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GPU-based normalized cuts for road extraction using satellite imagery

JOURNAL OF EARTH SYSTEM SCIENCE 2014年第8期123卷 1759-1769页

作者： Senthilnath, J. Sindhu, S. Omkar, S. N. Indian Inst Sci Dept Aerosp Engn Bangalore 560012 Karnataka India

This paper presents a GPU implementation of normalized cuts for road extraction problem using panchromatic satellite imagery. The roads have been extracted in three stages namely pre-processing, image segmentation and post-processing. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, vegetation,. and fallow regions). The road regions are then extracted using the normalized cuts algorithm. Normalized cuts algorithm is a graph-based partitioning 'approach whose focus lies in extracting the global impression (perceptual grouping) of an image rather than local features. For the segmented image, post-processing is carried out using morphological operations - erosion and dilation. Finally, the road extracted image is overlaid on the original image. Here, a GPGPU (General Purpose graphical processing unit) approach has been adopted to implement the same algorithm on the GPU for fast processing. A performance comparison of this proposed GPU implementation of normalized cuts algorithm with the earlier algorithm (CPU implementation) is presented. From the results, we conclude that the computational improvement in terms of time as the size of image increases for the proposed GPU implementation of normalized cuts. Also, a qualitative and quantitative assessment of the segmentation results has been projected.

关键词： Road extraction normalized cuts graphical processing unit

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Highly interactive computational steering for coupled 3D flow problems utilizing multiple GPUs

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COMPUTING AND VISUALIZATION IN SCIENCE 2010年第7期13卷 299-314页

作者： Linxweiler, Jan Krafczyk, Manfred Toelke, Jonas Braunschweig Univ Technol Inst Computat Modelling Civil Engn Pockelsstr 3 D-38106 Braunschweig Germany Ingrain Inc Houston TX 77027 USA

Most computational fluid dynamics (CFD) simulations require massive computational power which is usually provided by traditional High Performance Computing (HPC) environments. Although interactivity of the simulation process is highly appreciated by scientists and engineers, due to limitations of typical HPC environments, present CFD simulations are usually executed non interactively. A recent trend is to harness the parallel computational power of graphics processing units (GPUs) for general purpose applications. As an alternative to traditional massively parallel computing, GPU computing has also gained popularity in the CFD community, especially for its application to the lattice Boltzmann method (LBM). For instance, Tolke and others presented very efficient implementations of the LBM for 2D as well as 3D space (Toelke J, in Comput Visual Sci. (2008);Toelke J and Krafczk M, in Int J Comput Fluid Dyn 22(7): 443-456 (2008)). In this work we motivate the use of GPU computing to facilitate interactive CFD simulations. In our approach, the simulation is executed on multiple GPUs instead of traditional HPC environments, which allows the integration of the complete simulation process into a single desktop application. To demonstrate the feasibility of our approach, we show a fully bidirectional fluid-structure-interaction for self induced membrane oscillations in a turbulent flow. The efficiency of the approach allows a 3D simulation close to realtime.

关键词： Computational steering Computational fluid dynamics Lattice Boltzmann method graphical processing unit High performance computing Design patterns

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Tempest: GPU-CPU Computing for High-Throughput Database Spectral Matching

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JOURNAL OF PROTEOME RESEARCH 2012年第7期11卷 3581-3591页

作者： Milloy, Jeffrey A. Faherty, Brendan K. Gerber, Scott A. Dartmouth Hitchcock Med Ctr Norris Cotton Canc Ctr Lebanon NH 03756 USA Geisel Sch Med Dartmouth Dept Genet Lebanon NH 03756 USA Geisel Sch Med Dartmouth Dept Biochem Lebanon NH 03756 USA

Modern mass spectrometers are now capable of producing hundreds of thousands of tandem (MS/MS) spectra per experiment, making the translation of these fragmentation spectra into peptide matches a common bottleneck in proteomics research. When coupled with experimental designs that enrich for post-translational modifications such as phosphorylation and/or include isotopically labeled amino acids for quantification, additional burdens are placed on this computational infrastructure by shotgun sequencing. To address this issue, we have developed a new database searching program that utilizes the massively parallel compute capabilities of a graphical processing unit (GPU) to produce peptide spectral matches in a very high throughput fashion. Our program, named Tempest, combines efficient database digestion and MS/MS spectral indexing on a CPU with fast similarity scoring on a GPU. In our implementation, the entire similarity score, including the generation of full theoretical peptide candidate fragmentation spectra and its comparison to experimental spectra, is conducted on the GPU. Although Tempest uses the classical SEQUEST XCorr score as a primary metric for evaluating similarity for spectra collected at unit resolution, we have developed a new "Accelerated Score" for MS/MS spectra collected at high resolution that is based on a computationally inexpensive dot product but exhibits scoring accuracy similar to that of the classical XCorr. In our experience, Tempest provides compute-cluster level performance in an affordable desktop computer.

关键词： search algorithm peptide spectral matching graphical processing unit GPU tandem mass spectrometry peptide identification

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Reinforcement learning based distantly supervised biomedical named entity recognition

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2023年第2期17卷 317-330页

作者： Bali, Manish Anandaraj, S. P. Presidency Univ Dept Comp Sci & Engn Bengaluru 560064 Karnataka India

Data used by current Biomedical named entity recognition (BioNER) systems has mostly been manually labelled for supervision. However, it might be difficult to find large amounts of annotated data, especially in fields with a high level of specialization, such as biomedical, bioinformatics, and so on. When dictionaries and ontologies are available, which are domain-specific knowledge resources, automatically tagged distantly supervised biomedical training data can be developed. However, any such distantly supervised NER result is normally noisy. The prevalence of false positives and false negatives with this type of autonomously generated data is the main problem that directly affects efficiency. This research investigates distant supervision to detect false positive occurrences in BioNER task. A reinforcement learning technique is employed that is modelled as a graphical processing unit (GPU) accelerated Markov decision process (MDP) with a neural network policy. To deal with false negative cases, we employ a partial annotation conditional random field (CRF) technique. Results on two benchmark datasets show a cutting-edge methodology that can enhance the functionality of the neural NER system. It goes on to show how the proposed approach cuts down on human annotated data for BioNER tasks in Natural Language processing (NLP).

关键词： Named entity recognition reinforcement learning neural network Markov decision process graphical processing unit

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Run-time neuro-fuzzy type-2 controller for power optimisation of GP-GPU architecture

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IET CIRCUITS DEVICES & SYSTEMS 2020年第8期14卷 1253-1257页

作者： Najam, Shaheryar Ahmed, Jameel Riphah Int Univ Dept Elect Engn Islamabad Pakistan

The increasing demand for high-performance computing has emphasised the invocation of sophisticated multi/many-core computing architecture. graphical processing unit (GPU) is considered to be an essential innovation in this regard as GPU offers a significant amount of parallelism in the execution of complex computing applications. The performance of GPUs in reducing the computational time of such applications is worth mentioning. Although GPUs appear to be a problem-solving solution for complex applications yet high power consumption has been a challenging problem, associated with this many-core computer architecture. Efficient resource management is a emerging and promising solution to this challenge;however, reducing the resources would degrade the system's overall performance. On the other hand, reducing the resources based on the analysis of workload can save significant power without degrading the system's overall performance. Therefore, a smart controller to optimise the resources of general purpose-GPU (GP-GPU) architecture is required. AFBRMC-2, a neuro-fuzzy type-2 based controller, is presented for GP-GPU architecture and, based on a feedback mechanism, keeps analysing the stats of processor and manages resources using dynamic voltage frequency scaling and core gating techniques. The proposed controller achieved up to 55% reduction in power consumption against various benchmarks on the NVIDIA TK1 GPU kit.

关键词： parallel architectures graphics processing units fuzzy neural nets fuzzy control multiprocessing systems power aware computing neurocontrollers feedback resource allocation power consumption power optimisation GP-GPU architecture high-performance computing graphical processing unit computational time problem-solving solution power consumption many-core computer architecture smart controller general purpose-GPU architecture AFBRMC-2 dynamic voltage frequency scaling core gating NVIDIA TK1 GPU kit run-time neuro-fuzzy type-2 controller many-core computing architecture multicore computing architecture parallelism resource management feedback mechanism

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Solving path problems on the GPU

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PARALLEL COMPUTING 2010年第5-6期36卷 241-253页

作者： Buluc, Aydin Gilbert, John R. Budak, Ceren Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA

We consider the computation of shortest paths on Graphic processing units (GPUs). The blocked recursive elimination strategy we use is applicable to a class of algorithms (such as all-pairs shortest-paths, transitive closure, and LU decomposition without pivoting) having similar data access patterns. Using the all-pairs shortest-paths problem as an example, we uncover potential gains over this class of algorithms. The impressive computational power and memory bandwidth of the CPU make it an attractive platform to run such computationally intensive algorithms. Although improvements over CPU implementations have previously been achieved for those algorithms in terms of raw speed, the utilization of the underlying computational resources was quite low. We implemented a recursively partitioned all-pairs shortest-paths algorithm that harnesses the power of GPUs better than existing implementations. The alternate schedule of path computations allowed us to cast almost all operations into matrix-matrix multiplications on a semiring. Since matrix-matrix multiplication is highly optimized and has a high ratio of computation to communication, our implementation does not suffer from the premature saturation of bandwidth resources as iterative algorithms do. By increasing temporal locality, our implementation runs more than two orders of magnitude faster on an NVIDIA 8800 CPU than on an Opteron. Our work provides evidence that programmers should rethink algorithms instead of directly porting them to CPU. (C) 2009 Elsevier By. All rights reserved.

关键词： All-pairs shortest-paths Gaussian elimination graphical processing unit Semiring Matrix multiplication Graph algorithm Shortest path Linear algebra

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Implementation of Algorithms with a Fine-Grained Parallelism on GPUs

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NUMERICAL ANALYSIS AND APPLICATIONS 2011年第1期4卷 46-55页

作者： Kalgin, K. V. Russian Acad Sci Inst Comp Math & Math Geophys Siberian Branch Pr Akad Lavrenteva 6 Novosibirsk 630090 Russia

The efficiency of implementations of algorithms with a fine-grained parallelism onGPUs that support the CUDA architecture is studied. Cellular automata and difference schemes are used for testing. Several versions of implementations are proposed and their efficiency is analyzed. An example of GPU application for modeling the process of carbon dioxide oxidation on the catalyst surface is given.

关键词： graphical processing unit GPGPU CUDA cellular automata fine-grained algorithms parallel implementation

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GPU-Powered Shotgun Stochastic Search for Dirichlet Process Mixtures of Gaussian graphical Models

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JOURNAL OF COMPUTATIONAL AND graphical STATISTICS 2016年第3期25卷 762-788页

作者： Mukherjee, Chiranjit Rodriguez, Abel Netflix Los Gatos CA USA Univ Calif Santa Cruz Dept Appl Math & Stat Santa Cruz CA 95064 USA

Gaussian graphical models (GGMs) are popular for modeling high-dimensional multivariate data with sparse conditional dependencies. A mixture of GGMs extends this model to the more realistic scenario where observations come from a heterogenous population composed of a small number of homogeneous subgroups. In this article, we present a novel stochastic search algorithm for finding the posterior mode of high dimensional Dirichlet process mixtures of decomposable GGMs. Further, we investigate how to harness the massive thread-parallelization capabilities of graphical processing units to accelerate computation. The computational advantages of our algorithms are demonstrated with various simulated data examples in which we compare our stochastic search with a Markov chain Monte Carlo (MCMC) algorithm in moderate dimensional data examples. These experiments show that our stochastic search largely outperforms the MCMC algorithm in terms of computing-times and in terms of the quality of the posterior mode discovered. Finally, we analyze a gene expression dataset in which MCMC algorithms are too slow to be practically useful.

关键词： Dirichlet process mixture Gaussian graphical model graphical processing unit Stochastic search

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Aligning molecular sequences using hybrid bioinspired algorithm in GPU

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2020年第1期21卷 125-136页

作者： Jayapriya, J. Arock, Michael Natl Inst Technol Dept Comp Applicat Tiruchirappalli Tamil Nadu India

To explicate the functionality of the basic cell, there is a need for the study of bioinformatics. To better understand the structural and functional information of molecules, sequence analysis is considered as the root domain. In this, aligning the sequence is the first step, an NP-complete problem like all biological problems. Owing to the increased molecular data in biology, there is a demand for the development of efficient approaches to this sequence alignment problem. From the study it is concluded that there is trade-off between accuracy and computational time. Focusing on the latter in this paper, a new parallel hybridised bio-inspired approach (PGWOGO) is proposed without sacrificing the accuracy. A grey wolf optimiser technique is hybridised with the genetic operators and the parallel phases are implemented in Quadro 4,000 graphics processing unit. A new crossover and mutation operator's namely horizontal crossover and local gaps shuffle mutation operator between aligned blocks are employed. The performance of proposed algorithm is evaluated using the cells update per second (CUPS) and compared with the state-of-the-art techniques. The results show that the proposed algorithm yields better alignment than other techniques.

关键词： graphical processing unit GPU alignment hybrid bioinspired grey wolf optimiser GWO genetic operators crossover mutation

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Current state of the art of regional hyperthermia treatment planning: a review

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RADIATION ONCOLOGY 2015年第1期10卷 1-14页

作者： Kok, H. P. Wust, P. Stauffer, P. R. Bardati, F. van Rhoon, G. C. Crezee, J. Univ Amsterdam Acad Med Ctr Dept Radiat Oncol Meibergdreef 9 NL-1105 AZ Amsterdam Netherlands Charite Dept Radiat Oncol D-13353 Berlin Germany Thomas Jefferson Univ Dept Radiat Oncol Philadelphia PA 19107 USA Univ Roma Tor Vergata Dept Civil Engn & Comp Sci Rome Italy Erasmus MC Canc Inst Dept Radiat Oncol Rotterdam Netherlands

Locoregional hyperthermia, i.e. increasing the tumor temperature to 40-45 degrees C using an external heating device, is a very effective radio and chemosensitizer, which significantly improves clinical outcome. There is a clear thermal dose-effect relation, but the pursued optimal thermal dose of 43 degrees C for 1 h can often not be realized due to treatment limiting hot spots in normal tissue. Modern heating devices have a large number of independent antennas, which provides flexible power steering to optimize tumor heating and minimize hot spots, but manual selection of optimal settings is difficult. Treatment planning is a very valuable tool to improve locoregional heating. This paper reviews the developments in treatment planning software for tissue segmentation, electromagnetic field calculations, thermal modeling and optimization techniques. Over the last decade, simulation tools have become more advanced. On-line use has become possible by implementing algorithms on the graphical processing unit, which allows real-time computations. The number of applications using treatment planning is increasing rapidly and moving on from retrospective analyses towards assisting prospective clinical treatment strategies. Some clinically relevant applications will be discussed.

关键词： graphical processing unit Treatment Planning System Specific Absorption Rate Tissue Segmentation Finite Difference Time Domain Method

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