PyCUDA and PyOpenCL: A Scripting-Based Approach to GPU Run-Time Code Generation

High-performance computing has recently seen a surge of interest in heterogeneous systems, with an emphasis on modern Graphics Processing Units (GPUs). These devices offer tremendous potential for performance and efficiency in important large-scale applications of computational science. However, exploiting this potential can be challenging, as one must adapt to the specialized and rapidly evolving computing environment currently exhibited by GPUs. One way of addressing this challenge is to embrace better techniques and develop tools tailored to their needs. This article presents one simple technique, GPU run-time code generation (RTCG), along with PyCUDA and PyOpenCL, two open-source toolkits that support this technique. In introducing PyCUDA and PyOpenCL, this article proposes the combination of a dynamic, high-level scripting language with the massive performance of a GPU as a compelling two-tiered computing platform, potentially offering significant performance and productivity advantages over conventional single-tier, static systems. The concept of RTCG is simple and easily implemented using existing, robust infrastructure. Nonetheless it is powerful enough to support (and encourage) the creation of custom application-specific tools by its users. The premise of the paper is illustrated by a wide range of examples where the technique has been applied with considerable success.Comment: Submitted to Parallel Computing, Elsevie

Fast signal processing

Zvětšující se množství dat v moderním zpracování obrazu vyžaduje nový postupy v psaní algoritmů. Největší překážkou pro úspěšné zrychlení algoritmu je paralelizace a následná optimalizace. Programy jako CUDA a OpenCL s modifikovaným programovacím jazykem a rozhraním pomáhají s tímto problémem a otevírají paralelní zpracování širšímu okruhu lidí. V této práci zabývám základy zpracování obrazu a tomu jak paralelizace algoritmů může urychlit zpracování obrazu.An increasing amount of data in modern image processing requires a new approach in algorithms. The biggest obstacle for successful speed up of an algorithm is parallelization and subsequent optimization. Architectures like CUDA and OpenCL with modified programing languages and interfaces help to overcome this obstacle and bring parallel computing to a broader audience. In this paper I take a look at basics of image processing and how parallelization can speed up the algorithms in image processing.

PiCo: A Domain-Specific Language for Data Analytics Pipelines

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models—for which only informal (and often confusing) semantics is generally provided—all share a common under- lying model, namely, the Dataflow model. Using this model as a starting point, it is possible to categorize and analyze almost all aspects about Big Data analytics tools from a high level perspective. This analysis can be considered as a first step toward a formal model to be exploited in the design of a (new) framework for Big Data analytics. By putting clear separations between all levels of abstraction (i.e., from the runtime to the user API), it is easier for a programmer or software designer to avoid mixing low level with high level aspects, as we are often used to see in state-of-the-art Big Data analytics frameworks. From the user-level perspective, we think that a clearer and simple semantics is preferable, together with a strong separation of concerns. For this reason, we use the Dataflow model as a starting point to build a programming environment with a simplified programming model implemented as a Domain-Specific Language, that is on top of a stack of layers that build a prototypical framework for Big Data analytics. The contribution of this thesis is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm, Google Dataflow), thus making it easier to understand high-level data-processing applications written in such frameworks. As result of this analysis, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level. Second, we propose a programming environment based on such layered model in the form of a Domain-Specific Language (DSL) for processing data collections, called PiCo (Pipeline Composition). The main entity of this programming model is the Pipeline, basically a DAG-composition of processing elements. This model is intended to give the user an unique interface for both stream and batch processing, hiding completely data management and focusing only on operations, which are represented by Pipeline stages. Our DSL will be built on top of the FastFlow library, exploiting both shared and distributed parallelism, and implemented in C++11/14 with the aim of porting C++ into the Big Data world

GPGPU application in fusion science

GPGPUs have firmly earned their reputation in HPC (High Performance Computing) as hardware for massively parallel computation. However their application in fusion science is quite marginal and not considered a mainstream approach to numerical problems. Computation advances have increased immensely over the last decade and continue to accelerate. GPGPU boards were always an alternative and exotic approach to problem solving and scientific programming, which was cultivated only by enthusiasts and specialized programmers. Today it is about 10 years, since the first fully programmable GPUs appeared on the market. And due to exponential growth in processing power over the years GPGPUs are not the alternative choice any more, but they became the main choice for big problem solving. Originally developed for and dominating in fields such as image and media processing, image rendering, video encoding/decoding, image scaling, stereo vision and pattern recognition GPGPUs are also becoming mainstream computation platforms in scientific fields such as signal processing, physics, finance and biology. This PhD contains solutions and approaches to two relevant problems for fusion and plasma science using GPGPU processing. First problem belongs to the realms of plasma and accelerator physics. I will present number of plasma simulations built on a PIC (Particle In Cell) method such as plasma sheath simulation, electron beam simulation, negative ion beam simulation and space charge compensation simulation. Second problem belongs to the realms of tomography and real-time control. I will present number of simulated tomographic plasma reconstructions of Fourier-Bessel type and their analysis all in real-time oriented approach, i.e. GPGPU based implementations are integrated into MARTe environment. MARTe is a framework for real-time application developed at JET (Joint European Torus) and used in several european fusion labs. These two sets of problems represent a complete spectrum of GPGPU operation capabilities. PIC based problems are large complex simulations operated as batch processes, which do not have a time constraint and operate on huge amounts of memory. While tomographic plasma reconstructions are online (realtime) processes, which have a strict latency/time constraints suggested by the time scales of real-time control and operate on relatively small amounts of memory. Such a variety of problems covers a very broad range of disciplines and fields of science: such as plasma physics, NBI (Neutral Beam Injector) physics, tokamak physics, parallel computing, iterative/direct matrix solvers, PIC method, tomography and so on. PhD thesis also includes an extended performance analysis of Nvidia GPU cards considering the applicability to the real-time control and real-time performance. In order to approach the aforementioned problems I as a PhD candidate had to gain knowledge in those relevant fields and build a vast range of practical skills such as: parallel/sequential CPU programming, GPU programming, MARTe programming, MatLab programming, IDL programming and Python programming

Efficient Algorithms for Large-Scale Image Analysis

This work develops highly efficient algorithms for analyzing large images. Applications include object-based change detection and screening. The algorithms are 10-100 times as fast as existing software, sometimes even outperforming FGPA/GPU hardware, because they are designed to suit the computer architecture. This thesis describes the implementation details and the underlying algorithm engineering methodology, so that both may also be applied to other applications

Resource efficient processing and communication in sensor/actuator environments

The future of computer systems will not be dominated by personal computer like hardware platforms but by embedded and cyber-physical systems assisting humans in a hidden but omnipresent manner. These pervasive computing devices can, for example, be utilized in the home automation sector to create sensor/ actuator networks supporting the inhabitants of a house in everyday life. The efficient usage of resources is an important topic at design time and operation time of mobile embedded and cyber-physical systems. Therefore, this thesis presents methods which allow an efficient use of energy and processing resources in sensor/actuator networks. These networks comprise different nodes cooperating for a “smart” joint control function. Sensor/actuator nodes are typical cyber-physical systems comprising sensors/actuators and processing and communication components. Processing components of today’s sensor nodes can comprise many-core chips. This thesis introduces new methods for optimizing the code and the application mapping of the aforementioned systems and presents novel results with regard to design space explorations for energy-efficient and embedded many-core systems. The considered many-core systems are graphics processing units. The application code for these graphics processing units is optimized for a particular platform variant with the objectives of minimal energy consumption and/or of minimal runtime. These two objectives are targeted with the utilization of multi-objective optimization techniques. The mapping optimizations are realized by means of multi-objective design space explorations. Furthermore, this thesis introduces new techniques and functions for a resource-efficient middleware design employing service-oriented architectures. Therefore, a service-oriented architecture based middleware framework is presented which comprises a lightweight service orchestration. In addition to that, a flexible resource management mechanism will be introduced. This resource management adapts resource utilization and services to an environmental context and provides methods to reduce the energy consumption of sensor nodes

Parallelization and improvement of beamforming process in synthetic aperture systems for real-time ultrasonic image generation

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 9-02-2016La ecografía es hoy en día uno de los métodos de visualización más populares para examinar el interior de cuerpos opacos. Su aplicación es especialmente significativa tanto en el campo del diagnóstico médico como en las aplicaciones de evaluación no destructiva en el ámbito industrial, donde se evalúa la integridad de un componente o una estructura. El desarrollo de sistemas ecográficos de alta calidad y con buenas prestaciones se basa en el empleo de sistemas multisensoriales conocidos como arrays que pueden estar compuestos por varias decenas de elementos. El desarrollo de estos dispositivos tiene asociada una elevada complejidad, tanto por el número de sensores y la electrónica necesaria para la adquisición paralela de señales, como por la etapa de procesamiento de los datos adquiridos que debe operar en tiempo real. Esta etapa de procesamiento de señal trabaja con un elevado flujo de datos en paralelo y desarrolla, además de la composición de imagen, otras sofisticadas técnicas de medidas sobre los datos (medida de elasticidad, flujo, etc). En este sentido, el desarrollo de nuevos sistemas de imagen con mayores prestaciones (resolución, rango dinámico, imagen 3D, etc) está fuertemente limitado por el número de canales en la apertura del array. Mientras algunos estudios se han centrado en la reducción activa de sensores (sparse arrays como ejemplo), otros se han centrado en analizar diferentes estrategias de adquisiciónn que, operando con un número reducido de canales electrónicos en paralelo, sean capaz por multiplexación emular el funcionamiento de una apertura plena. A estas últimas técnicas se las agrupa mediante el concepto de Técnicas de Apertura Sintética (SAFT). Su interés radica en que no solo son capaces de reducir los requerimientos hardware del sistema (bajo consumo, portabilidad, coste, etc) sino que además permiten dentro de cierto compromiso la mejora de la calidad de imagen respecto a los sistemas convencionales...Ultrasound is nowadays one of the most popular visualization methods to examine the interior of opaque objects. Its application is particularly significant in the field of medical diagnosis as well as non-destructive evaluation applications in industry. The development of high performance ultrasound imaging systems is based on the use of multisensory systems known as arrays, which may be composed by dozens of elements. The development of these devices has associated a high complexity, due to the number of sensors and electronics needed for the parallel acquisition of signals, and for the processing stage of the acquired data which must operate on real-time. This signal processing stage works with a high data flow in parallel and develops, besides the image composition, other sophisticated measure techniques (measure of elasticity, flow, etc). In this sense, the development of new imaging systems with higher performance (resolution, dynamic range, 3D imaging, etc) is strongly limited by the number of channels in array’s aperture. While some studies have been focused on the reduction of active sensors (i.e. sparse arrays), others have been centered on analysing different acquisition strategies which, operating with reduced number of electronic channels in parallel, are able to emulate by multiplexing the behavior of a full aperture. These latest techniques are grouped under the term known as Synthetic Aperture Techniques (SAFT). Their interest is that they are able to reduce hardware requirements (low power, portability, cost, etc) and also allow to improve the image quality over conventional systems...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

計算機支援によるペプチド設計の理論と応用

学位の種別: 課程博士審査委員会委員 : (主査)東京大学客員准教授 富井 健太郎, 東京大学教授 菅野 純夫, 東京大学教授 浅井 潔, 東京大学准教授 木立 尚孝, 東京大学客員准教授 KamY. Zhang, 東京大学客員教授 泰地 真弘人University of Tokyo(東京大学