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2016 International Symposium on Grids and Clouds, ISGC 2016

作者： Laveti, Ramesh Naidu Center for Development of Advanced Computing C-DAC Knowledge Park India

Compute and data intensive scientific applications demand compilers to allocate more temporaries on the stack. For example, the change resolution component of a global spectral model changes the resolution of the input files using Nearest Neighbour Interpolation which requires large temporaries on the stack. Temporaries include sub-arrays, automatic arrays and, sub-sections corresponding to actual arguments of a subroutine. If the infrastructure cannot provide adequate stack space at runtime relative to the total size of the temporaries, then the application program runs out of stack and aborts. Allocating the heap memory to store the large temporaries introduced around 25% additional runtime because of allocation and deallocation of the memory. This is observed in various components of a global spectral model. We propose an automatic dynamic stack management framework which uses application profile information and the information related to the required stack memory. It does not mandate any hardware configuration changes. This technique manages stack frames on RAM by the compiler-inserted code into the application binary. Our experiments with a global spectral model show that we are able to obtain an average runtime savings of 21% along with a compile time overhead of 4%. The actual gain depends on the size of the temporaries in an application and the size of the RAM. Currently, it supports sequential and OpenMP applications. We further enhance our framework to deal with the complex MPI and GPU programming paradigms. © Copyright owned by the author(s) under the terms of the Creative Commons

关键词： Random access storage

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3D Anatomical Structure-Guided Deep Learning for Accurate Diffusion Microstructure Imaging

3D Anatomical Structure-Guided Deep Learning for Accurate Di...

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IEEE International Symposium on Biomedical Imaging

作者： Xinrui Ma Jian Cheng Wenxin Fan Ruoyou Wu Yongquan Ye Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou Zhejiang China Pengcheng Laboratory Shenzhen Guangdong China United Imaging Healthcare Houston TX USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation; however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients.

关键词： Deep learning PSNR Accuracy Three-dimensional displays Magnetic resonance imaging Estimation Reconstruction algorithms Diffusion models Feature extraction Microstructure

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Conceptualizing a desktop environment for cognitively challenged people 12

Conceptualizing a desktop environment for cognitively challe...

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International Information Technology Conference, CUBE 2012

作者： Kumar, Shiv Nath Baijal, Sagun Chourey, Leena Ramamurthy, Aparna Sasikumar, M. Center for Development of Advanced Computing Rain Tree Marg CBD-Belapur Navi Mumbai-400614 India Center for Development of Advanced Computing Anusandhan Bhawan C-56/1 Sector 62 Noida-201307 India Center for Development of Advanced Computing Pune University Campus Pune-411007 India

ISBN: (纸本)9781450311854

In India as well as in different parts of the world, a large section of society suffers from different kinds of disabilities. These difficulties impose certain constraints on people suffering with them in following their routine life as compared to normal persons. Of these difficulties, cognitive disability is one of the most difficult to address. This disability relates to different kinds of problems with brain like amnesia, learning disability, attention problems etc. The type and nature of problem may vary from person to person. Common symptoms like slow grasping, longer response time and distraction in nature have been identified by observing the affected users at various rehabilitation centers. Using ICT based solutions, life can be made better for such persons. Computers can assist them in different ways in rehabilitation and enhancing their quality of life. The aim of this paper is to highlight the issues and challenges faced by cognitively impaired persons in using computers and to propose solution(s) so that computer usage will be easy for them. An accessible desktop environment with relevant features is conceptualized that will help user in smooth interaction with computer without distraction. Specific features of the proposed desktop are focused primarily on memory and attention based issues and provides support for affected users, parents/caretakers and teachers. The proposed solution is validated with and impacts of design have been collected from the relevant stack holders such as end users, caretakers and *** enhancements are done on open source operating system environment and are available as open source. This paper discusses the system and the rationale for the various features incorporated. Copyright 2012 ACM.

关键词： Human computer interaction

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3D ANATOMICAL STRUCTURE-GUIDED DEEP LEARNING FOR ACCURATE DIFFUSION MICROSTRUCTURE IMAGING

arXiv

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arXiv 2025年

作者： Ma, Xinrui Cheng, Jian Fan, Wenxin Wu, Ruoyou Ye, Yongquan Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Pengcheng Laboratory Guangdong Shenzhen China United Imaging Healthcare HoustonTX United States

Diffusion magnetic resonance imaging (dMRI) is a crucial noninvasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation;however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients. © 2025, CC BY-NC-ND.

关键词： Tissue

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Critical Infrastructure Asset Discovery and Monitoring for Cyber Security 6th

Critical Infrastructure Asset Discovery and Monitoring for C...

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6th International Conference and Exhibition on Smart Grids and Smart Cities, ISUW 2020

作者： Hareesh, Reddi Senthil Kumar, R.K. Kalluri, Rajesh Bindhumadhava, B.S. Real Time Systems & Internet of Things Center for Development of Advanced Computing Bengaluru India

ISBN: (纸本)9789811690075

With ever growing cyber threats on critical infrastructures, need of deploying the security measures to protect these should be of utmost priority. However, without knowing about the assets in the network and what needs to be secured, all the threat modeling activities, strategies or mitigation/remediation activities will be incomplete or ineffective. Hence the critical infrastructure asset discovery and monitoring is a vital part for providing security. But unlike IT networks where asset discovery is automated, critical infrastructures often perform asset discovery and management manually which leads to incomplete and inefficient asset tracking and management over time and makes it ineffective in quick identification and response to potential threats. An automated approach for asset discovery and management is significant to secure critical infrastructures from cyber security incidents and other threats. This paper proposes an automation methodology for asset discovery, managing and monitoring. The solution is compliant to several NERC-CIP requirements and NCIIPC guidelines. It involves identification and real time monitoring of the critical assets connected to the network, baseline network behavior, maintain repository of asset details, compliance check adhering to policies and generates real time network map for visualization in the Purdue model for the better management of the assets. And it also generates incidents compatible with third party SOC/ SIEMs. The solution helps to reduce the risk of cyber security incidents in critical infrastructures. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Critical infrastructures

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Vibration and displacement analysis of carbon nanotube based cantilever for NEMS applications 2

Vibration and displacement analysis of carbon nanotube based...

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2nd International Conference on Communication and Electronics Systems, ICCES 2017

作者： Verma, Manu Singh, Balwinder Chabbra, Varun Devi, Rekha Academic and Consultancy Services and Division Center of Development of Advanced Computing Mohali Punjab India

ISBN: (纸本)9781509050130

The cantilever is a wide-ranging structure used in nano electromechanical (NEMS) applications including sensors, actuators or in probes for the detection of displacement, mass, and charge. High sensitivity, selectivity, wide dynamic range and fast response time are required for applications in real time monitoring. Carbon nanotubes provide exceptional electrical and mechanical properties like high tensile strength, large surface area, high elasticity, flexibility, high electrical and thermal conductivity which give rise to new sensing concepts. This paper proposes the design and analysis of cantilever made up of carbon nanotube using finite element method to analyze the displacement of cantilever gets affected by varying load. The effect of thickness change on the resonant frequency of the cantilever for CNT material is also analyzed. It is found that that the simulation values of displacement for carbon nanotube are 1.11E-5 at 10 N/m2 and 5.56E-5 at 50 N/m2 The simulation values of resonant frequency for CNT material are 4.42E7 at the thickness of 1nm and 6.73E8 at 15nm. © 2017 IEEE.

关键词： Carbon nanotubes

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Parallelization of reverse time migration using MPI + OpenMP

Parallelization of reverse time migration using MPI + OpenMP

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2016 International Conference on advanced Communication Control and computing Technologies, ICACCCT 2016

作者： Akanksha, S. Kansara Kumar, Gardas Naresh Gujarat Technological University Gujarat India Center of Development for Advanced Computing Pune India

ISBN: (纸本)9781467395458

Increasing computational power ensures effective implementation of high-end HPC applications but does not guarantee performance. Seismic modelling is one such high computational demanding application. In oil and gas industries, reverse time migration is one of the important migration algorithms that guarantees accurate imaging results but its practical implementation gets hindered due to its high computational cost. In this paper we will discuss reverse time migration for solving 2D wave equations using finite difference method and its parallel implementation using Message Passing Interface(MPI) and OpenMP. © 2016 IEEE.

关键词： Message passing

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Seasonal ensemble forecasting application on dependable sumegha scientific cloud infrastructure

Seasonal ensemble forecasting application on dependable sume...

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2016 International Symposium on Grids and Clouds, ISGC 2016

作者： Laveti, Ramesh Naidu Prahladarao, B.B. Arackle, Vineeth Simon Arunachalam, B. Center for Development of Advanced Computing C-DAC Knowledge Park Byappanahalli Bangalore India

Despite several advances in understanding the behaviour of monsoon variability, innovations in the numerical modeling and the availability of higher computational capabilities, accurate prediction of Indian summer monsoon still remains a serious challenge. Seasonal Forecast Model (SFM), developed for seasonal forecast and climate research, is used for forecasting the Indian summer monsoon in advance of a season. Ensemble forecasting method helps us in finding and minimizing the uncertainty inherent in seasonal forecast. The inherent parallel nature and the bursty computational demands of the ensemble forecasting method allows it to effectively utilize the Infrastructure-as-a-Service (IaaS) model on the cloud platform. However, realizing huge scientific experiments is still a challenge to the cloud service providers as well as to the climate modeling community. To start with prototype experiments using SFM model were conducted at T-62 resolution (~ 200 km x 200 km grid). The experience gained from the prototype runs were used by the SuMegha operational team to fine tune the configuration of SuMegha Cloud resources to improve the quality of service. High resolution SFM at T-320 (~ 37 km x 37 km grid) was also configured and experiments were conducted to understand the scalability, computational performance of the application and the reliability of SuMegha Cloud. In this paper, we use SFM as a case study to present the key problems found by climate applications, and propose a framework to run the same on SuMegha Cloud infrastructure to allow a climate model to take advantage of these cloud resources in a seamless and reliable way. The framework uses classification and outlier detection techniques to classify the resources and also to identify the faulty resources. It addresses the challenges such as unexpected hardware failures, power outages, failed porting and software bugs. We share our experience in conducting the ensemble forecasting experiments on SuMegha Cloud u

关键词： Climate models

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PARALLEL computing AT CRS4

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C-PHYSICS AND COMPUTERS 1993年第6期4卷 1315-1321页

作者： SCHEININE, AL Parallel Computing Group Center for Advanced Studies Research and Development in Sardinia via Nazario Sauro 10 I-09123 Cagliari Italy

An overview is given of parallel computing work being done at CRS4 (Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna). Parallel computation projects include: parallelization of a simulation of the interaction of high energy particles with matter (GEANT), domain decomposition for numerical solution of partial differential equations, seismic migration for oil prospecting, finite-element structural analysis, parallel molecular dynamics, a C++ library for distributed processing of specific functions, and real-time visualization of a computer simulation that runs as distributed processes.

关键词： DOMAIN DECOMPOSITION GEANT SEISMIC MIGRATION PARALLEL MOLECULAR DYNAMICS

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Loop partitioning unimodular transformations for distributed memory multiprocessors 5

Loop partitioning unimodular transformations for distributed...

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5th International Parallel Processing Symposium, IPPS 1991

作者： Kulkarni, D. Kumar, K.G. Basu, A. Paulraj, A. Center for Development of Advanced Computing 2/1 Brunton Road Bangalore560 025 India

ISBN: (纸本)0818691670

We present a unimodular transformation called rotation to partition the iteration space of a perfectly nested loop. The transformation captures the individual transformations like loop interchange, reversal, and skewing in a uniform framework. Though the transformation is for any architecture, we have specifically addressed its application to distributed memory machines in this paper. We prove that the transformation is free from deadlocks. We finally describe the choice of an optimal rotation based on the parallelism factor, load imbalance and the volume of communication in the transformed space. © 1991 IEEE.

关键词： Memory architecture

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