Man-leung Wong · 2005
Abstract- Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuits synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. In this paper, we propose to implement a parallel EA on consumer-level graphics cards. We perform experiments to compare our parallel EA with an ordinary EA and demonstrate that the former is much more effective than the latter. Since consumer-level graphics cards are available in ubiquitous personal computers and these computers are easy to use and manage, more people will be able to use our parallel algorithm to solve their problems encountered in real-world applications. 1
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Ventsislav Nikolov · Lecture notes in computer science · 2018
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The MIT Press eBooks · 2021
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Ankush Rai, R. Jagadeesh Kannan · Asian Journal of Pharmaceutical and Clinical Research · 2017
This research work presents a novel central processing unit-graphics processing unit (CPU-GPU) computing scheme for multiple object trackingduring a surveillance operation. This facilitates nonlinear computational jobs to avail completion of computation in minimal processing time for tracking function. The work is divided into two essential objectives. First is to dynamically divide the processing operations into parallel units, and second is to reduce the communication between CPU-GPU processing units.
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NTUR (臺灣機構典藏) · 2009
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Li‐Jen Chang, Kuo-Chun Hsu, P.-C. Li · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · 2009
Color Doppler ultrasound is a routinely used diagnostic tool for assessing blood flow information in real time. The required signal processing is computationally intensive, involving autocorrelation, linear filtering, median filtering, and thresholding. Because of the large amount of data and high computational requirement, color Doppler signal processing has been mainly implemented on custom-designed hardware, with software-based implementation--particularly on a general-purpose CPU--not being successful. In this paper, we describe the use of a graphics processing unit for implementing signal-processing algorithms for color Doppler ultrasound that achieves a frame rate of 160 fps for frames comprising 500 scan lines x 128 range samples, with each scan line being obtained from an ensemble size of 8 with an 8-tap FIR clutter filter.
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Song Jun Park, Dale R. Shires · 2010
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Delevacq, Audrey · theses.fr (ABES) · 2013
Plusieurs problèmes d'optimisation combinatoire sont dits NP-difficiles et ne peuvent être résolus de façon optimale par des algorithmes exacts. Les métaheuristiques ont prouvé qu'elles pouvaient être efficaces pour résoudre un grand nombre de ces problèmes en leur trouvant des solutions approchées en un temps raisonnable. Cependant, face à des instances de grande taille, elles ont besoin d'un temps de calcul et d'une quantité d'espace mémoire considérables pour être performantes dans l'exploration de l'espace de recherche. Par conséquent, l'intérêt voué à leur déploiement sur des architectures de calcul haute performance a augmenté durant ces dernières années. Les approches de parallélisation existantes suivent généralement les paradigmes de passage de messages ou de mémoire partagée qui conviennent aux architectures traditionnelles à base de microprocesseurs, aussi appelés CPU (Central Processing Unit).Cependant, la recherche évolue très rapidement dans le domaine du parallélisme et de nouvelles architectures émergent, notamment les accélérateurs matériels qui permettent de décharger le CPU de certaines de ses tâches. Parmi ceux-ci, les processeurs graphiques ou GPU (Graphics Processing Units) présentent une architecture massivement parallèle possédant un grand potentiel mais aussi de nouvelles difficultés d'algorithmique et de programmation. En effet, les modèles de parallélisation de métaheuristiques existants sont généralement inadaptés aux environnements de calcul de type GPU. Certains travaux ont d'ailleurs abordé ce sujet sans toutefois y apporter une vision globale et fondamentale.L'objectif général de cette thèse est de proposer un cadre de référence permettant l'implémentation efficace des métaheuristiques sur des architectures parallèles basées sur les GPU. Elle débute par un état de l'art décrivant les travaux existants sur la parallélisation GPU des métaheuristiques et les classifications générales des métaheuristiques parallèles. Une taxonomie originale est ensuite proposée afin de classifier les implémentations recensées et de formaliser les stratégies de parallélisation sur GPU dans un cadre méthodologique cohérent. Cette thèse vise également à valider cette taxonomie en exploitant ses principales composantes pour proposer des stratégies de parallélisation originales spécifiquement adaptées aux architectures GPU. Plusieurs implémentations performantes basées sur les métaheuristiques d'Optimisation par Colonie de Fourmis et de Recherche Locale Itérée sont ainsi proposées pour la résolution du problème du Voyageur de Commerce. Une étude expérimentale structurée et minutieuse est réalisée afin d'évaluer et de comparer la performance des approches autant au niveau de la qualité des solutions trouvées que de la réduction du temps de calcul.
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Hong Li, Allison M. Kolpas, Linda Petzold et al. · 2008
We consider an individual-based model for fish schooling which incorporates a tendency for each fish to align its position and orientation with an appropriate average of its neighbors ’ positions and orientations, plus a tendency for each fish to avoid collisions. To accurately determine statistical properties of the collective motion of fish whose dynamics are described by such a model, many realizations are typically required. This carries a very high computational cost. The current generation of graphics processing units is well-suited to this task. We describe our implementation, and present computational experiments illustrating the power of this technology for this important and challenging class of problems.
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WANG Jijun,CHENG Hua · SHILAP Revista de lepidopterología · 2017
In order to get the GPU power data quickly and accurately,this paper proposes a General Purpose Graphics Processing Unit(GPGPU) power estimation model based on hardware performance counting events.Through analysing power distribution during GPGPU program running,it selects a set of performance events which are closely related to application program running power.Then it figures out the relationship between hardware performance counting events and realtime power using Back Propagation Atificial Neural Network(BP-ANN).Finally,it builds a GPGPU power estimation model.Experimental results indicate that compared with the Multiple Linear Regression(MLR) power estimation model,the proposed model has higher estimation accuracy and versatility.
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Angger Abdul Razak, Adharul Muttaqin, Muhammad Aswin · Jurnal EECCIS (Electrics Electronics Communications Controls Informatics Systems) · 2019
Finite Difference Time Domain (FDTD) merupakan salah satu metode yang banyak digunakan untuk mengevaluasi dan mensimulasi gelombang elektromagnetik beserta interaksinya dengan material sekitarnya. Namun, FDTD juga dikenal dengan kebutuhan sumber daya komputer yang besar. Pada paper ini, FDTD yang pada umumnya dijalankan menggunakan komputasi Central Processing unit (CPU) akan dijalankan menggunakan komputasi Graphics Processing Unit (GPU) dan dievaluasi kelayakannya. Selain itu, perbandingan energi yang digunakan pada kedua metode kalkulasi tersebut juga akan dibandingkan sebagai target utama riset ini. Hasil menunjukkan jika komputasi GPU dapat menggunakan energi sekitar 10,8 kali lebih kecil dibandingkan komputasi CPU untuk menjalankankasus yang sama.
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Jeffrey D. Blanchard, Erik Opavsky, Emircan Uysaler · ACM Transactions on Parallel Computing · 2016
Extracting a set of multiple order statistics from a huge data set provides important information about the distribution of the values in the full set of data. This article introduces an algorithm, bucketMultiSelect, for simultaneously selecting multiple order statistics with a graphics processing unit (GPU). Typically, when a large set of order statistics is desired, the vector is sorted. When the sorted version of the vector is not needed, bucketMultiSelect significantly reduces computation time by eliminating a large portion of the unnecessary operations involved in sorting. For large vectors, bucketMultiSelect returns thousands of order statistics in less time than sorting the vector while typically using less memory. For vectors containing 2 28 values of type double, bucketMultiSelect selects the 101 percentile order statistics in less than 95ms and is more than 8× faster than sorting the vector with a GPU optimized merge sort.
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Ashok Dwarakinath · SUNY Digital Repository Support (State University of New York System) · 2008
Stony Brook University Libraries. \nSBU Graduate School in Computer Science. \nLawrence Martin (Dean of Graduate School), Professor Tzi-cker Chiueh, Thesis Advisor \nComputer Science Department, Professor Jennifer L.Wong \nComputer Science Department.
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Kailash W. Kalare, Mohammad S. Obaidat, Jitendra V. Tembhurne et al. · 2020
In this paper, we propose a work distribution strategy to implement DNA global sequence alignment (GSA). The proposed strategy is implemented on Compute Unified Device Architecture (CUDA) for large biological sequences. The main objective of this work is to minimize the execution time required for DNA global alignment of large biological sequences. The proposed approach covers both the memory optimizations and minimization of execution time. We considered the biological sequences of different size to fit into the global memory under CUDA. The efficient use of global memory and memory optimization dominate the results of execution time. The results demonstrate the significant higher speedup using CUDA.
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Yishin Lin · Zenodo (CERN European Organization for Nuclear Research) · 2017
<em>gpda</em> is an R package, conducting probability density approximation (PDA; Turner & Sederberg, 2012; Holmes, 2015). This package provides R functions and CUDA C API to harness the parallel computing power of graphics processing unit (GPU), making PDA computation efficient. PDA calculates likelihood even when their analytic functions are unavailable. It allows researchers to model computationally complex biological processes, which in the past could only be approached by overly simplified models. PDA is however computationally demanding. It requires a large number of Monte Carlo simulations to attain satisfactory approximation precision. Monte Carlo simulations add a heavy computational burden on every step of PDA algorithm.
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Igor Majdandzic, Christian Trefftz, Gregory S. Wolffe · 2008
A parallel algorithm to compute a discrete approximation to the Voronoi diagram is presented. The algorithm, which executes in single instruction multiple data (SIMD) mode, was implemented on a high-end graphics processing unit (GPU) using NVIDIApsilas compute unified device architecture (CUDA) development environment. The performance of the resulting code is investigated and presented, and a mathematical model is developed that predicts the performance of the algorithm.
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Abeer A. Mohamad Alshiha, Mohammed W. Al-Neama, Abdalrahman R. Qubaa · Indonesian Journal of Electrical Engineering and Computer Science · 2023
The expansion of biometric applications and databases is worrying. Processing extensive or sophisticated biometric data results in longer wait times, which might restrict application usefulness. This work focuses on accelerating the processing of biometric data and proposes a parallel method of data processing that exceeds the capabilities of a central processing unit (CPU). The combination of the graphics processing unit (GPU) and compute unified device architecture (CUDA) results in at least three times the processing speed of a published accurate and secure multimodal biometric system. The GPU-assisted approach beats the CPU-only implementation when saturating the CPU-only performance with more people than the available thread count. The GPU-assisted solution is also proven to have the same accuracy as the original system, indicating accuracy and processing performance improvements in the demanding big data environment.
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Gustaf Hendeby, Jeroen D. Hol, Rickard Karlsson et al. · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2007
Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007
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Inyong Shin, Yo-Sung Ho · Journal of Broadcast Engineering · 2011
양안식 3차원 방송의 경우 좌우 두 시점에 해당하는 영상을 동시에 전송해야 하기 때문에 전송 대역폭의 부담이 매우 크다. 이러한 부담을 줄이기 위해 좌우 시점의 두 영상을 전송하는 대신에 좌영상과 이에 해당하는 깊이맵을 부호화하여 전송하는 방법이 있다. 이러한 3차원 방송 시스템의 수신단에서는 좌영상과 깊이맵을 복호한 뒤에 우영상을 만들어 좌우 영상을 실시간으로 출력한다. 본 논문에서는 좌영상과 깊이맵을 이용하여 가상시점 영상을 생성할 때 생기는 빈 공간을 효율적으로 채우는 기법을 제안하고, 전 과정의 실시간 처리를 위해 이를 GPU상에서 병렬로 처리되도록 구현했다. 그 결과 효과적으로 홀 채움을 수행하면서 CPU 대비 15배 이상 빠르게 양안식 영상을 생성할 수 있었다. In this paper, we propose a fast depth-image-based rendering method to generate a virtual view image in real-time using a graphic processor unit (GPU) for a 3D broadcasting system. Before the transmission, we encode the input 2D+depth video using the H.264 coding standard. At the receiver, we decode the received bitstream and generate a stereo video using a GPU which can compute in parallel. In this paper, we apply a simple and efficient hole filling method to reduce the decoder complexity and reduce hole filling errors. Besides, we design a vertical parallel structure for a forward mapping process to take advantage of the single instruction multiple thread structure of GPU. We also utilize high speed GPU memories to boost the computation speed. As a result, we can generate virtual view images 15 times faster than the case of CPU-based processing.
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Gašper Forjanič · 2010
Over the past few years, we have seen an exponential performance boost of the graphics processing unit (GPU) compared to the central processing unit (CPU). Reason for that are larger and more detailed textures, photorealistic effects and high display resolutions, which require parallel processing of large amounts of data. This caused the GPU to evolve into the general purpose processor (GPP). We took a look at the GPE architecture on the latest and previous generations of NVidia GPUs and the multi-GPU solution for linking two or more graphics cards (Scalable Link Interface - SLI). As an example of GPU performance, we pointed out hardware anti-aliasing technique, which is one of the most processor demanding graphics operations.
We examined the CUDA programming model and used the acquired knowledge to write an example of a program using the CUDA model. CUDA is based on programming language C / C++, so the learning curve for experienced C programmer is quick and efficient. There may be difficulties with memory optimizations and with finding the best suitable number of blocks and threads.
The purpose of this dissertation is to test the GPU performance within the CPU application domain using benchmark applications on Windows and Linux operating system. The test equipment was based on three Intel CPUs (Q6600, i7 920 and i7 980X) and two NVidia GPUs with low-end GeForce 8400GS and high-end GeForce GTX 295.
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