Graphics processing unit utilization in circuit simulation

Nykypäivän grafiikkaprosessorit (GPU) koostuvat sadoista monisäikeisistä, moniytimisistä prosessoreista ja monimutkaisesta korkean kaistanleveyden muistiarkkitehtuurista. Tämän vuoksi niistä on tullut hyvä vaihtoehto nopeuttamaan rinnakkaistettua yleislaskentaa, jossa suuria datamääriä käsitellään samoilla funktioilla. Myös piirisimuloinnin alalla on esitelty menestyksellisiä GPU-laskennan sovellutuksia. Tämän opinnäytteen tavoitteena on tutkia GPU-laskennan mahdollisuuksia APLAC-piirisimulointiohjelmassa. Työssä esitellään myös diodimallin laskennan toteutus GPU:lla. Epälineaarinen diodimalli toteutettiin NVIDIAn CUDA-arkkitehtuurilla, joka on niin sanottu SIMT-arkkitehtuuri (single-instruction, multiple-thread) eli yksi käsky suoritetaan kerrallaan usealle säikeelle. CUDA-laite ohjelmoitiin CUDA C -ohjelmointirajapinnalla, joka on standardin C-kielen laajennus. Testitulokset paljastivat että diodin yksinkertaisesta epälineaarisuudesta johtuen sen laskenta on liian kevyt, jotta GPU:n tehokkuudesta olisi mitään nopeusetua. Vaadittavat muutokset piirianalyysin rakenteeseen sekä datan hallintaan johtivat marginaalisesti alkuperäistä pidempään kokonaissimulointiaikaan. Kun diodimallia monimutkaistetaan moninkertaistamalla sen laskenta, CUDA-toteutus on nopeampi kuin alkuperäinen malli. Tämä antaa karkean arvion siitä kuinka monimutkainen malli hyötyy GPU-laskennasta. Vaikka diodimalli ei ollutkaan nopeampi GPU:lla, tämä toteutus on hyvä perusta tuleville CUDA-sovelluksille APLACissa. Näistä seuraavana on huomattavasti monimutkaisempi BSIM3-transistorimallin laskenta, joka mitä todennäköisimmin hyötyy GPU:n laskentatehosta.Graphics processing units (GPU) of today include hundreds of multi-threaded, multicore processors and a complex, high-bandwidth memory architecture, making them a good alternative to speed up general-purpose parallel computation where large data quantities are processed with same functions. Some successful applications of GPU computation have also been introduced in the field of circuit simulation. The objective of this thesis is to examine the GPU's computing potential in the APLAC circuit simulation software. The realization of a diode model on a GPU device is also presented. The nonlinear diode model was implemented on NVIDIA's Compute Unified Device Architecture (CUDA), that is a single-instruction, multiple-thread (SIMT) architecture. A CUDA device was programmed using the CUDA C application programming interface, which is an extension of the standard C language. The test results revealed that due to the diode's simple nonlinearity, its evaluation is computationally too light to gain any speed benefit from the GPU's computation power. The required modifications to the circuit analysis structure and data handling resulted in a marginally longer total simulation time than initially. However, when the diode model is made more complex by multiplying its evaluation, the CUDA implementation is faster than the original model. This gives a rough estimate of how complex a model benefits from the GPU computation. Although, the diode model evaluation was not faster on the GPU, this implementation is a good foundation for future CUDA applications in APLAC. The next of these applications will be the computationally more complex BSIM3 transistor model, which will most likely benefit from the computing power of GPU devices

Image Re-ranking Acceleration On Gpus

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. 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