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Simulating structural plasticity of the brain more scalable than expected

JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2023年 171卷 24-27页

作者： Czappa, Fabian Geiss, Alexander Wolf, Felix Tech Univ Darmstadt Dept Comp Sci Lab Parallel Programming Darmstadt Germany

Structural plasticity of the brain describes the creation of new and the deletion of old synapses over time. Rinke et al. (JPDC 2018) introduced a scalable algorithm that simulates structural plasticity for up to one billion neurons on current hardware using a variant of the Barnes-Hut algorithm. They demonstrate good scalability and prove a runtime complexity of O (n log2 n). In this comment paper, we show that with careful consideration of the algorithm and a rigorous proof, the theoretical runtime can even be classified as O (n log n).(c) 2022 Elsevier Inc. All rights reserved.

关键词： Brain Connectome Simulation Barnes-Hut Large-scale

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Accelerating Brain Simulations with the Fast Multipole Method 28th

Accelerating Brain Simulations with the Fast Multipole Metho...

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28th International Conference on parallel and Distributed Computing (Euro-Par)

作者： Noettgen, Hannah Czappa, Fabian Wolf, Felix Tech Univ Darmstadt Lab Parallel Programming Darmstadt Germany

ISBN: (纸本)9783031125973;9783031125966

The brain is probably the most complex organ in the human body. To understand processes such as learning or healing after brain lesions, we need suitable tools for brain simulations. The Model of Structural Plasticity offers a solution to that problem. It provides a way to model the brain bottom-up by specifying the behavior of the neurons and using structural plasticity to form the synapses. However, its original formulation involves a pairwise evaluation of attraction kernels, which drastically limits scalability. While this complexity has recently been decreased to O(***(2)n) after reformulating the task as a variant of an nbody problem and solving it using an adapted version of the Barnes-Hut approximation, we propose an even faster approximation based on the fast multipole method (FMM). The fast multipole method was initially introduced to solve pairwise interactions in linear time. Our adaptation achieves this time complexity, and it is also faster in practice than the previous approximation.

关键词： Fast Multipole Method Brain Simulation Structural Plasticity Scalability

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COVIDDCGAN: Oversampling Model Using DCGAN Network to Balance a COVID-19 Dataset

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2023年第5期22卷 1533-1549页

作者： Javadi-Moghaddam, Seyyed-Mohammad Gholamalinejad, Hossain Fard, Hamid Mohammadi Bozorgmehr Univ Qaenat Dept Comp Engn Qaen Iran Bozorgmehr Univ Qaenat Dept Comp Sci Qaen Iran Tech Univ Darmstadt TU Darmstadt Parallel Programming Lab Darmstadt Germany

The COVID-19 infection was announced as a pandemic in late 2019. Due to the high speed of the spread, rapid diagnosis can prevent the virus outbreak. Detection of the virus using prominent information from CT scan images is a fast, cheap, and accessible method. However, these image datasets are imbalanced due to the nature of medical data and the lack of coronavirus images. Consequently, the conventional classification algorithms classify this data unsuitably. Oversampling technique is one of the most well-known methods that try to balance the dataset by increasing the minority class of the data. This paper presents a new oversampling model using an improved deep convolutional generative adversarial network (DCGAN) to produce samples that improve classifier performance. In previous DCGAN structures, the feature extraction took place only in the convolution layer, while in the proposed structure, it is done in both the convolution layer and the pooling layer. A Haar transform layer as the pooling layer tries to extract better features. Evaluation results on two hospital datasets express an accuracy of 95.8 and a loss criterion of 0.5354 for the suggested architecture. Moreover, compared to the standard DCGAN structure, the proposed model has superiority in all classification criteria. Therefore, the new model can assist radiologists in validating the initial screening.

关键词： DCGAN structure oversampling model COVID-19 detection CT images detection

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Accelerating Brain Simulations with the Fast Multipole Method

arXiv

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arXiv 2023年

作者： Nöttgen, Hannah Czappa, Fabian Wolf, Felix Laboratory for Parallel Programming Technical University of Darmstadt Darmstadt Germany

The brain is probably the most complex organ in the human body. To understand processes such as learning or healing after brain lesions, we need suitable tools for brain simulations. The Model of Structural Plasticity offers a solution to that problem. It provides a way to model the brain bottom-up by specifying the behavior of the neurons and using structural plasticity to form the synapses. However, its original formulation involves a pairwise evaluation of attraction kernels, which drastically limits scalability. While this complexity has recently been decreased to O(n · log2 n) after reformulating the task as a variant of an n-body problem and solving it using an adapted version of the Barnes-Hut approximation, we propose an even faster approximation based on the fast multipole method (FMM). The fast multipole method was initially introduced to solve pairwise interactions in linear time. Our adaptation achieves this time complexity, and it is also faster in practice than the previous approximation. © 2023, CC BY-NC-ND.

关键词： Scalability

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HDOT - An approach towards productive programming of hybrid applications

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JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2020年第0期137卷 104-118页

作者： Ciesko, Jan Martinez-Ferrer, Pedro J. Penacoba Veigas, Raul Teruel, Xavier Beltran, Vicenc Barcelona Supercomp Ctr Barcelona Spain Barcelona Supercomp Ctr Grp Parallel Programming Models Barcelona Spain Barcelona Supercomp Ctr Parallel & Distributed Programming Models Barcelona Spain

A wealth of important scientific and engineering applications are configured for use on high performance computing architectures using functionality found in the MPI specification. This specification provides application developers with a straightforward means for implementing their ideas for execution on distributed-memory parallel processing computers. OpenMP directives provide a means for operating on shared-memory regions of those computers. With the advent of machines composed of many-core processors, the strict synchronisation required by the bulk synchronous parallel (BSP) communication model can hinder performance increases. This is due to the complexity to handle load imbalances, to reduce serialisation imposed by blocking communication patterns, to overlap communication with computation and, finally, to deal with increasing memory overheads. The MPI specification provides advanced features such as non-blocking calls or shared memory to mitigate some of these factors. However, applying these features efficiently usually requires significant changes on the application structure. Task parallel programming models are being developed as a means of mitigating the abovementioned issues but without requiring extensive changes on the application code. In this work, we present a methodology to develop hybrid applications based on tasks called hierarchical domain overdecomposition with tasking (HDOT). This methodology overcomes most of the issues found on MPI-only and traditional hybrid MPI+OpenMP applications. However, by emphasising the reuse of data partition schemes from process-level and applying them to task-level, it enables a natural coexistence between MPI and shared-memory programming models. The proposed methodology shows promising results in terms of programmability and performance measured on a set of applications. (C) 2019 Elsevier Inc. All rights reserved.

关键词： parallel programming Hybrid programming MPI OpenMP OmpSs-2

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Recognition of Particle Impacts in Acoustic Fixing of Dust Flow Using an Artificial Neural Network

Recognition of Particle Impacts in Acoustic Fixing of Dust F...

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International Conference on Current Problems of Applied Mathematics and Computer Systems, CPAMCS 2023

作者： Malinovskaya, Elena Valuev, Georgii Nazarov, Anton Grobova, Sofya Maksimenkov, Leonid A.M. Obukhov Institute of Atmospheric Physics of Russian Academy of Sciences Moscow Russia North Caucasus Mathematical Research Center North Caucasus Federal University Stavropol Russia Educational and Scientific Center for Computational Mathematics and Parallel Programming On Supercomputers North Caucasus Federal University Stavropol Russia

ISBN: (纸本)9783031640094

The search for methods to capture and record data on the intensity of the saltation flux of large particles (bouncing over the surface) is an important task, since their movement causes the generation of dust aerosol in arid areas. The proposed approach simulates a prototype device capable of tracing the number of particles, in particular in dust storm conditions. The article proposes a method for analyzing audio recordings to detect particle impacts using a neural network approach. The spectrogram of the sound signal is analyzed. The neural network performs the recognition of the intensity and frequency of particle impacts. The accuracy of the neural network on a test sample obtained in natural conditions is 87.27%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Audio recordings

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Simulating Structural Plasticity of the Brain more Scalable than Expected

arXiv

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arXiv 2022年

作者： Czappa, Fabian Geiß, Alexander Wolf, Felix Laboratory for Parallel Programming Department of Computer Science Technical University of Darmstadt Germany

Structural plasticity of the brain describes the creation of new and the deletion of old synapses over time. Rinke et al. (JPDC 2018) introduced a scalable algorithm that simulates structural plasticity for up to one billion neurons on current hardware using a variant of the Barnes–Hut algorithm. They demonstrate good scalability and prove a runtime complexity of O(n log2 n). In this comment paper, we show that with careful consideration of the algorithm and a rigorous proof, the theoretical runtime can even be classified as O(n log n). © 2022, CC BY-NC-ND.

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Decentralised Blockchain-based Solutions for Electronic Healthcare Record with Interacting Social Networking Components

Decentralised Blockchain-based Solutions for Electronic Heal...

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2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Munoz, Daniel-Jesus Constantinescu, Denisa-Andreea ITIS Software CAOSD Group Universidad de Málaga Andalucía Tech Málaga Spain HPACuma Parallel Programming Models and Compilers Group Universidad de Málaga Computer Architecture Department Málaga Spain

ISBN: (纸本)9781665440325

The healthcare industry is a conjunction of monolithic applications based on neutral and time-stamped diagnostics. Hence, it is particularly interested in decentralised technologies to achieve global and social-driven electronic health records. Blockchain is a decentralised networking system where multiple copies of immutable data records are distributed and validated among different independent nodes. While traditionally used to create currently famous digital cryptocurrencies like Bitcoin, its worldwide fever helped to extend its applications beyond the financial industry. However, given how fast completely new technologies evolve, healthcare stakeholders are struggling to find the correct usage of blockchain ecosystem developments. Our goal is to provide a summary of the state-of-the-art efforts in the development of decentralised blockchain-based solutions for electronic healthcare records, in which social networking and wearable technology data is considered in order to provide patient-driven diagnostics - proven to be critical in the current SARS-CoV-2 pandemic. In this paper, we present a comprehensive guide on blockchain technologies competing in the healthcare market, including emerging blockchain-based initiatives and trends. Additionally, we discuss the main caveats of blockchain applications development in this industry based on practical experience. © 2021 IEEE.

关键词： Blockchain

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Automatic Instrumentation Refinement for Empirical Performance Modeling 1

Automatic Instrumentation Refinement for Empirical Performan...

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

作者： Lehr, Jan-Patrick Calotoiu, Alexandru Bischof, Christian Wolf, Felix Tech Univ Darmstadt Inst Comp Sci Darmstadt Germany Tech Univ Darmstadt Lab Parallel Programming Darmstadt Germany

ISBN: (纸本)9781728160269

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： automatic instrumentation performance modeling high-performance computing performance analysis

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Understanding the Scalability of Molecular Simulation Using Empirical Performance Modeling 6th

Understanding the Scalability of Molecular Simulation Using ...

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7th Workshop on Extreme-Scale programming Tools (ESPT)

作者： Shudler, Sergei Vrabec, Jadran Wolf, Felix Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA Tech Univ Berlin Thermodynam & Proc Engn D-10587 Berlin Germany Tech Univ Darmstadt Lab Parallel Programming D-64289 Darmstadt Germany

ISBN: (纸本)9783030178727;9783030178710

Molecular dynamics (MD) simulation allows for the study of static and dynamic properties of molecular ensembles at various molecular scales, from monatomics to macromolecules such as proteins and nucleic acids. It has applications in biology, materials science, biochemistry, and biophysics. Recent developments in simulation techniques spurred the emergence of the computational molecular engineering (CME) field, which focuses specifically on the needs of industrial users in engineering. Within CME, the simulation code ms2 allows users to calculate thermodynamic properties of bulk fluids. It is a parallel code that aims to scale the temporal range of the simulation while keeping the execution time minimal. In this paper, we use empirical performance modeling to study the impact of simulation parameters on the execution time. Our approach is a systematic workflow that can be used as a blue-print in other fields that aim to scale their simulation codes. We show that the generated models can help users better understand how to scale the simulation with minimal increase in execution time.

关键词： Molecular dynamics Performance modeling parallel programming

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