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Selected Papers of the 12th International Workshop on Applied Problems in Theory of Probabilities and Mathematical Statistics (Summer Session) in the Framework of the conference on Information and Telecommunication Technologies and Mathematical Modeling of high-Tech Systems, APTP+MS 2018

作者： Daraseliya, Anastasia V. Sopin, Eduard S. Rykov, Vladimir V. Department of Applied Probability and Informatics Peoples' Friendship University of Russia Miklukho-Maklaya Str. 6 Moscow117198 Russia Institute of Informatics Problems FRC CSC RAS 44-2 Vavilov Str. Moscow119333 Russia Departmen of Applied Mathematics and Computer Modeling Gubkin Russian State University of Oil and Gas 65 Leninsky Prospekt Moscow119991 Russia

We constructed mathematical models of cloud computing systems taking into account various mechanisms for increasing energy efficiency in terms of queuing theory, and analytical expressions for the main characteristics of energy consumption and server performance metrics were obtained. We investigated and compered three different energy efficiency improving mechanisms of cloud computing systems: the shutdown delay mechanism, the switch on delay mechanism and the threshold-based switch on mechanism. The general principle of functioning mechanisms for energy efficiency improving is that all mechanisms try to find a middle ground between continuous operation without shutdowns and with switching on as soon as it remains empty. We formulated the energy consumption optimization problem of the cloud computing system for each parameter used in this energy efficiency mechanisms. We conducted a numerical analysis of the formulas for solving the optimization problem of energy consumption in cloud computing system based on the initial data close to the real ones. Copyright © 2018 for the individual papers by the papers' authors.

关键词： Optimization

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Recovery of soil moisture active passive (smap) instrument's active measurements via coupled dictionary learning 16

Recovery of soil moisture active passive (smap) instrument's...

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16th Computational Imaging conference, COMIG 2018

作者： Fotiadou, Konstantina Tsagkatakis, Grigorios Moghaddam, Mahta Tsakalides, Panagiotis Crete Greece Department of Computer Science University of Crete Crete Greece Los AngelesCA United States

NASA's Soil Moisture Active Passive (SMAP) satellite mission combines a passive L-band radiometer and an active Synthetic Aperture Radar (SAR) instrument in order to monitor the near-surface soil moisture and freeze-thaw states globally, with a revisit frequency of 2-3 days. SMAP provides three soil moisture products: A high-resolution from the radar, a low-resolution from the radiometer, and an intermediate-resolution from the fusion of the radar and radiometer measurements. Unfortunately, SMAP's SAR instrument halted its transmissions after a short operating period. In order to address this limitation, we introduce a novel post-acquisition computational technique aiming to synthesize the active measurements of SMAP, by exploiting the mathematical frameworks of Sparse Representations and Dictionary Learning. We propose a coupled dictionary learning model which considers joint feature spaces, composed of active and passive images, in order to recover the missing active measurements. We formulate our coupled dictionary learning problem within the context of the Alternating Direction Method of Multipliers. Our experimental results demonstrate the ability of the proposed approach to reconstruct the active measurements, achieving better performance compared to state-of-the-art coupled dictionary learning techniques. © 2018, Society for Imaging Science and Technology.

关键词： Soil moisture

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Energy-Efficient, high-performance, highly-Compressed Deep Neural Network Design using Block-Circulant Matrices 17

Energy-Efficient, High-Performance, Highly-Compressed Deep N...

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IEEE/ACM 36th International conference on computer-Aided Design (ICCAD)

作者： Liao, Siyu Li, Zhe Lin, Xue Qiu, Qinru Wang, Yanzhi Yuan, Bo CUNY New York NY 10021 USA Syracuse Univ Syracuse NY 13244 USA Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781538630938

Deep neural networks (DNNs) have emerged as the most powerful machine learning technique in numerous artificial intelligent applications. However, the large sizes of DNNs make themselves both computation and memory intensive, thereby limiting the hardware performance of dedicated DNN accelerators. In this paper, we propose a holistic framework for energy-efficient high-performance highly-compressed DNN hardware design. First, we propose block-circulant matrix-based DNN training and inference schemes, which theoretically guarantee Big-O complexity reduction in both computational cost (from O(n(2)) to O(n log n)) and storage requirement (from O(n(2)) to O(n)) of DNNs. Second, we dedicatedly optimize the hardware architecture, especially on the key fast Fourier transform (FFT) module, to improve the overall performance in terms of energy efficiency, computation performance and resource cost. Third, we propose a design flow to perform hardware-software cooptimization with the purpose of achieving good balance between test accuracy and hardware performance of DNNs. Based on the proposed design flow, two block-circulant matrix-based DNNs on two different datasets are implemented and evaluated on FPGA. The fixed-point quantization and the proposed block-circulant matrix-based inference scheme enables the network to achieve as high as 3.5 TOPS computation performance and 3.69 TOPS/W energy efficiency while the memory is saved by 108X similar to 116X with negligible accuracy degradation.

关键词： Hardware Training Complexity theory Mathematical model Artificial neural networks Computational modeling Computational efficiency

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Context-Aware Memory Profiling for Speculative Parallelism 24

Context-Aware Memory Profiling for Speculative Parallelism

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IEEE 24th International conference on high performance Computing Workshops (HiPCW)

作者： Kim, Changsu Kim, Juhyun Kang, Juwon Lee, Jae W. Kim, Hanjun POSTECH Pohang South Korea Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781538622933

To expose hidden parallelism from programs with complex dependences, modern compilers employ memory profilers to augment imprecise static analyses. Since dynamic dependence patterns among instructions can vary widely depending on the context, such as function call site stack and loop nest level, context-aware memory profiling is of great value for precise memory profiling. However, recording memory dependences with full context information causes huge overheads in terms of CPU cycles and memory space. Existing profilers mitigate this problem by compromising precision, coverage, or both. This paper proposes a new precise Context-Aware Memory Profiling (CAMP) framework that efficiently traces all the memory dependences with full context information. CAMP statically analyzes a context tree of a program that illustrates all the possible dynamic contexts, and simplifies context management during profiling. For 14 programs from SPEC CINT2000 and CINT2006 benchmark suites, CAMP increases speculative parallelism opportunities by 12.6% on average and by up to 63.0% compared to the baseline context -oblivious, loop-aware memory profiler.

关键词： conferences high performance computing

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performance Evaluation of Apache Hadoop Benchmarks under a Dynamic Checkpointing Mechanism

Performance Evaluation of Apache Hadoop Benchmarks under a D...

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computer Science Society (SCCC) International conference Chilean FLAGGED

作者： Paulo Vinicius Cardoso Patricia Pitthan Barcelos Post Graduation Program in Informatics Federal University of Santa Maria Santa Maria RS Brazil Computing Systems Laboratory Federal University of Santa Maria Santa Maria RS Brazil

ISBN: (纸本)9781538692349;9781538692332

The increasingly robust high performance Computing (HPC) systems have promoted many researches on fault tolerance mechanisms. However, the efficiency of these techniques is an important challenge, as the data and resources demand is also getting higher. in this work, we presents how the framework Apache Hadoop implements the Checkpoint and Recovery technique for fault tolerance providing on its distributed file system (Hadoop Distributed File System). The demand of large amounts of data from all applications supported by Hadoop framework can compromise the mechanism efficiency, once its configurations attributes are defined statically. So we present a dynamic configuration mechanism to checkpoint for Apache Hadoop through a resource monitoring, whose goal is to make the Hadoop checkpoint adaptable. The proposed mechanism is then evaluated and submitted to performance tests.

关键词： Cluster computing Fault tolerance Fault tolerant systems Monitoring Heart beat File systems

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Photoluminescence and Electrical Properties study of ITO-stabilized ZnO Thin-Film Transistors with different annealing temperatures

Photoluminescence and Electrical Properties study of ITO-sta...

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International conference on Display Technology, ICDT 2018

作者： Zhong, Wei Li, Guoyuan Deng, Sunbin Yin, Xuemei Lan, Linfeng Chen, Rongsheng Kwok, Hoi Sing School of Electronic and Information Engineering South China University of Technology Guangzhou China State Key Laboratory on Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong School of Materials Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781510867659

Bottom-gate thin-film transistors (TFTs) with ITO-stabilized ZnO channel layers were successfully fabricated by co-sputtering of ZnO target and ITO target. We investigate the effects of the post-deposition thermal annealing temperature on the defects inside of the ITO-stabilized ZnO thin film and on the electrical performance of ITO-stabilized ZnO TFTs. The beneficial effect of various annealing temperature appears because of a reorganization of the amorphous network with controlling the defect chemistry in the films indicated by photoluminescence analysis. ITO-stabilized ZnO thin film with the annealing temperature of 300°C exhibited less intense defect emission peaks due to reduction in the number of vacancies and interstitials via filling the oxygen vacancies with O2, and diffusion of zinc interstitial to exact sites (VZn) of the system. Furthermore, We systematically investigated the impact of various annealing temperature on their performance characteristics. It was found that ITO-stabilized ZnO TFT with annealing temperature of 300°C exhibited fairly high electrical characteristics, especially with a saturation field-effect mobility of 27.4 cm2V-1s-1 and the threshold voltage as low as -0.64 V. The implied correlation between the post-deposition thermal treatment and the characteristics of a transistor was investigated and demonstrated by photoluminescence analysis. © 2018 SID.

关键词： Thin film transistors

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GraphTuner: An Input Dependence Aware Loop Perforation Scheme for Efficient Execution of Approximated Graph Algorithms 35

GraphTuner: An Input Dependence Aware Loop Perforation Schem...

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35th IEEE International conference on computer Design (ICCD)

作者： Omar, Hamza Ahmad, Masab Khan, Omer Univ Connecticut Storrs CT 06269 USA

ISBN: (纸本)9781538622544

Graph algorithms have gained popularity and are utilized in high performance and mobile computing paradigms. Input dependence due to input graph changes leads to performance variations in such algorithms. The impact of input dependence for graph algorithms is not well studied in the context of approximate computing. This paper conducts such analysis by applying loop perforation, which is a general approximation mechanism that transforms the program loops to drop a subset of their total iterations. The analysis identifies the need to adapt the inner and outer loop perforation as a function of input graph characteristics, such as the density or size of the graph. A predictive model is proposed to learn the near-optimal loop perforation rates using synthetic input graphs. When the input-aware loop perforation model is applied to real world graphs, the evaluated graph algorithms systematically degrade accuracy to achieve performance and power benefits. Results show similar to 30% performance and similar to 19% power utilization improvements on average at a program accuracy loss threshold of 10% for an NVidia((R)) GPU. The analysis is also conducted for two concurrent Intel((R)) CPU architectures, an 8-core Xeon(TM) and a 61-core Xeon Phi(TM) machine.

关键词： Approximation algorithms Algorithm design and analysis Prediction algorithms Benchmark testing computer architecture Complexity theory Quality of service

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Field surveillance and ai based steam allocation optimization workflow for mature brownfield steam floods

Field surveillance and ai based steam allocation optimizatio...

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SPE International Heavy Oil conference and Exhibition 2018, HOCE 2018

作者： Kumar, Anjani Novlesky, Alex Bityutsky, Erykah Koci, Paul Wightman, Jeff Computer Modelling Group Ltd. United States Occidental Petroleum Corporation United States

ISBN: (纸本)9781613996409

Heavy oil reservoirs often require thermal enhanced oil recovery (EOR) processes to improve the mobility of the highly viscous oil. When working with steam flooding operations, finding the optimal steam injection rates is very important given the high cost of steam generation and the current low oil price environment. Steam injection and allocation then becomes an exercise of optimizing cost, improving productivity and net present value (NPV). As the field matures, producers are faced with declining oil rates and increasing steam oil ratios (SOR). Operators must work to reduce injection rates on declining groups of wells to maintain a low SOR and free up capacity for newer, more productive groups of wells. Operators also need a strong surveillance program to monitor field operational parameters like SOR, remaining Oil-in-Place (OIP) distribution in the reservoir, steam breakthrough in the producers, temperature surveys in observation wells etc. Using the surveillance data in conjunction with reservoir simulation, operators must determine a go-forward operating strategy for the steam injection process. The proposed steam flood optimization workflow incorporates field surveillance data and numerical simulation, driven by machine learning and AI enabled Algorithms, to predict future steam flood reservoir performance and maximize NPV for the reservoir. The process intelligently determines an optimal current field level and well level injection rates, how long to inject at that rate, how fast to reduce rates on mature wells so that it can be reallocated to newly developed regions of the field. A case study has been performed on a subsection of a Middle Eastern reservoir containing eight vertical injectors and four sets of horizontal producers with laterals landed in multiple reservoir zones. Following just the steam reallocation optimization process, NPV for the section improved by 42.4% with corresponding decrease in cumulative SOR by 24%. However, if workover and alter

关键词： Steam

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Near Automatic Translation of Autonomie-Based Power Train Architectures for Multi-Physics Simulations Using high performance Computing

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SAE INTERNATIONAL JOURNAL OF COMMERCIAL VEHICLES 2017年第2期10卷 483-488页

作者： Haupt, Tomasz Henley, Gregory Card, Angela Mazzola, Michael S. Doude, Matthew Shurin, Scott Goodin, Christopher Mississippi State Univ Starkville MS 39759 USA US Army TARDEC Warren MI USA US Army Erdc Vicksburg MS USA

The Powertrain Analysis and Computational Environment (PACE) is a powertrain simulation tool that provides an advanced behavioral modeling capability for the powertrain subsystems of conventional or hybrid-electric vehicles. Due to its origins in Argonne National Lab's Autonomie, PACE benefits from the reputation of Autonomic as a validated modeling tool capable of simulating the advanced hardware and control features of modern vehicle powertrains. llowever, unlike Autonomic that is developed and executed in Mathwork's MATLAB/Simulink environment, PACE is developed in C++ and is targeted for high-performance Computing (HPC) platforms. Indeed, PACE is used as one of several actors within a comprehensive ground vehicle co-simulation system (CRES-GV MERCURY): during a single MERCURY run, thousands of concurrent PACE instances interact with other high-performance, distributed MERCURY components. A proof-of-concept implementation of PACE, as applied to a conventional powertrain architecture, was presented at the SAE2016 conference. Since then, a C++ library of components implementing the functionality of the corresponding Simulink subsystems has been developed, followed by streamlining the process of the generation of the C++ code for a particular powertrain;the native Simulink XML representation of the architecture (components and their connectivity) is used for an automatic generation of the simulation workflow and thus effectively reproducing the functionality of the Autonomie models while making it compliant to MERCURY requirements. The resulting PACE models are rigorously verified and validated against results generated by Simulink runs. Furthermore, the whole process is largely automated, thus providing time savings when implementing new vehicle architectures.

关键词： AUTOMOBILE power trains AUTOMOBILE part testing SIMULATION methods & models high performance computing AUTOMOTIVE engineering

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A seven-year study of middle and high school teachers participating in research experiences for teachers programs: Exploring the relationship between teacher performance and student achievement 124

A seven-year study of middle and high school teachers partic...

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124th ASEE Annual conference and Exposition

作者： Ragusa, Gisele Juarez, Cathalina University of Southern California United States

Research identifies a national urgency to improve teacher performance and student achievement in science and engineering. This paper responds to this need and presents the results of seven-years of Research Experience for Teachers (RET) programs funded by the National Science Foundation in which engineering faculty collaborate with middle and high school teachers and their students. One program (3 years) is a comprehensive teacher professional development program in which middle school teachers participate in an intensive summer research experience in computer science and engineering labs, build curriculum based on the laboratory research content that they learn, participate in lesson study, and implement new curriculum in their middle classrooms. The second program (4 years) is a high school teacher RET program with similar components. This paper reports the results of both of the RET programs. The two programs had the combined intent of bringing innovative computer science and engineering research to middle and high school teachers and their students and improving teacher performance, while simultaneously improving student achievement through scientific inquiry, engaging students in computational thinking, and engineering design. The programs' design included a summer intensive experience in which teachers fully participated in a computer science or engineering laboratory research and engaged in an inquiry focused content-to-pedagogy teacher professional development workshop, building curriculum from their lab research experience with foci on scientific experimentation and improving students' science achievement and literacy. The programs were both aligned with Common Core Math Standards and Next Generation Science Standards and addressed the research question: What is the impact of an intensive research-based teacher professional development program on teacher and student performance? In total, seventy teachers and their 10, 398 students participated in the two R

关键词： Students

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