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A Novel Data-Centric Programming Model for Large-Scale parallel Systems 25th

A Novel Data-Centric Programming Model for Large-Scale Paral...

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25th international conference on parallel and distributed Computing (Euro-Par)

作者： Talia, Domenico Trunfio, Paolo Marozzo, Fabrizio Belcastro, Loris Garcia-Blas, Javier del Rio, David Couvee, Philippe Goret, Gael Vincent, Lionel Fernandez-Pena, Alberto Martin de Blas, Daniel Nardi, Mirko Pizzuti, Teresa Spataru, Adrian Justyna, Marek Univ Calabria Arcavacata Di Rende Italy Univ Carlos III Madrid Madrid Spain Atos BDS R&D Data Management Echirolles France Inst Invest Sanitaria Gregorio Maranon Madrid Spain INTEGRIS Rome Italy Inst E Austria Timisoara Timisoara Romania PSNC Poznan Poland

ISBN: (纸本)9783030483401;9783030483395

This paper presents the main features and the programming constructs of the DCEx programming model designed for the implementation of data-centric large-scale parallel applications on Exascale computing platforms. To support scalable parallelism, the DCEx programming model employs private data structures and limits the amount of shared data among parallel threads. The basic idea of DCEx is structuring programs into data-parallel blocks to be managed by a large number of parallel threads. parallel blocks are the units of shared- and distributed-memory parallel computation, communication, and migration in the memory/storage hierarchy. Threads execute close to data using near-data synchronization according to the PGAS model. A use case is also discussed showing the DCEx features for Exascale programming.

关键词： Large-scale parallelism Exascale systems Data-centric applications

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Big data collaborative artificial intelligence and high-performance computing to drive physical design of fusion

Big data collaborative artificial intelligence and high-perf...

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Cloud Computing, Big Data applications and Software Engineering (CBASE), international conference on

作者： Pan Liu Gongjie Liu Jie Liu Chencheng Liu Wenbin Xiong Zhangcun Tang Yinzhao Zhou Yonggang Wang Zhouming Yuan Tianfu Innovation Energy Establishment Chengdu China Beijng PARATERA Technology Corp. Ltd Beijing China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China Central South University of Forestry and Technology Changsha China Nuclear and Radiation Safety Center MEE Beijing China University of Electronic Science and Technology of China Chengdu China

Controlled thermonuclear fusion has always been a dream pursued by mankind. However, the physical processes of controlled thermonuclear fusion are complex, requiring numerical simulations with high performance computing, and the amount of data generated by the physical processes on spatial, temporal and temperature scales is too large to be captured, managed, processed and collated in a reasonable time frame by mainstream software tools to achieve more aggressive fusion physical design. The data are too large to be captured, managed, processed, and collated into more aggressive targets for fusion physical design in a reasonable time by mainstream software tools. At the same time, the failure of fusion ignition can be caused by the distortion of various key physical quantities, and only by decomposing the process step by step and clarifying the changes of key physical quantities in the fusion physics process, can an effective mechanism be formed to prevent the distortion of key physical quantities from causing ignition failure in experimental physics. Big data in collaboration with artificial intelligence and high performance computing to drive the physical design of fusion is a novel avenue. By data acquisition with and pre-processing, this allows the creation of small sample libraries and deep learning. With supervised learning function convergence, incorporating solid/fluid computational methods, further network layering and cell expansion, the parameters of the physical model conforming to Lawson’s criterion will become experimental physical parameters, and we find the new approach in Data capacity, Model combination approach, Type of material, Calculation speed, Optimisation of design iteration times, etc. are superior to the traditional approach.

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distributed methods with compressed communication for solving variational inequalities, with theoretical guarantees 22

Distributed methods with compressed communication for solvin...

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Proceedings of the 36th international conference on Neural Information processing Systems

作者： Aleksandr Beznosikov Peter Richtárik Michael Diskin Max Ryabinin Alexander Gasnikov Research Center for Artificial Intelligence Innopolis University Moscow Institute of Physics and Technology HSE University and Yandex Russia King Abdullah University of Science and Technology Saudi Arabia HSE University and Yandex Russia Yandex and HSE University Russia MIPT HSE University and Institute for Information Transmission Problems RAS Russia

ISBN: (纸本)9781713871088

Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications, including adversarial learning, GANs, transport and robust optimization. With increasing data and problem sizes necessary to train high performing models across various applications, we need to rely on parallel and distributed computing. However, in distributed training, communication among the compute nodes is a key bottleneck during training, and this problem is exacerbated for high dimensional and over-parameterized models. Due to these considerations, it is important to equip existing methods with strategies that would allow to reduce the volume of transmitted information during training while obtaining a model of comparable quality. In this paper, we present the first theoretically grounded distributed methods for solving variational inequalities and saddle point problems using compressed communication: MASHA1 and MASHA2. Our theory and methods allow for the use of both unbiased (such as Randk; MASHA1) and contractive (such as Topk; MASHA2) compressors. New algorithms support bidirectional compressions, and also can be modified for stochastic setting with batches and for federated learning with partial participation of clients. We empirically validated our conclusions using two experimental setups: a standard bilinear min-max problem, and large-scale distributed adversarial training of transformers.

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Developing global gene regulatory network inference method for investigating disease genes using High-Performance Computing resources

Developing global gene regulatory network inference method f...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Younghoon Kim Dept. of Supercomputing Applications Korea Institute of Science and Technology Information (KISTI)

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Inferring gene regulatory networks are important in investigating disease genes and to understand overall perspectives and mechanisms for diseases, global gene regulatory networks are needed. However, inferring global networks require tremendous computations and huge information content, and the inference without these requirements results in infeasible execution in reasonable time and inaccurate networks. To solve this problem, we had developed MONET technique, MOdularized NETwork learning, in which computational burden can be overcome by divide-and-conquer strategy with High Performance Computing(HPC)-resources and insufficiency of information is tackled by integrating the existing biological information. In this study, we have developed MONET2, which extends to Homo sapiens and enables more accurate resulting networks by integrating multiple biological information from genomics, epigenomics, interactome, disease gene data, and so on. We used MONET2 to construct the global networks of Alzheimer’s disease by incorporating more than eleven biological data sources in thirteen brain cell types. MONET2 enables the inference of accurate global networks in a feasible time by using parallel processing techniques based on HPC-resources of KISTI and we plan to open this method to the public like the first version of MONET which was developed as a plugin of Cytoscape.

关键词： Soft sensors High performance computing Genomics parallel processing Brain cells Biology Bioinformatics

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Improving the Runtime Performance of Non-linear Mixed-Effects Model Estimation 25th

Improving the Runtime Performance of Non-linear Mixed-Effect...

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25th international conference on parallel and distributed Computing (Euro-Par)

作者： Haber, Tom van Reeth, Frank Hasselt Univ Expertise Ctr Digital Media Hasselt Belgium

ISBN: (纸本)9783030483401;9783030483395

Non-linear mixed effects models (NLMEM) are frequently used in drug development for pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PK-PD) analyses. Parameter estimation for these models can be time-consuming due to the need for numerical integration. Additionally, the structural model is often expressed using differential equations requiring computationally intensive time-stepping ODE solvers. Overall, this often leads to long computation times in the order of hours or even days. Combining the right mathematical tools as well as techniques from computer science, the computational cost can be significantly reduced. In this paper, several approaches are detailed for improving the performance of parameter estimation for NLMEM. Applying these, often easy, techniques can lead to an order of magnitude speedup.

关键词： Non-linear Mixed effects models High-performance computing parallel

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A Memory Saving Mechanism Based on Data Transferring for Pipeline parallelism

A Memory Saving Mechanism Based on Data Transferring for Pip...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Wei Jiang Rui Xu Sheng Ma Qiong Wang Xiang Hou Hongyi Lu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

The correctness and robustness of the neural network model are usually proportional to its depth and width. Currently, the neural network models become deeper and wider to cope with complex applications, which leads to high memory capacity requirement and computer capacity requirements of the training process. The multi-accelerator parallelism is a promising choice for the two challenges, which deploys multiple accelerators in parallel for training neural networks. Among them, the pipeline parallel scheme has a great advantage in training speed, but its memory capacity requirements are relatively higher than other parallel schemes. Aiming at solving this challenge of pipeline parallel scheme, we propose a data transfer mechanism, which effectively reduces the peak memory usage of the training process by real-time data transferring. In the experiment, we implement our design and apply it to Pipedream, a mature pipeline parallel scheme. The memory requirement of training process is reduced by up to 48.5%, and the speed loss is kept within a reasonable range.

关键词： Training Computational modeling Pipelines Neural networks Memory management Bandwidth parallel processing

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Issues and Algorithm of distributed Shared Memory

Issues and Algorithm of Distributed Shared Memory

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international conference on Innovative Computing (ICIC)

作者： Mian Basam Ahmad Sundas Sagheer University of Management and Technology Lahore Pakistan

ISBN: (纸本)9781665400923

Internet of things is a rapidly growing industry, and there are more connected devices than humans. There are unlimited internet of things devices around us. The scope of these devices has increased manifold in recent years in different areas like healthcare, unmanned vehicles, automatic monitoring systems, and smart homes, etc. These connected devices produce lots of data that need to be processed to achieve the goal of automation in daily life. The cloud provides many centralized solutions to process data using powerful resources in real-time, but there could be latency issues due to increased network traffic. So a distributed solution is much needed to meet this growing demand of processing data in parallel. In this paper, we propose a distributed shared memory [18] abstraction for the internet of things that uses a graph-aware partitioning algorithm and uses smart message passing techniques to process data in distributed and parallel fashion [19].

关键词： Manifolds Message passing distributed databases Telecommunication traffic Smart homes Medical services Real-time systems

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An Algorithmic and Software Pipeline for Very Large Scale Scientific Data Compression with Error Guarantees

An Algorithmic and Software Pipeline for Very Large Scale Sc...

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international conference on High Performance Computing

作者： Tania Banerjee Jong Choi Jaemoon Lee Qian Gong Ruonan Wang Scott Klasky Anand Rangarajan Sanjay Ranka University of Florida USA Oak Ridge National Laboratory USA

Efficient data compression is becoming increasingly critical for storing scientific data because many scientific applications produce vast amounts of data. This paper presents an end-to-end algorithmic and software pipeline for data compression that guarantees both error bounds on primary data (PD) and derived data, known as Quantities of Interest (QoI).We demonstrate the effectiveness of the pipeline by compressing fusion data generated by a large-scale fusion code, XGC, which produces tens of petabytes of data in a single day. We demonstrate that the compression is conducted by setting aside computational resources known as staging nodes, and does not impact the simulation performance. For efficient parallel I/O, the pipeline uses ADIOS2, which many codes such as XGC already use for their parallel I/O. We show that our approach can compress the data by two orders of magnitude while guaranteeing high accuracy on both the PD and the QoIs. Further, the amount of resources required by compression is a few percent of the resources required by simulation while ensuring that the compression time for each stage is less than the corresponding simulation *** pipeline consists of three main steps. The first step decomposes the data using domain decomposition into small subdomains. Each subdomain is then compressed independently to achieve a high level of parallelism. The second step uses existing techniques that guarantee error bounds on the primary data for each subdomain. The third step uses a post-processing optimization technique based on Lagrange multipliers to reduce the QoI errors for data corresponding to each subdomain. The Lagrange multipliers generated can be further quantized or truncated to increase the compression level. All of the above characteristics of our approach make it highly practical to apply on-the-fly compression while guaranteeing errors on QoIs that are critical to the scientists.

关键词： Codes High performance computing Pipelines Software algorithms Data compression Machine learning parallel processing

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Load Balanced Double Star: A High Performance Architecture for Data Intensive applications

Load Balanced Double Star: A High Performance Architecture f...

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2020 international conference on Computer Science, Engineering and applications, ICCSEA 2020

作者： Barik, Himansu Swain, Nirmal Keshari Adhikari, Nibedita Biju Patnaik University of Technology Dept. of Computer Science Engg Rourkela India

ISBN: (纸本)9781728158303

In recent years, the Big Data challenge has attracted increasing attention. To help shield application programmers from the complexity of distribution, many distributed computation frameworks have been proposed in a cloud environment. The parallel interconnection networks play a vital role in big data systems. The Double Star (DS) network is newly suggested reliable interconnection topology for future systems to be employed in high speed parallel computing domain. The proposed system is derived from n-Star, one of the robust systems for advanced networks. In this paper we try to develop an algorithm for load balancing in DS. The Clustered Dimension Exchange Method (CDEM) is at the bottom of the proposed algorithm which balances each processing element of the DS network. © 2020 IEEE.

关键词： Big data

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for Highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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IEEE international conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanlong Qiu Siqi Wang Xi Yang Xinyuan Qiu Chengkun Wu Yingbo Cui Canqun Yang Institute for Quantum Information & State Key Laboratory of High-Performance Computing College of Computer Science National University of Defense Technology Changsha P. R. China National Supercomputer Center in Tianjin Tianjin P. R. China National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha P. R. China Department of Biology and Chemistry College of Science National University of Defense Technology Changsha P. R. China

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

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