating Systems for Supercomputers, ROSS 2017

作者： Evans, Noah Olivier, Stephen L. Barrett, Richard Stelle, George Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States Sandia National Laboratories AlbuquerqueNM United States Programming Models Team Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9781450350860

This paper describes improvements in task scheduling for the Chapel parallel programming language provided in its default on-node tasking runtime, the Qthreads library. We describe a new scheduler distrib which builds on the approaches of two previous Qthreads schedulers, Sherwood and Nemesis, and combines the best aspects of both-work stealing and load balancing from Sherwood and a lock free queue access from Nemesis- to make task queuing better suited for the use of Chapel in the manycore era. We demonstrate the efficacy of this new scheduler by showing improvements in various individual benchmarks of the Chapel test suite on the Intel Knights Landing architecture. © 2017 ACM.

关键词： Scheduling

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