A model-based design flow for embedded vision applications on heterogeneous architectures

The ability to gather information from images is straightforward to human, and one of the principal input to understand external world. Computer vision (CV) is the process to extract such knowledge from the visual domain in an algorithmic fashion. The requested computational power to process these information is very high. Until recently, the only feasible way to meet non-functional requirements like performance was to develop custom hardware, which is costly, time-consuming and can not be reused in a general purpose. The recent introduction of low-power and low-cost heterogeneous embedded boards, in which CPUs are combine with heterogeneous accelerators like GPUs, DSPs and FPGAs, can combine the hardware efficiency needed for non-functional requirements with the flexibility of software development. Embedded vision is the term used to identify the application of the aforementioned CV algorithms applied in the embedded field, which usually requires to satisfy, other than functional requirements, also non-functional requirements such as real-time performance, power, and energy efficiency. Rapid prototyping, early algorithm parametrization, testing, and validation of complex embedded video applications for such heterogeneous architectures is a very challenging task. This thesis presents a comprehensive framework that: 1) Is based on a model-based paradigm. Differently from the standard approaches at the state of the art that require designers to manually model the algorithm in any programming language, the proposed approach allows for a rapid prototyping, algorithm validation and parametrization in a model-based design environment (i.e., Matlab/Simulink). The framework relies on a multi-level design and verification flow by which the high-level model is then semi-automatically refined towards the final automatic synthesis into the target hardware device. 2) Relies on a polyglot parallel programming model. The proposed model combines different programming languages and environments such as C/C++, OpenMP, PThreads, OpenVX, OpenCV, and CUDA to best exploit different levels of parallelism while guaranteeing a semi-automatic customization. 3) Optimizes the application performance and energy efficiency through a novel algorithm for the mapping and scheduling of the application 3 tasks on the heterogeneous computing elements of the device. Such an algorithm, called exclusive earliest finish time (XEFT), takes into consideration the possible multiple implementation of tasks for different computing elements (e.g., a task primitive for CPU and an equivalent parallel implementation for GPU). It introduces and takes advantage of the notion of exclusive overlap between primitives to improve the load balancing. This thesis is the result of three years of research activity, during which all the incremental steps made to compose the framework have been tested on real case studie

Toward High-Performance Computing and Big Data Analytics Convergence: The Case of Spark-DIY

Convergence between high-performance computing (HPC) and big data analytics (BDA) is currently an established research area that has spawned new opportunities for unifying the platform layer and data abstractions in these ecosystems. This work presents an architectural model that enables the interoperability of established BDA and HPC execution models, reflecting the key design features that interest both the HPC and BDA communities, and including an abstract data collection and operational model that generates a unified interface for hybrid applications. This architecture can be implemented in different ways depending on the process- and data-centric platforms of choice and the mechanisms put in place to effectively meet the requirements of the architecture. The Spark-DIY platform is introduced in the paper as a prototype implementation of the architecture proposed. It preserves the interfaces and execution environment of the popular BDA platform Apache Spark, making it compatible with any Spark-based application and tool, while providing efficient communication and kernel execution via DIY, a powerful communication pattern library built on top of MPI. Later, Spark-DIY is analyzed in terms of performance by building a representative use case from the hydrogeology domain, EnKF-HGS. This application is a clear example of how current HPC simulations are evolving toward hybrid HPC-BDA applications, integrating HPC simulations within a BDA environment.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness under Grant TIN2016-79637-P(toward Unification of HPC and Big Data Paradigms), in part by the Spanish Ministry of Education under Grant FPU15/00422 TrainingProgram for Academic and Teaching Staff Grant, in part by the Advanced Scientific Computing Research, Office of Science, U.S.Department of Energy, under Contract DE-AC02-06CH11357, and in part by the DOE with under Agreement DE-DC000122495,Program Manager Laura Biven

OpenCL Actors - Adding Data Parallelism to Actor-based Programming with CAF

The actor model of computation has been designed for a seamless support of concurrency and distribution. However, it remains unspecific about data parallel program flows, while available processing power of modern many core hardware such as graphics processing units (GPUs) or coprocessors increases the relevance of data parallelism for general-purpose computation. In this work, we introduce OpenCL-enabled actors to the C++ Actor Framework (CAF). This offers a high level interface for accessing any OpenCL device without leaving the actor paradigm. The new type of actor is integrated into the runtime environment of CAF and gives rise to transparent message passing in distributed systems on heterogeneous hardware. Following the actor logic in CAF, OpenCL kernels can be composed while encapsulated in C++ actors, hence operate in a multi-stage fashion on data resident at the GPU. Developers are thus enabled to build complex data parallel programs from primitives without leaving the actor paradigm, nor sacrificing performance. Our evaluations on commodity GPUs, an Nvidia TESLA, and an Intel PHI reveal the expected linear scaling behavior when offloading larger workloads. For sub-second duties, the efficiency of offloading was found to largely differ between devices. Moreover, our findings indicate a negligible overhead over programming with the native OpenCL API.Comment: 28 page

Reducing the Complexity of Heterogeneous Computing: A Unified Approach for Application Development and Runtime Optimization

Heterogeneous systems with accelerators promise considerable performance improvements at a lower cost than homogeneous CPU-only systems. However, to benefit from this potential, considerable work is required from developers to integrate them efficiently in an application. This work contributes a new framework implemented with an online-learning runtime system that simplifies development and makes applications more portable, efficient and reliable across different systems

Multi-GPU support on the marrow algorithmic skeleton framework

Dissertação para obtenção do Grau de Mestre em Engenharia InformáticaWith the proliferation of general purpose GPUs, workload parallelization and datatransfer optimization became an increasing concern. The natural evolution from using a single GPU, is multiplying the amount of available processors, presenting new challenges, as tuning the workload decompositions and load balancing, when dealing with heterogeneous systems. Higher-level programming is a very important asset in a multi-GPU environment, due to the complexity inherent to the currently used GPGPU APIs (OpenCL and CUDA), because of their low-level and code overhead. This can be obtained by introducing an abstraction layer, which has the advantage of enabling implicit optimizations and orchestrations such as transparent load balancing mechanism and reduced explicit code overhead. Algorithmic Skeletons, previously used in cluster environments, have recently been adapted to the GPGPU context. Skeletons abstract most sources of code overhead, by defining computation patterns of commonly used algorithms. The Marrow algorithmic skeleton library is one of these, taking advantage of the abstractions to automate the orchestration needed for an efficient GPU execution. This thesis proposes the extension of Marrow to leverage the use of algorithmic skeletons in the modular and efficient programming of multiple heterogeneous GPUs, within a single machine. We were able to achieve a good balance between simplicity of the programming model and performance, obtaining good scalability when using multiple GPUs, with an efficient load distribution, although at the price of some overhead when using a single-GPU.projects PTDC/EIA-EIA/102579/2008 and PTDC/EIA-EIA/111518/200

HPC algorithms for nonnegative decompositions

Muchos problemas procedentes de aplicaciones del mundo real pueden ser modelados como problemas matemáticos con magnitudes no negativas, y por tanto, las soluciones de estos problemas matemáticos solo tienen sentido si son no negativas. Estas magnitudes no negativas pueden ser, por ejemplo, las frecuencias en una señal sonora, las intensidades de los pixeles de una imagen, etc. Algunos de estos problemas pueden ser modelados utilizando un sistema de ecuaciones lineales sobredeterminado. Cuando la solución de dicho problema debe ser restringida a valores no negativos, aparece un problema llamado problema de mínimos cuadrados no negativos (NNLS por sus siglas en inglés). La solución de dicho problema tiene múltiples aplicaciones en ciencia e ingeniería. Otra descomposición no negativa importante es la Factorización de Matrices No negativas (NMF por sus siglas en inglés). La NMF es una herramienta muy popular utilizada en varios campos, como por ejemplo: clasificación de documentos, aprendizaje automático, análisis de imagen o separación de señales sonoras. Esta factorización intenta aproximar una matriz no negativa con el producto de dos matrices no negativas de menor tamaño, creando habitualmente representaciones por partes de los datos originales. Los algoritmos diseñados para calcular la solución de estos dos problemas no negativos tienen un elevado coste computacional, y debido a ese elevado coste, estas descomposiciones pueden beneficiarse mucho del uso de técnicas de Computación de Altas Prestaciones (HPC por sus siglas en inglés). Estos sistemas computacionales de altas prestaciones incluyen desde los modernos computadores multinucleo a lo último en aceleradores de calculo (Unidades de Procesamiento Gráfico (GPU), Intel Many Integrated Core (MIC), etc.). Para obtener el máximo rendimiento de estos sistemas, los desarrolladores deben utilizar tecnologías software tales como la programación paralela, la vectoración o el uso de librerías de computación altas prestaciones. A pesar de que existen diversos algoritmos para calcular la NMF y resolver el problema NNLS, no todos ellos disponen de una implementación paralela y eficiente. Además, es muy interesante reunir diversos algoritmos con propiedades diferentes en una sola librería computacional. Esta tesis presenta una librería computacional de altas prestaciones que contiene implementaciones paralelas y eficientes de los mejores algoritmos existentes actualmente para calcular la NMF. Además la tesis también incluye una comparación experimental entre las diferentes implementaciones presentadas. Esta librería centrada en el cálculo de la NMF soporta múltiples arquitecturas tales como CPUs multinucleo, GPUs e Intel MIC. El objetivo de esta librería es ofrecer un abanico de algoritmos eficientes para ayudar a científicos, ingenieros o cualquier tipo de profesionales que necesitan hacer uso de la NMF. Otro problema abordado en esta tesis es la actualización de las factorizaciones no negativas. El problema de la actualización se ha estudiado tanto para la solución del problema NNLS como para el calculo de la NMF. Existen problemas no negativos cuya solución es próxima a otros problemas que ya han sido resueltos, el problema de la actualización consiste en aprovechar la solución de un problema A que ya ha sido resuelto, para obtener la solución de un problema B cercano al problema A. Utilizando esta aproximación, el problema B puede ser resuelto más rápido que si se tuviera que resolver sin aprovechar la solución conocida del problema A. En esta tesis se presenta una metodología algorítmica para resolver ambos problemas de actualización: la actualización de la solución del problema NNLS y la actualización de la NMF. Además se presentan evaluaciones empíricas de las soluciones presentadas para ambos problemas. Los resultados de estas evaluaciones muestran que los algoritmos propuestos son más rápidos que resoMolts problemes procedents de aplicacions del mon real poden ser modelats com problemes matemàtics en magnituts no negatives, i per tant, les solucions de estos problemes matemàtics només tenen sentit si son no negatives. Estes magnituts no negatives poden ser, per eixemple, la concentració dels elements en un compost químic, les freqüències en una senyal sonora, les intensitats dels pixels de una image, etc. Alguns d'estos problemes poden ser modelats utilisant un sistema d'equacions llineals sobredeterminat. Quant la solució de este problema deu ser restringida a valors no negatius, apareix un problema nomenat problema de mínims quadrats no negatius (NNLS per les seues sigles en anglés). La solució de este problema te múltiples aplicacions en ciències i ingenieria. Un atra descomposició no negativa important es la Factorisació de Matrius No negatives(NMF per les seues sigles en anglés). La NMF es una ferramenta molt popular utilisada en diversos camps, com per eixemple: classificacio de documents, aprenentage automàtic, anàlisis de image o separació de senyals sonores. Esta factorisació intenta aproximar una matriu no negativa en el producte de dos matrius no negatives de menor tamany, creant habitualment representacions a parts de les dades originals. Els algoritmes dissenyats per a calcular la solució de estos dos problemes no negatius tenen un elevat cost computacional, i degut a este elevat cost, estes descomposicions poden beneficiar-se molt del us de tècniques de Computació de Altes Prestacions (HPC per les seues sigles en anglés). Estos sistemes de computació de altes prestacions inclouen des dels moderns computadors multinucli a lo últim en acceleradors de càlcul (Unitats de Processament Gràfic (GPU), Intel Many Core (MIC), etc.). Per a obtindre el màxim rendiment de estos sistemes, els desenrolladors deuen utilisar tecnologies software tals com la programació paralela, la vectorisació o el us de llibreries de computació de altes prestacions. A pesar de que existixen diversos algoritmes per a calcular la NMF i resoldre el problema NNLS, no tots ells disponen de una implementació paralela i eficient. Ademés, es molt interessant reunir diversos algoritmes en propietats diferents en una sola llibreria computacional. Esta tesis presenta una llibreria computacional de altes prestacions que conté implementacions paraleles i eficients dels millors algoritmes existents per a calcular la NMF. Ademés, la tesis també inclou una comparació experimental entre les diferents implementacions presentades. Esta llibreria centrada en el càlcul de la NMF soporta diverses arquitectures tals com CPUs multinucli, GPUs i Intel MIC. El objectiu de esta llibreria es oferir una varietat de algoritmes eficients per a ajudar a científics, ingeniers o qualsevol tipo de professionals que necessiten utilisar la NMF. Un atre problema abordat en esta tesis es la actualisació de les factorisacions no negatives. El problema de la actualisació se ha estudiat tant per a la solució del problema NNLS com per a el càlcul de la NMF. Existixen problemes no negatius la solució dels quals es pròxima a atres problemes no negatius que ya han sigut resolts, el problema de la actualisació consistix en aprofitar la solució de un problema A que ya ha sigut resolt, per a obtindre la solució de un problema B pròxim al problema A. Utilisant esta aproximació, el problema B pot ser resolt molt mes ràpidament que si tinguera que ser resolt des de 0 sense aprofitar la solució coneguda del problema A. En esta tesis es presenta una metodologia algorítmica per a resoldre els dos problemes de actualisació: la actualisació de la solució del problema NNLS i la actualisació de la NMF. Ademés es presenten evaluacions empíriques de les solucions presentades per als dos problemes. Els resultats de estes evaluacions mostren que els algoritmes proposts son més ràpits que resoldre el problema des de 0 en tots elsMany real world-problems can be modelled as mathematical problems with nonnegative magnitudes, and, therefore, the solutions of these problems are meaningful only if their values are nonnegative. Examples of these nonnegative magnitudes are the concentration of components in a chemical compound, frequencies in an audio signal, pixel intensities on an image, etc. Some of these problems can be modelled to an overdetermined system of linear equations. When the solution of this system of equations should be constrained to nonnegative values, a new problem arises. This problem is called the Nonnegative Least Squares (NNLS) problem, and its solution has multiple applications in science and engineering, especially for solving optimization problems with nonnegative restrictions. Another important nonnegativity constrained decomposition is the Nonnegative Matrix Factorization (NMF). The NMF is a very popular tool in many fields such as document clustering, data mining, machine learning, image analysis, chemical analysis, and audio source separation. This factorization tries to approximate a nonnegative data matrix with the product of two smaller nonnegative matrices, usually creating parts based representations of the original data. The algorithms that are designed to compute the solution of these two nonnegative problems have a high computational cost. Due to this high cost, these decompositions can benefit from the extra performance obtained using High Performance Computing (HPC) techniques. Nowadays, there are very powerful computational systems that offer high performance and can be used to solve extremely complex problems in science and engineering. From modern multicore CPUs to the newest computational accelerators (Graphics Processing Units(GPU), Intel Many Integrated Core(MIC), etc.), the performance of these systems keeps increasing continuously. To make the most of the hardware capabilities of these HPC systems, developers should use software technologies such as parallel programming, vectorization, or high performance computing libraries. While there are several algorithms for computing the NMF and for solving the NNLS problem, not all of them have an efficient parallel implementation available. Furthermore, it is very interesting to group several algorithms with different properties into a single computational library. This thesis presents a high-performance computational library with efficient parallel implementations of the best algorithms to compute the NMF in the current state of the art. In addition, an experimental comparison between the different implementations is presented. This library is focused on the computation of the NMF supporting multiple architectures like multicore CPUs, GPUs and Intel MIC. The goal of the library is to offer a full suit of algorithms to help researchers, engineers or professionals that need to use the NMF. Another problem that is dealt with in this thesis is the updating of nonnegative decompositions. The updating problem has been studied for both the solution of the NNLS problem and the NMF. Sometimes there are nonnegative problems that are close to other nonnegative problems that have already been solved. The updating problem tries to take advantage of the solution of a problem A, that has already been solved in order to obtain a solution of a new problem B, which is closely related to problem A. With this approach, problem B can be solved faster than solving it from scratch and not taking advantage of the already known solution of problem A. In this thesis, an algorithmic scheme is proposed for both the updating of the solution of NNLS problems and the updating of the NMF. Empirical evaluations for both updating problems are also presented. The results show that the proposed algorithms are faster than solving the problems from scratch in all of the tested cases.San Juan Sebastián, P. (2018). HPC algorithms for nonnegative decompositions [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11306

Generating and auto-tuning parallel stencil codes

In this thesis, we present a software framework, Patus, which generates high performance stencil codes for different types of hardware platforms, including current multicore CPU and graphics processing unit architectures. The ultimate goals of the framework are productivity, portability (of both the code and performance), and achieving a high performance on the target platform. A stencil computation updates every grid point in a structured grid based on the values of its neighboring points. This class of computations occurs frequently in scientific and general purpose computing (e.g., in partial differential equation solvers or in image processing), justifying the focus on this kind of computation. The proposed key ingredients to achieve the goals of productivity, portability, and performance are domain specific languages (DSLs) and the auto-tuning methodology. The Patus stencil specification DSL allows the programmer to express a stencil computation in a concise way independently of hardware architecture-specific details. Thus, it increases the programmer productivity by disburdening her or him of low level programming model issues and of manually applying hardware platform-specific code optimization techniques. The use of domain specific languages also implies code reusability: once implemented, the same stencil specification can be reused on different hardware platforms, i.e., the specification code is portable across hardware architectures. Constructing the language to be geared towards a special purpose makes it amenable to more aggressive optimizations and therefore to potentially higher performance. Auto-tuning provides performance and performance portability by automated adaptation of implementation-specific parameters to the characteristics of the hardware on which the code will run. By automating the process of parameter tuning — which essentially amounts to solving an integer programming problem in which the objective function is the number representing the code's performance as a function of the parameter configuration, — the system can also be used more productively than if the programmer had to fine-tune the code manually. We show performance results for a variety of stencils, for which Patus was used to generate the corresponding implementations. The selection includes stencils taken from two real-world applications: a simulation of the temperature within the human body during hyperthermia cancer treatment and a seismic application. These examples demonstrate the framework's flexibility and ability to produce high performance code