The proceedings contain 46 papers. The special focus in this conference is on Recent Advancement in Computer, Communication and Computational Sciences. The topics include: Interactive User Interface to Advent HCI Arte...
ISBN:
(纸本)9789811359330
The proceedings contain 46 papers. The special focus in this conference is on Recent Advancement in Computer, Communication and Computational Sciences. The topics include: Interactive User Interface to Advent HCI Artefact;a Comprehensive Survey on Artificial Bee Colony Algorithm as a Frontier in Swarm Intelligence;Modeling and Simulation of Al6082 MMC of Gravity Die Casting for Solidification Time;a Survey on the Detection of Windows Desktops Malware;Machine Learning Based Approach for Detection of Lung Cancer in DICOM CT Image;extractive Summary: An optimization Approach Using Bat Algorithm;phylogenetics algorithms and applications;comparative Study of Forecasting Model for Price Prediction of Rice;Wind Power Forecasting Using Hybrid ARIMA-ANN Technique;detection of Online Malicious Behavior: An Overview;On the Development of Feature-Based Sprint in AGILE;a Reliable Novel Framework of User-Oriented Software Engineering;android-Based Blind Learning Application;an Approach for Test Case Prioritization Using Harmony Search for Aspect-Oriented Software Systems;onboard Data Acquisition System to Monitor the Vehicle;toward Adapting Metamodeling Approach for Legacy to Cloud Migration;application of Cloud Computing for Priority Job Scheduling by Multiple Robots Operating in a Co-operative Environment;a Framework for Security Management in Cloud Based on Quantum Cryptography;analyzing Student Performance Using Data Mining;prediction of Employee Turnover Using Ensemble Learning;Reliable Data Delivery with Extended IPV4 Using Low-Power Personal Area Network;automated Review Analyzing System Using Sentiment Analysis;a Comprehensive Survey on Extractive and Abstractive Techniques for Text Summarization;cloud Storage–optimization of Initial Phase for Privacy-Preserving Public Auditing;multilevel Steganography for Data Protection.
In this article we aim to combine video data analysis techniques, scalable machine learning, and Shape memory polymers (SMPs) materials to develop a model-based architecture for the advancement of rapid characterizati...
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multilevel inverters are one of the most brilliant technology evolution in power conversion. It provides a stair wave output signal out of multi DC sources used as input. This paper presents a new multilevel inverter ...
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Rapidly changing computer architectures, such as those found at high-performance computing (HPC) facilities, present the need for mini-applications (miniapps) that capture essential algorithms used in large applicatio...
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ISBN:
(数字)9781665422871
ISBN:
(纸本)9781665422888
Rapidly changing computer architectures, such as those found at high-performance computing (HPC) facilities, present the need for mini-applications (miniapps) that capture essential algorithms used in large applications to test program performance and portability, aiding transitions to new systems. The COVID-19 pandemic has fueled a flurry of activity in computational drug discovery, including the use of supercomputers and GPU acceleration for massive virtual screens for therapeutics. Recent work targeting COVID-19 at the Oak Ridge Leadership Computing Facility (OLCF) used the GPU-accelerated program AutoDock-GPU to screen billions of compounds on the Summit supercomputer. In this paper we present the development of a new miniapp, miniAutoDock-GPU, that can be used to evaluate the performance and portability of GPU-accelerated protein-ligand docking programs on different computer architectures. These tests are especially relevant as facilities transition from petascale systems and prepare for upcoming exascale systems that will use a variety of GPU vendors. The key calculations, namely, the Lamarckian genetic algorithm combined with a local search using a Solis-Wets based random optimization algorithm, are implemented. We developed versions of the miniapp using several different programming models for GPU acceleration, including a version using the CUDA runtime API for NVIDIA GPUs, and the Kokkos middle-ware API which is facilitated by C++ template libraries. A third version, currently in progress, uses the HIP programming model. These efforts will help facilitate the transition to exascale systems for this important emerging HPC application, as well as its use on a wide range of heterogeneous platforms.
We present design and optimization of nanoarrays, which consist of metallic nanoparticles that possess plasmonic properties. Optimal two-dimensional arrangements of nanoparticles are found by using an optimization env...
We present design and optimization of nanoarrays, which consist of metallic nanoparticles that possess plasmonic properties. Optimal two-dimensional arrangements of nanoparticles are found by using an optimization environment involving genetic algorithms and a three-dimensional full-wave solver based on the multilevel fast multipole algorithm. The nanoarrays are designed to provide maximum radiation at desired directions when they are excited via isotopic sources. The designed structures and their radiation characteristics are extensively investigated by considering various parameters, such as grid size, nanoparticle shape, distance between nanoparticles, and material. The results demonstrate the favorable radiation characteristics of the designs, as well as the capabilities of the optimization environment to design compact nanoarrays for beam-steering applications.
The analysis of Floating-Point-related issues in HPC codes is becoming a topic of major interest: parallel computing and code optimization often break the reproducibility of numerical results across machines, compiler...
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ISBN:
(纸本)9781728160160
The analysis of Floating-Point-related issues in HPC codes is becoming a topic of major interest: parallel computing and code optimization often break the reproducibility of numerical results across machines, compilers and even executions of the same program. This paper presents how the Verrou tool can help during all stages of the Floating-Point analysis of HPC codes: diagnose, debugging and optimization. Recent developments of Verrou are presented, along with examples illustrating the interest of these new features for industrial codes such as code aster. More specifically, the Verrou arithmetic back-ends now allow analyzing or emulating mixed-precision programs. Interlibm, an interposition layer for the mathematical library, is introduced to mitigate long-standing issues with algorithms from the libm. Finally, debugging algorithms are extended in order to produce useful information as soon as it is available. All these features are available in released version 2.1.0 and upcoming version 2.2.0.
Neural networks deployed on edge devices must be efficient both in terms of their model size and the amount of data movement they cause when classifying inputs. These efficiencies are typically achieved through model ...
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ISBN:
(数字)9780738110448
ISBN:
(纸本)9781665422673
Neural networks deployed on edge devices must be efficient both in terms of their model size and the amount of data movement they cause when classifying inputs. These efficiencies are typically achieved through model compression: pruning a fully trained network model by zeroing out the weights. Given the overall challenge of neural network correctness, we argue that focusing on correctness preservation may allow the community to make measurable progress. We present a state-of-the-art model compression framework called Condensa around which we have launched correctness preservation studies. After presenting Condensa, we describe our initial efforts at understanding the effect of model compression in semantic terms, going beyond the top n% accuracy that Condensa is currently based on. We also take up the relatively unexplored direction of data compression that may help reduce data movement. We report preliminary results of learning from decompressed data to understand the effects of compression artifacts. Learning without decompressing input data also holds promise in terms of boosting efficiency, and we also report preliminary results in this regard. Our experiments centered around a state-of-the-art model compression framework called Condensa and two data compression algorithms, namely JPEG and ZFP, demonstrate the potential for employing model-and dataset compression without adversely affecting correctness.
Complex polynomial optimization has recently gained more attention in both theory and practice. In this paper, we study optimization of a real-valued general conjugate complex form over various popular constraint sets...
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Complex polynomial optimization has recently gained more attention in both theory and practice. In this paper, we study optimization of a real-valued general conjugate complex form over various popular constraint sets including the m-th roots of complex unity, the complex unit circle, and the complex unit sphere. A real-valued general conjugate complex form is a homogenous polynomial function of complex variables as well as their conjugates, and always takes real values. General conjugate form optimization is a wide class of complex polynomial optimization models, which include many homogenous polynomial optimization in the real domain with either discrete or continuous variables, and Hermitian quadratic form optimization as well as its higher degree extensions. All the problems under consideration are NP-hard in general and we focus on polynomial-time approximation algorithms with worst-case performance ratios. These approximation ratios improve previous results when restricting our problems to some special classes of complex polynomial optimization, and improve or equate previous results when restricting our problems to some special classes of polynomial optimization in the real domain. The algorithms are based on tensor relaxation and random sampling. Our novel technical contributions are to establish the first set of probability lower bounds for random sampling over the m-th root of unity, the complex unit circle, and the complex unit sphere, and to propose the first polarization formula linking general conjugate forms and complex multilinear forms. Some preliminary numerical experiments are conducted to show good performance of the proposed algorithms.
This research focuses on the two purposes of maximizing the amount of heat generated by incineration and minimizing the collection distance of waste, in determining allocations and locations of general waste incinerat...
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This research focuses on the two purposes of maximizing the amount of heat generated by incineration and minimizing the collection distance of waste, in determining allocations and locations of general waste incineration facilities as a case study of Chiba northwest bay area. For these purposes, we propose the multi-objective optimization with Voronoi diagram and genetic algorithm (MOVGA). As for the maximization of the amount of generated heat, we predict the amount by using regression equation of multiple linear regression analysis and formulate it as the set partitioning problem (SPP) to maximize the prediction value. As for the minimization of waste collection distances, we formulate it as the multi-Weber problem. To solve these two problems, we use MOVGA, which has the seeds of the Voronoi diagram as a gene. As a result of the survey using data of 2015 year of Chiba northwest bay area, in the case of 3 facilities it was found that the calorific value increased enough to cover the power of 4,205 households (converted to housing complex) per year despite the increase of 3% t-km per year.
We propose a parallel stochastic Newton method (PSN) for minimizing unconstrained smooth convex functions. We analyze the method in the strongly convex case, and give conditions under which acceleration can be expec...
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We propose a parallel stochastic Newton method (PSN) for minimizing unconstrained smooth convex functions. We analyze the method in the strongly convex case, and give conditions under which acceleration can be expected when compared to its serial counterpart. We show how PSN can be applied to the large quadratic function minimization in general, and empirical risk minimization problems. We demonstrate the practical efficiency of the method through numerical experiments and models of simple matrix classes.
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