Efficient quality control is critical in microarray technology. It can be best ensured by the Third Dye Array Visualization (TDAV) technology, which allows quality evaluation of all fabricated microarray slides prior ...
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ISBN:
(纸本)1932415262
Efficient quality control is critical in microarray technology. It can be best ensured by the Third Dye Array Visualization (TDAV) technology, which allows quality evaluation of all fabricated microarray slides prior to their use in clinical/biological tests. To fully utilize the potential of TDAV technology, we have recently introduced the parallelprocessing algorithm to microarray data acquisition. We implemented our printing batch quality control module in the Message Passing Interface (MPI) standards, thus permitting quality evaluation of thousands of slides efficiently prior to distribution to individual investigators for hybridization. We have also implemented a critical step in data acquisition from microarray images in MPI: the signal detection and signal background segmentation. Our work represents the first time use of a parallel algorithm in the microarray field.
We have developed a parallel and distributed computing framework to solve an inverse problem, which involves massive data sets and is of great importance to petroleum industry. A Monte Carlo method, combined with prox...
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ISBN:
(纸本)9781932415582
We have developed a parallel and distributed computing framework to solve an inverse problem, which involves massive data sets and is of great importance to petroleum industry. A Monte Carlo method, combined with proxies to avoid excessive data processing, is employed to identify reservoir simulation models that best match the oilfield production history. Subsequently, the selected models are used to forecast future productions with uncertainty estimates. The parallelization framework combines: 1) message passing for tightly coupled intra-simulation decomposition;and 2) scheduler/Grid remote procedure calls for model parameter sweeps. A preliminary numerical test has included 3,159 simulations on a 256-processor Intel Xeon cluster at the USC-CACS. The results provide uncertainty estimates of unprecedented precision.
Proceedings of the 2019 international conference on parallel and distributed processing techniques and applications (PDPTA'19) held July 29th - August 1st, 2019 in Las Vegas, Nevada.
Proceedings of the 2019 international conference on parallel and distributed processing techniques and applications (PDPTA'19) held July 29th - August 1st, 2019 in Las Vegas, Nevada.
Virtual Machine Migration (VMM) is a key technology in data centers. Due to the uncertainty of applications in resource allocation, the imbalance of resource utilization is badly poor. In this paper, an Auto-regressio...
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ISBN:
(纸本)9781538637906
Virtual Machine Migration (VMM) is a key technology in data centers. Due to the uncertainty of applications in resource allocation, the imbalance of resource utilization is badly poor. In this paper, an Auto-regression Moving Average model is proposed to predict the resource requirement of a certain virtual machine, while the resource utilization rate of the physical machine is analyzed. Since the existing VMM scheme basically follows one by one migration, which makes the migration be unable to achieve the global optimum and save more energy, the paper introduces migrated cost matrix, and recommends a set of migrations with the best performance from a global point of view to carry out the migration, and improves the "three-step" method in the traditional migrated scheme to achieve energy efficiency. Simulation experiments show that the proposed scheme in the paper can effectively reduce the energy consumption, and can improve the quality of service in a certain degree.
Acceleration for the training process of Deep Neural Networks (DNNs) has been the focus of deep learning field. There were many researches of accelerating deep learning on different platforms. Among them, Intel Xeon P...
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ISBN:
(纸本)9781538637906
Acceleration for the training process of Deep Neural Networks (DNNs) has been the focus of deep learning field. There were many researches of accelerating deep learning on different platforms. Among them, Intel Xeon Phi Co-processor is a many-core platform which provides both strong programmability and high performance. But previous work about Intel Many Integrated Core (MIC) focused on parallel computing only in MIC. In this paper, we speed up the training process of DNNs applied for automatic speech recognition with CPU+MIC architecture. In this architecture, the training process of DNNs is executed both on MIC and CPU. We apply several optimization methods for I/O and calculation and set up experiments to approve these methods. Putting all methods together, results show that our optimized algorithm acquires about 20x speedup compared with the original sequential algorithm on CPU which uses one core.
The performance of a conservative time management algorithm in a distributed simulation system degrades significantly if a large number of null messages are exchanged across the logical processes in order to avoid dea...
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ISBN:
(纸本)1601320841
The performance of a conservative time management algorithm in a distributed simulation system degrades significantly if a large number of null messages are exchanged across the logical processes in order to avoid deadlock. This situation gets more severe when the exchange of null messages is increased due to the poor selection of key parameters such as lookahead values. This paper presents a generic mathematical model that uses null messages to avoid deadlock. Since the proposed mathematical model is generic, the performance of any conservative synchronization algorithm can be approximated. In addition, we develop a performance model that demonstrates that how a conservative distributed simulation system performs with the null message algorithm (NMA). The simulation results show that the performance of a distributed system degrades if the NMA generates an excessive number of null messages due to the improper selection of parameters.
The extensive growth of smartphones has spawned the propagation of malicious applications. Due to the increasing use of polymorphic malware, detection is becoming more difficult. To this end, ensemble learning has bee...
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ISBN:
(纸本)9781538637906
The extensive growth of smartphones has spawned the propagation of malicious applications. Due to the increasing use of polymorphic malware, detection is becoming more difficult. To this end, ensemble learning has been proposed to improve accuracy in malware detection, without severely sacrificing time complexity. In this paper, we propose a hybrid detection system, TFBOOST, which incorporates the tensor filter algorithm into boosting ensemble generalization architecture, in order to improve detection efficacy. TFBOOST uses a static analysis to extract features and a level-by-level boosting structure with re-sampling process to diversify base learners. Experimental results show that TFBOOST generally outperforms state-of-the-art ensemble algorithms with higher detection precision and lower false positive rates. Finally, we visually interpret the high-level results of TFBOOST and conjecture that repackaged malware is the mainstay of potential malware.
Trust between communicating peers is increasingly catching the attention of the research community. Several trust models and trust management protocols have been proposed to assist the task of establishing trust betwe...
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ISBN:
(纸本)1932415262
Trust between communicating peers is increasingly catching the attention of the research community. Several trust models and trust management protocols have been proposed to assist the task of establishing trust between communicating peers, all of which make use of reputation mechanisms. In the present literature on reputation, a classification of reputation does not exist. In this paper, we present a three dimensional classification of reputation for peer-to-peer communication. We validate our classification for reputation by drawing from the existing proposed trust models. Additionally, we show that the different types of reputation along the three dimensions are interrelated and cannot be treated in isolation.
The Fortran parallel Transformer (FPT) is a parallelization tool for Fortran-77 programs. It is used for the automatic parallelization of loops, program transformations, dependence analysis, performance tuning and cod...
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The Fortran parallel Transformer (FPT) is a parallelization tool for Fortran-77 programs. It is used for the automatic parallelization of loops, program transformations, dependence analysis, performance tuning and code generation for various platforms. FPT is able to deal with GOTO's by restructuring ill-structured code using hammock graph transformations. In this way more parallelism becomes detectable. The X-window based Programming Environment, PEFPT, extends FPT with interactive dependence analysis, the iteration space graph, ISG, and guided loop optimization. FPT contains a PVM (parallel Virtual Machine) code generator which converts the parallel loops into PVM master- and slave-code for a network of workstations. This includes job schedul;ing, synchronization and optimized data communication. The productivity gained is about a factor of 10 in programming time and a significant speedup of the execution. (C) 1998 Elsevier Science Inc. All rights reserved.
Programming message passing systems can be tedious and error-prone, especially for the inexperienced user facing the sheer amount of available functionality in todays message passing libraries. Instead of choosing the...
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ISBN:
(纸本)1932415262
Programming message passing systems can be tedious and error-prone, especially for the inexperienced user facing the sheer amount of available functionality in todays message passing libraries. Instead of choosing the most optimal communication function, many users tend to apply only a small set of well-known standard operations. This paper describes a pattern matching approach based on execution traces which detects connected groups of point-to-point communication operations that may resemble existing collective operations. After high-lighting the detected patterns, users are able to improve their codes by replacing the point-to-point operations with more appropriate and efficient collective alternatives.
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