9 research outputs found

    Performance-aware component composition for GPU-based systems

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    Programming Languages for Data-Intensive HPC Applications: a Systematic Mapping Study

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    A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006–2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.Additional co-authors: Sabri Pllana, Ana Respício, José Simão, Luís Veiga, Ari Vis

    Parallel source code transformation techniques using design patterns

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    Mención Internacional en el título de doctorIn recent years, the traditional approaches for improving performance, such as increasing the clock frequency, has come to a dead-end. To tackle this issue, parallel architectures, such as multi-/many-core processors, have been envisioned to increase the performance by providing greater processing capabilities. However, programming efficiently for this architectures demands big efforts in order to transform sequential applications into parallel and to optimize such applications. Compared to sequential programming, designing and implementing parallel applications for operating on modern hardware poses a number of new challenges to developers such as data races, deadlocks, load imbalance, etc. To pave the way, parallel design patterns provide a way to encapsulate algorithmic aspects, allowing users to implement robust, readable and portable solutions with such high-level abstractions. Basically, these patterns instantiate parallelism while hiding away the complexity of concurrency mechanisms, such as thread management, synchronizations or data sharing. Nonetheless, frameworks following this philosophy does not share the same interface and users require understanding different libraries, and their capabilities, not only to decide which fits best for their purposes but also to properly leverage them. Furthermore, in order to parallelize these applications, it is necessary to analyze the sequential code in order to detect the regions of code that can be parallelized that is a time consuming and complex task. Additionally, different libraries targeted to specific devices provide some algorithms implementations that are already parallel and highly-tuned. In these situations, it is also necessary to analyze and determine which routine implementation is the most suitable for a given problem. To tackle these issues, this thesis aims at simplifying and minimizing the necessary efforts to transform sequential applications into parallel. This way, resulting codes will improve their performance by fully exploiting the available resources while the development efforts will be considerably reduced. Basically, in this thesis, we contribute with the following. First, we propose a technique to detect potential parallel patterns in sequential code. Second, we provide a novel generic C++ interface for parallel patterns which acts as a switch among existing frameworks. Third, we implement a framework that is able to transform sequential code into parallel using the proposed pattern discovery technique and pattern interface. Finally, we propose mechanisms that are able to select the most suitable device and routine implementation to solve a given problem based on previous performance information. The evaluation demonstrates that using the proposed techniques can minimize the refactoring and optimization time while improving the performance of the resulting applications with respect to the original code.En los últimos años, las técnicas tradicionales para mejorar el rendimiento, como es el caso del incremento de la frecuencia de reloj, han llegado a sus límites. Con el fin de seguir mejorando el rendimiento, se han desarrollado las arquitecturas paralelas, las cuales proporcionan un incremento del rendimiento al estar provistas de mayores capacidades de procesamiento. Sin embargo, programar de forma eficiente para estas arquitecturas requieren de grandes esfuerzos por parte de los desarrolladores. Comparado con la programación secuencial, diseñar e implementar aplicaciones paralelas enfocadas a trabajar en estas arquitecturas presentan una gran cantidad de dificultades como son las condiciones de carrera, los deadlocks o el incorrecto balanceo de la carga. En este sentido, los patrones paralelos son una forma de encapsular aspectos algorítmicos de las aplicaciones permitiendo el desarrollo de soluciones robustas, portables y legibles gracias a las abstracciones de alto nivel. En general, estos patrones son capaces de proporcionar el paralelismo a la vez que ocultan las complejidades derivadas de los mecanismos de control de concurrencia necesarios como el manejo de los hilos, las sincronizaciones o la compartición de datos. No obstante, los diferentes frameworks que siguen esta filosofía no comparten una única interfaz lo que conlleva que los usuarios deban conocer múltiples bibliotecas y sus capacidades, con el fin de decidir cuál de ellos es mejor para una situación concreta y como usarlos de forma eficiente. Además, con el fin de paralelizar aplicaciones existentes, es necesario analizar e identificar las regiones del código que pueden ser paralelizadas, lo cual es una tarea ardua y compleja. Además, algunos algoritmos ya se encuentran implementados en paralelo y optimizados para arquitecturas concretas en diversas bibliotecas. Esto da lugar a que sea necesario analizar y determinar que implementación concreta es la más adecuada para solucionar un problema dado. Para paliar estas situaciones, está tesis busca simplificar y minimizar el esfuerzo necesario para transformar aplicaciones secuenciales en paralelas. De esta forma, los códigos resultantes serán capaces de explotar los recursos disponibles a la vez que se reduce considerablemente el esfuerzo de desarrollo necesario. En general, esta tesis contribuye con lo siguiente. En primer lugar, se propone una técnica de detección de patrones paralelos en códigos secuenciales. En segundo lugar, se presenta una interfaz genérica de patrones paralelos para C++ que permite seleccionar la implementación de dichos patrones proporcionada por frameworks ya existentes. En tercer lugar, se introduce un framework de transformación de código secuencial a paralelo que hace uso de las técnicas de detección de patrones y la interfaz presentadas. Finalmente, se proponen mecanismos capaces de seleccionar la implementación más adecuada para solucionar un problema concreto basándose en el rendimiento obtenido en ejecuciones previas. Gracias a la evaluación realizada se ha podido demostrar que uso de las técnicas presentadas pueden minimizar el tiempo necesario para transformar y optimizar el código a la vez que mejora el rendimiento de las aplicaciones transformadas.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: David Expósito Singh.- Secretario: Rafael Asenjo Plaza.- Vocal: Marco Aldinucc

    Structured parallelism discovery with hybrid static-dynamic analysis and evaluation technique

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    Parallel computer architectures have dominated the computing landscape for the past two decades; a trend that is only expected to continue and intensify, with increasing specialization and heterogeneity. This creates huge pressure across the software stack to produce programming languages, libraries, frameworks and tools which will efficiently exploit the capabilities of parallel computers, not only for new software, but also revitalizing existing sequential code. Automatic parallelization, despite decades of research, has had limited success in transforming sequential software to take advantage of efficient parallel execution. This thesis investigates three approaches that use commutativity analysis as the enabler for parallelization. This has the potential to overcome limitations of traditional techniques. We introduce the concept of liveness-based commutativity for sequential loops. We examine the use of a practical analysis utilizing liveness-based commutativity in a symbolic execution framework. Symbolic execution represents input values as groups of constraints, consequently deriving the output as a function of the input and enabling the identification of further program properties. We employ this feature to develop an analysis and discern commutativity properties between loop iterations. We study the application of this approach on loops taken from real-world programs in the OLDEN and NAS Parallel Benchmark (NPB) suites, and identify its limitations and related overheads. Informed by these findings, we develop Dynamic Commutativity Analysis (DCA), a new technique that leverages profiling information from program execution with specific input sets. Using profiling information, we track liveness information and detect loop commutativity by examining the code’s live-out values. We evaluate DCA against almost 1400 loops of the NPB suite, discovering 86% of them as parallelizable. Comparing our results against dependence-based methods, we match the detection efficacy of two dynamic and outperform three static approaches, respectively. Additionally, DCA is able to automatically detect parallelism in loops which iterate over Pointer-Linked Data Structures (PLDSs), taken from wide range of benchmarks used in the literature, where all other techniques we considered failed. Parallelizing the discovered loops, our methodology achieves an average speedup of 3.6× across NPB (and up to 55×) and up to 36.9× for the PLDS-based loops on a 72-core host. We also demonstrate that our methodology, despite relying on specific input values for profiling each program, is able to correctly identify parallelism that is valid for all potential input sets. Lastly, we develop a methodology to utilize liveness-based commutativity, as implemented in DCA, to detect latent loop parallelism in the shape of patterns. Our approach applies a series of transformations which subsequently enable multiple applications of DCA over the generated multi-loop code section and match its loop commutativity outcomes against the expected criteria for each pattern. Applying our methodology on sets of sequential loops, we are able to identify well-known parallel patterns (i.e., maps, reduction and scans). This extends the scope of parallelism detection to loops, such as those performing scan operations, which cannot be determined as parallelizable by simply evaluating liveness-based commutativity conditions on their original form

    Optimización del rendimiento y la eficiencia energética en sistemas masivamente paralelos

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    RESUMEN Los sistemas heterogéneos son cada vez más relevantes, debido a sus capacidades de rendimiento y eficiencia energética, estando presentes en todo tipo de plataformas de cómputo, desde dispositivos embebidos y servidores, hasta nodos HPC de grandes centros de datos. Su complejidad hace que sean habitualmente usados bajo el paradigma de tareas y el modelo de programación host-device. Esto penaliza fuertemente el aprovechamiento de los aceleradores y el consumo energético del sistema, además de dificultar la adaptación de las aplicaciones. La co-ejecución permite que todos los dispositivos cooperen para computar el mismo problema, consumiendo menos tiempo y energía. No obstante, los programadores deben encargarse de toda la gestión de los dispositivos, la distribución de la carga y la portabilidad del código entre sistemas, complicando notablemente su programación. Esta tesis ofrece contribuciones para mejorar el rendimiento y la eficiencia energética en estos sistemas masivamente paralelos. Se realizan propuestas que abordan objetivos generalmente contrapuestos: se mejora la usabilidad y la programabilidad, a la vez que se garantiza una mayor abstracción y extensibilidad del sistema, y al mismo tiempo se aumenta el rendimiento, la escalabilidad y la eficiencia energética. Para ello, se proponen dos motores de ejecución con enfoques completamente distintos. EngineCL, centrado en OpenCL y con una API de alto nivel, favorece la máxima compatibilidad entre todo tipo de dispositivos y proporciona un sistema modular extensible. Su versatilidad permite adaptarlo a entornos para los que no fue concebido, como aplicaciones con ejecuciones restringidas por tiempo o simuladores HPC de dinámica molecular, como el utilizado en un centro de investigación internacional. Considerando las tendencias industriales y enfatizando la aplicabilidad profesional, CoexecutorRuntime proporciona un sistema flexible centrado en C++/SYCL que dota de soporte a la co-ejecución a la tecnología oneAPI. Este runtime acerca a los programadores al dominio del problema, posibilitando la explotación de estrategias dinámicas adaptativas que mejoran la eficiencia en todo tipo de aplicaciones.ABSTRACT Heterogeneous systems are becoming increasingly relevant, due to their performance and energy efficiency capabilities, being present in all types of computing platforms, from embedded devices and servers to HPC nodes in large data centers. Their complexity implies that they are usually used under the task paradigm and the host-device programming model. This strongly penalizes accelerator utilization and system energy consumption, as well as making it difficult to adapt applications. Co-execution allows all devices to simultaneously compute the same problem, cooperating to consume less time and energy. However, programmers must handle all device management, workload distribution and code portability between systems, significantly complicating their programming. This thesis offers contributions to improve performance and energy efficiency in these massively parallel systems. The proposals address the following generally conflicting objectives: usability and programmability are improved, while ensuring enhanced system abstraction and extensibility, and at the same time performance, scalability and energy efficiency are increased. To achieve this, two runtime systems with completely different approaches are proposed. EngineCL, focused on OpenCL and with a high-level API, provides an extensible modular system and favors maximum compatibility between all types of devices. Its versatility allows it to be adapted to environments for which it was not originally designed, including applications with time-constrained executions or molecular dynamics HPC simulators, such as the one used in an international research center. Considering industrial trends and emphasizing professional applicability, CoexecutorRuntime provides a flexible C++/SYCL-based system that provides co-execution support for oneAPI technology. This runtime brings programmers closer to the problem domain, enabling the exploitation of dynamic adaptive strategies that improve efficiency in all types of applications.Funding: This PhD has been supported by the Spanish Ministry of Education (FPU16/03299 grant), the Spanish Science and Technology Commission under contracts TIN2016-76635-C2-2-R and PID2019-105660RB-C22. This work has also been partially supported by the Mont-Blanc 3: European Scalable and Power Efficient HPC Platform based on Low-Power Embedded Technology project (G.A. No. 671697) from the European Union’s Horizon 2020 Research and Innovation Programme (H2020 Programme). Some activities have also been funded by the Spanish Science and Technology Commission under contract TIN2016-81840-REDT (CAPAP-H6 network). The Integration II: Hybrid programming models of Chapter 4 has been partially performed under the Project HPC-EUROPA3 (INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 Programme. In particular, the author gratefully acknowledges the support of the SPMT Department of the High Performance Computing Center Stuttgart (HLRS)

    Code generation for 3D partial differential equation models from a high-level functional intermediate language

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    Partial Differential Equation (PDE) modelling is an important tool in scientific domains for bridging theory with reality; however, they can be complex to program and even more difficult to abstract. The evolving parallel computing landscape is also making it increasingly difficult to write and maintain codes (such as PDE models) which retain performance across different parallel platforms. Computational scientists should be able to focus on their science instead of also having to become high performance computing experts in order to take advantage of faster parallel hardware. Current methods targeting this problem either concentrate on very niche applications, are too simplistic for real world problems or are too low-level to be easily programmable. Domain Specific Languages (DSLs) are a popular approach, but they have two opposing goals: improving programmability, while also providing high performance. This thesis presents a solution for developing performance portable 3D PDE models, using room acoustics simulations as a case study, by raising the abstraction level in the existing hardware-agnostic, intermediary language LIFT. This functional language and compiler is designed for DSLs to compile into and provides a separation of concerns for developing parallel applications. This separation enables DSL writers to focus on developing high-level abstractions providing productivity to the user, while LIFT turns the intermediary parallel representation these abstractions compile down to into hardware-optimised code. A suite of composable, algorithmic primitives enables LIFT to reuse functionality across domains and an exploratory search space provides a way to find the best optimisations for a given platform. As this thesis shows, room acoustic simulations are expressible in LIFT with only a few small changes to the framework. These expressions are able to achieve comparable or better performance to original hand-written benchmarks. Furthermore, such expressions enable room acoustics models to run across multiple platforms and easily swap in optimisations. Being able to test out what optimisations give the best performance for a given platform — without rewriting or retuning — allows computational scientists to focus on their own work. Optimisations previously inaccessible in LIFT are developed that target 3D stencils generally, including 3D PDE models. In particular, 2.5D Tiling and compiler passes to inline private arrays and structs are added to the LIFT ecosystem, giving high performance to various 3D stencil codes. The 2.5D Tiling optimisation is coded functionally for the first time in LIFT and is selected automatically by additional rewrite rules. These rewrite rules, such as the one for 2.5D Tiling, are explored in a search space to find the best set of optimisations for an application on a given platform. Building on previous work, LIFT is extended to enable complex boundary conditions and room shapes for room acoustics models. This is the first intermediate representation in a high-level code generator to do so. Additionally, it is also the first high-level framework to support frequency-dependent boundary handling for room acoustics simulations. Combined, these contributions show that high-level abstractions for 3D PDE models are possible, enabling computational scientists to optimise and parallelise their codes more easily across different parallel platforms

    High-Performance Modelling and Simulation for Big Data Applications

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    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

    High-Performance Modelling and Simulation for Big Data Applications

    Get PDF
    This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

    Linguagens para a Computação de Alto Desempenho, utilizadas no processamento de Big Data: Um Estudo de Mapeamento Sistemático

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    Big Data são conjuntos de informação de alto Volume, Velocidade e/ou Variedade que exigem formas inovadoras e económicas de processamento, que permitem uma melhor percepção, tomada de decisões e automação de processos. Desde 2002, a taxa de melhoria do desempenho em processadores simples diminuiu bruscamente. A fim de aumentar o poder dos processadores, foram utilizados múltiplos cores, em paralelo, num único chip. Para conseguir beneficiar deste tipo de arquiteturas, é necessário reescrever os programas sequenciais. O objetivo da Computação de Alto Desempenho (CAD) é estudar as metodologias e técnicas que permitem a exploração destas arquiteturas. O desafio é a necessidade de combinar o desenvolvimento de Software para a CAD com a gestão e análise de Big Data. Quando a computação paralela e distribuída é obrigatória, o código torna-se mais difícil. Para tal, é necessário saber quais são as linguagens a utilizar para facilitar essa tarefa. Pelo facto da literatura existente sobre o tópico da CAD se encontrar muito dispersa, foi conduzido um Estudo de Mapeamento Sistemático (EMS), que agrega caraterísticas sobre as diferentes linguagens encontradas (categoria; natureza; perfis de utilizador típicos; eficácia; tipos de artigos publicados na área), no processamento de Big Data, auxiliando estudantes, investigadores, ou outros profissionais que necessitem de uma introdução ou uma visão panorâmica sobre este tema. A pesquisa de artigos foi efetuada numa busca automatizada, baseada em palavraschave, nas bases de dados de 8 bibliotecas digitais selecionadas. Este processo resultou numa amostra inicial de 420 artigos, que foi reduzida a 152 artigos, publicados entre Janeiro de 2006 e Março de 2018. A análise manual desses artigos permitiu-nos identificar 26 linguagens em 33 publicações incluídas. Sumarizei e comparei as informações com as opiniões de profissionais. Os resultados indicaram que a maioria destas linguagens são Linguagem de Propósito Geral (LPG) em vez de Linguagem de Domínio Específico (LDE), o que nos leva a concluir que existe uma oportunidade de investigação aplicada de linguagens que tornem a codificação mais fácil para os especialistas do domínio
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