Reducing the burden of parallel loop schedulers for many-core processors

Funder: FP7 People: Marie‐Curie Actions; Id: http://dx.doi.org/10.13039/100011264; Grant(s): 327744Summary: As core counts in processors increases, it becomes harder to schedule and distribute work in a timely and scalable manner. This article enhances the scalability of parallel loop schedulers by specializing schedulers for fine‐grain loops. We propose a low‐overhead work distribution mechanism for a static scheduler that uses no atomic operations. We integrate our static scheduler with the Intel OpenMP and Cilkplus parallel task schedulers to build hybrid schedulers. Compiler support enables efficient reductions for Cilk, without changing the programming interface of Cilk reducers. Detailed, quantitative measurements demonstrate that our techniques achieve scalable performance on a 48‐core machine and the scheduling overhead is 43% lower than Intel OpenMP and 12.1× lower than Cilk. We demonstrate consistent performance improvements on a range of HPC and data analytics codes. Performance gains are more important as loops become finer‐grain and thread counts increase. We observe consistently 16%–30% speedup on 48 threads, with a peak of 2.8× speedup

Performance Analysis Of Pde Based Parallel Algorithms On Different Computer Architectures

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Bilişim Enstitüsü, 2009Thesis (M.Sc.) -- İstanbul Technical University, Institute of Informatics, 2009Son yıllarda dağıtık algoritmaların farklı platformlarda kullanılabilmesi platform ve uygulama bağımsız performans analizi uygulamaları ihtiyacını arttırmıştır. Farklı donanımları ve haberleşme metodlarını destekleyen uygulamalar kullanıcılara donanım ve yazılımdan bağımsız ortak bir zemin hazırladıkları için kolaylık sağlamaktadır. Kısmi fark denklemleri hesaplamalı bilim ve mühendisliğin bir çok alanında kullanılmaktadır (ısı, dalga yayılımı gibi). Bu denklemlerin sayısal çözümü yinelemeli yöntemler kullanılarak yapılmaktadır. Problemin boyutu ve hata değerine göre çözüme ulaşmak için gereken yineleme sayısı ve buna bağlı olarak süresi değişmektedir. Kısmi fark denklemelerinin tek işlemcili bilgisayarlardaki çözümü uzun sürdüğü ve yüksek boyutlarda hafızaları yetersiz kaldığı için paralelleştirilerek birden fazla bilgisayarın işlemcisi ve hafızası kullanılarak çözülmektedir. Tezimde eliptik kısmi fark denklemlerini Gauss-Seidel ve Successive Over-Relaxation (SOR) metodlarını kullanarak çözen paralel algoritmalar kullanılmıştır. Performans analizi ve eniyilemesi kabaca üç adımdan oluşmaktadır; ölçüm, sonuçların analizi, darboğazların tespit edilip yazılımda iyileştirme yapılması. Ölçüm aşamasında programın koşarken ürettiği performans bilgisi toplanır, toplanan bu veriler görselleştirme araçları ile anlaşılır hale getirilerek yorumlanır. Yorumlama aşamasında tespit edilen dar boğazlar belirlenir ve giderilme yöntemleri araştırılır. Gerekli iyileştirmeler yapılarak program yeniden analiz edilir. Bu aşamaların her birinde farklı uygulamalar kullanılabilir fakat tez çalışmamda uygulamaları tek çatı altında toplayan TAU kullanılmıştır. TAU (Tuning and Analysis Utilities) farklı donanımları ve işletim sistemlerini destekleyerek farklı paralelleştirme metodlarını analiz edebilmektedir. Açık kaynak kodlu olan TAU diğer açık kaynak kodlu uygulamalar ile uyumlu olup birçok seviyede bütünleşme sağlanmıştır. Bu tez çalışmasında, iki farklı platformda aynı uygulamanın performans analizi yapılarak platform farkının getirdiği farklılıklar incelenmektedir. Performans analizinde bir algoritmanın eniyilemesini yapmak için genel bir kural olmadığından her algoritma her platformda incelenerek gerekli değişiklikler yapılmalıdır. Bu bağlamda kullandığım PDE algoritmasının her iki sistemdeki analizi sonucu elde edilen bilgiler yorumlanmıştır.In last two decades, use of parallel algorithms on different architectures increased the need of architecture and application independent performance analysis tools. Tools that support different communication methods and hardware prepare a common ground regardless of equipments provided. Partial differential equations (PDE) are used in several applications (such as propagation of heat, wave) in computational science and engineering. These equations can be solved using iterative numerical methods. Problem size and error tolerance effects iteration count and computation time to solve equation. PDE computations take long time using single processor computers with sequential algorithms, and if data size gets bigger single processors memory may be insufficient. Thus, PDE?s are solved using parallel algorithms on multiple processors. In this thesis, elliptic partial differential equation is solved using Gauss-Seidel and Successive Over-Relaxation (SOR) methods parallel algorithms. Performance analysis and optimization basically has three steps; evaluation, analysis of gathered information, defining and optimizing bottlenecks. In evaluation, performance information is gathered while program runs, then observations are made on gathered information by using visualization tools. Bottlenecks are defined and optimization techniques are researched. Necessary improvements are made to analyze the program again. Different applications in each of these stages can be used but in this thesis TAU is used, which collects these applications under one roof. TAU (Tuning and Analysis Utilities) supports many hardware, operating systems and parallelization methods. TAU is an open source application and collaborates with other open source applications at different levels. In this thesis, differences based on performance analysis of an algorithm in different two architectures are investigated. In performance analysis and optimization there is no golden rule to speed up algorithm. Each algorithm must be analyzed on that specific architecture. In this context, the performance analysis of a PDE algorithm on two architectures has been interpreted.Yüksek LisansM.Sc

Optimización del rendimiento y la eficiencia energética en sistemas masivamente paralelos

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)

Modelling of Information Flow and Resource Utilization in the EDGE Distributed Web System

The adoption of Distributed Web Systems (DWS) into modern engineering design process has dramatically increased in recent years. The Engineering Design Guide and Environment (EDGE) is one such DWS, intended to provide an integrated set of tools for use in the development of new products and services. Previous attempts to improve the efficiency and scalability of DWS focused largely on hardware utilization (i.e. multithreading and virtualization) and software scalability (i.e. load balancing and cloud services). However, these techniques are often limited to analysis of the computational complexity of the algorithms implemented. This work seeks to improve the understanding of efficiency and scalability of DWS by modelling the dynamics of information flow and resource utilization by characterizing DWS workloads through historical usage data (i.e. request type, frequency, access time). The design and implementation of EDGE is described. A DWS model of an EDGE system is developed and validated against theoretical limiting cases. The DWS model is used to predict the throughput of an EDGE system given a resource allocation and workflow. Results of the simulation suggest that proposed DWS designs can be evaluated according to the usage requirements of an engineering firm, ultimately guiding an informed decision for the selection and deployment of a DWS in an enterprise environment. Recommendations for future work related to the continued development of EDGE, DWS modelling of EDGE installation environments, and the extension of DWS modelling to new product development processes are presented

Optimization Techniques for Parallel Programming of Embedded Many-Core Computing Platforms

Nowadays many-core computing platforms are widely adopted as a viable solution to accelerate compute-intensive workloads at different scales, from low-cost devices to HPC nodes. It is well established that heterogeneous platforms including a general-purpose host processor and a parallel programmable accelerator have the potential to dramatically increase the peak performance/Watt of computing architectures. However the adoption of these platforms further complicates application development, whereas it is widely acknowledged that software development is a critical activity for the platform design. The introduction of parallel architectures raises the need for programming paradigms capable of effectively leveraging an increasing number of processors, from two to thousands. In this scenario the study of optimization techniques to program parallel accelerators is paramount for two main objectives: first, improving performance and energy efficiency of the platform, which are key metrics for both embedded and HPC systems; second, enforcing software engineering practices with the aim to guarantee code quality and reduce software costs. This thesis presents a set of techniques that have been studied and designed to achieve these objectives overcoming the current state-of-the-art. As a first contribution, we discuss the use of OpenMP tasking as a general-purpose programming model to support the execution of diverse workloads, and we introduce a set of runtime-level techniques to support fine-grain tasks on high-end many-core accelerators (devices with a power consumption greater than 10W). Then we focus our attention on embedded computer vision (CV), with the aim to show how to achieve best performance by exploiting the characteristics of a specific application domain. To further reduce the power consumption of parallel accelerators beyond the current technological limits, we describe an approach based on the principles of approximate computing, which implies modification to the program semantics and proper hardware support at the architectural level

Überblick zur Softwareentwicklung in Wissenschaftlichen Anwendungen

Viele wissenschaftliche Disziplinen müssen heute immer komplexer werdende numerische Probleme lösen. Die Komplexität der benutzten wissenschaftlichen Software steigt dabei kontinuierlich an. Diese Komplexitätssteigerung wird durch eine ganze Reihe sich ändernder Anforderungen verursacht: Die Betrachtung gekoppelter Phänomene gewinnt Aufmerksamkeit und gleichzeitig müssen neue Technologien wie das Grid-Computing oder neue Multiprozessorarchitekturen genutzt werden, um weiterhin in angemessener Zeit zu Berechnungsergebnissen zu kommen. Diese Fülle an neuen Anforderungen kann nicht mehr von kleinen spezialisierten Wissenschaftlergruppen in Isolation bewältigt werden. Die Entwicklung wissenschaftlicher Software muss vielmehr in interdisziplinären Gruppen geschehen, was neue Herausforderungen in der Softwareentwicklung induziert. Ein Paradigmenwechsel zu einer stärkeren Separation von Verantwortlichkeiten innerhalb interdisziplinärer Entwicklergruppen ist bis jetzt in vielen Fällen nur in Ansätzen erkennbar. Die Kopplung partitioniert durchgeführter Simulationen physikalischer Phänomene ist ein wichtiges Beispiel für softwaretechnisch herausfordernde Aufgaben im Gebiet des wissenschaftlichen Rechnens. In diesem Kontext modellieren verschiedene Simulationsprogramme unterschiedliche Teile eines komplexeren gekoppelten Systems. Die vorliegende Arbeit gibt einen Überblick über Paradigmen, die darauf abzielen Softwareentwicklung für Berechnungsprogramme verlässlicher und weniger abhängig voneinander zu machen. Ein spezielles Augenmerk liegt auf der Entwicklung gekoppelter Simulationen.Fields of modern science and engineering are in need of solving more and more complex numerical problems. The complexity of scientific software thereby rises continuously. This growth is caused by a number of changing requirements. Coupled phenomena gain importance and new technologies like the computational Grid, graphical and heterogeneous multi-core processors have to be used to achieve high-performance. The amount of additional complexity can not be handled by small groups of specialised scientists. The interdiciplinary nature of scientific software thereby presents new challanges for software engineering. A paradigm shift towards a stronger separation of concerns becomes necessary in the development of future scientific software. The coupling of independently simulated physical phenomena is an important example for a software-engineering concern in the domain of computational science. In this context, different simulation-programs model only a part of a more complex coupled system. The present work gives overview on paradigms which aim at making software-development in computational sciences more reliable and less interdependent. A special focus is put on the development of coupled simulations