Optimierte Implementierung ausgewählter kollektiver Operationen unter Ausnutzung der Hardwareparallelität des InfiniBand Netzwerkes

Ziel der Arbet ist eine optimierte Implementierung der im MPI-1 Standard definierten Reduktionsoperationen MPI_Reduce(), MPI_Allreduce(), MPI_Scan(), MPI_Reduce_scatter() für das InfiniBand Netzwerk. Hierbei soll besonderer Wert auf spezielle InfiniBand Operationen und die Hardwareparallelität gelegt werden. InfiniBand ermöglicht es Kommunikationsoperationen klar von Berechnungen zu trennen, was eine Überlappung beider Operationstypen in der Reduktion ermöglicht. Das Potential dieser Methode soll modelltheoretisch als auch praktisch in einer prototypischen Implementierung im Rahmen des Open MPI Frameworks erfolgen. Das Endresultat soll mit vorhandenen Implementierungen (z.B. MVAPICH) verglichen werden.The performance of collective communication operations is one of the deciding factors in the overall performance of a MPI application. Current implementations of MPI use the point-to-point components to access the InfiniBand network. Therefore it is tried to improve the performance of a collective component by accessing the InfiniBand network directly. This should avoid overhead and make it possible to tune the algorithms to this specific network. Various algorithms for the MPI_Reduce, MPI_Allreduce, MPI_Scan and MPI_Reduce_scatter operations are presented. The theoretical performance of the algorithms is analyzed with the LogfP and LogGP models. Selected algorithms are implemented as part of an Open MPI collective component. Finally the performance of different algorithms and different MPI implementations is compared

Towards Ultra-High Resolution Models of Climate and Weather

We present a speculative extrapolation of the performance aspects of an atmospheric general circulation model to ultra-high resolution and describe alternative technological paths to realize integration of such a model in the relatively near future. Due to a superlinear scaling of the computational burden dictated by stability criterion, the solution of the equations of motion dominate the calculation at ultra-high resolutions. From this extrapolation, it is estimated that a credible kilometer scale atmospheric model would require at least a sustained ten petaflop computer to provide scientifically useful climate simulations. Our design study portends an alternate strategy for practical power-efficient implementations of petaflop scale systems. Embedded processor technology could be exploited to tailor a custom machine designed to ultra-high climate model specifications at relatively affordable cost and power considerations. The major conceptual changes required by a kilometer scale climate model are certain to be difficult to implement. Although the hardware, software, and algorithms are all equally critical in conducting ultra-high climate resolution studies, it is likely that the necessary petaflop computing technology will be available in advance of a credible kilometer scale climate model

Evaluating techniques for parallelization tuning in MPI, OmpSs and MPI/OmpSs

Parallel programming is used to partition a computational problem among multiple processing units and to define how they interact (communicate and synchronize) in order to guarantee the correct result. The performance that is achieved when executing the parallel program on a parallel architecture is usually far from the optimal: computation unbalance and excessive interaction among processing units often cause lost cycles, reducing the efficiency of parallel computation. In this thesis we propose techniques oriented to better exploit parallelism in parallel applications, with emphasis in techniques that increase asynchronism. Theoretically, this type of parallelization tuning promises multiple benefits. First, it should mitigate communication and synchronization delays, thus increasing the overall performance. Furthermore, parallelization tuning should expose additional parallelism and therefore increase the scalability of execution. Finally, increased asynchronism would provide higher tolerance to slower networks and external noise. In the first part of this thesis, we study the potential for tuning MPI parallelism. More specifically, we explore automatic techniques to overlap communication and computation. We propose a speculative messaging technique that increases the overlap and requires no changes of the original MPI application. Our technique automatically identifies the application’s MPI activity and reinterprets that activity using optimally placed non-blocking MPI requests. We demonstrate that this overlapping technique increases the asynchronism of MPI messages, maximizing the overlap, and consequently leading to execution speedup and higher tolerance to bandwidth reduction. However, in the case of realistic scientific workloads, we show that the overlapping potential is significantly limited by the pattern by which each MPI process locally operates on MPI messages. In the second part of this thesis, we study the potential for tuning hybrid MPI/OmpSs parallelism. We try to gain a better understanding of the parallelism of hybrid MPI/OmpSs applications in order to evaluate how these applications would execute on future machines and to predict the execution bottlenecks that are likely to emerge. We explore how MPI/OmpSs applications could scale on the parallel machine with hundreds of cores per node. Furthermore, we investigate how this high parallelism within each node would reflect on the network constraints. We especially focus on identifying critical code sections in MPI/OmpSs. We devised a technique that quickly evaluates, for a given MPI/OmpSs application and the selected target machine, which code section should be optimized in order to gain the highest performance benefits. Also, this thesis studies techniques to quickly explore the potential OmpSs parallelism inherent in applications. We provide mechanisms to easily evaluate potential parallelism of any task decomposition. Furthermore, we describe an iterative trialand-error approach to search for a task decomposition that will expose sufficient parallelism for a given target machine. Finally, we explore potential of automating the iterative approach by capturing the programmers’ experience into an expert system that can autonomously lead the search process. Also, throughout the work on this thesis, we designed development tools that can be useful to other researchers in the field. The most advanced of these tools is Tareador – a tool to help porting MPI applications to MPI/OmpSs programming model. Tareador provides a simple interface to propose some decomposition of a code into OmpSs tasks. Tareador dynamically calculates data dependencies among the annotated tasks, and automatically estimates the potential OmpSs parallelization. Furthermore, Tareador gives additional hints on how to complete the process of porting the application to OmpSs. Tareador already proved itself useful, by being included in the academic classes on parallel programming at UPC.La programación paralela consiste en dividir un problema de computación entre múltiples unidades de procesamiento y definir como interactúan (comunicación y sincronización) para garantizar un resultado correcto. El rendimiento de un programa paralelo normalmente está muy lejos de ser óptimo: el desequilibrio de la carga computacional y la excesiva interacción entre las unidades de procesamiento a menudo causa ciclos perdidos, reduciendo la eficiencia de la computación paralela. En esta tesis proponemos técnicas orientadas a explotar mejor el paralelismo en aplicaciones paralelas, poniendo énfasis en técnicas que incrementan el asincronismo. En teoría, estas técnicas prometen múltiples beneficios. Primero, tendrían que mitigar el retraso de la comunicación y la sincronización, y por lo tanto incrementar el rendimiento global. Además, la calibración de la paralelización tendría que exponer un paralelismo adicional, incrementando la escalabilidad de la ejecución. Finalmente, un incremente en el asincronismo proveería una tolerancia mayor a redes de comunicación lentas y ruido externo. En la primera parte de la tesis, estudiamos el potencial para la calibración del paralelismo a través de MPI. En concreto, exploramos técnicas automáticas para solapar la comunicación con la computación. Proponemos una técnica de mensajería especulativa que incrementa el solapamiento y no requiere cambios en la aplicación MPI original. Nuestra técnica identifica automáticamente la actividad MPI de la aplicación y la reinterpreta usando solicitudes MPI no bloqueantes situadas óptimamente. Demostramos que esta técnica maximiza el solapamiento y, en consecuencia, acelera la ejecución y permite una mayor tolerancia a las reducciones de ancho de banda. Aún así, en el caso de cargas de trabajo científico realistas, mostramos que el potencial de solapamiento está significativamente limitado por el patrón según el cual cada proceso MPI opera localmente en el paso de mensajes. En la segunda parte de esta tesis, exploramos el potencial para calibrar el paralelismo híbrido MPI/OmpSs. Intentamos obtener una comprensión mejor del paralelismo de aplicaciones híbridas MPI/OmpSs para evaluar de qué manera se ejecutarían en futuras máquinas. Exploramos como las aplicaciones MPI/OmpSs pueden escalar en una máquina paralela con centenares de núcleos por nodo. Además, investigamos cómo este paralelismo de cada nodo se reflejaría en las restricciones de la red de comunicación. En especia, nos concentramos en identificar secciones críticas de código en MPI/OmpSs. Hemos concebido una técnica que rápidamente evalúa, para una aplicación MPI/OmpSs dada y la máquina objetivo seleccionada, qué sección de código tendría que ser optimizada para obtener la mayor ganancia de rendimiento. También estudiamos técnicas para explorar rápidamente el paralelismo potencial de OmpSs inherente en las aplicaciones. Proporcionamos mecanismos para evaluar fácilmente el paralelismo potencial de cualquier descomposición en tareas. Además, describimos una aproximación iterativa para buscar una descomposición en tareas que mostrará el suficiente paralelismo en la máquina objetivo dada. Para finalizar, exploramos el potencial para automatizar la aproximación iterativa. En el trabajo expuesto en esta tesis hemos diseñado herramientas que pueden ser útiles para otros investigadores de este campo. La más avanzada es Tareador, una herramienta para ayudar a migrar aplicaciones al modelo de programación MPI/OmpSs. Tareador proporciona una interfaz simple para proponer una descomposición del código en tareas OmpSs. Tareador también calcula dinámicamente las dependencias de datos entre las tareas anotadas, y automáticamente estima el potencial de paralelización OmpSs. Por último, Tareador da indicaciones adicionales sobre como completar el proceso de migración a OmpSs. Tareador ya se ha mostrado útil al ser incluido en las clases de programación de la UPC

Optimization techniques for fine-grained communication in PGAS environments

Partitioned Global Address Space (PGAS) languages promise to deliver improved programmer productivity and good performance in large-scale parallel machines. However, adequate performance for applications that rely on fine-grained communication without compromising their programmability is difficult to achieve. Manual or compiler assistance code optimization is required to avoid fine-grained accesses. The downside of manually applying code transformations is the increased program complexity and hindering of the programmer productivity. On the other hand, compiler optimizations of fine-grained accesses require knowledge of physical data mapping and the use of parallel loop constructs. This thesis presents optimizations for solving the three main challenges of the fine-grain communication: (i) low network communication efficiency; (ii) large number of runtime calls; and (iii) network hotspot creation for the non-uniform distribution of network communication, To solve this problems, the dissertation presents three approaches. First, it presents an improved inspector-executor transformation to improve the network efficiency through runtime aggregation. Second, it presents incremental optimizations to the inspector-executor loop transformation to automatically remove the runtime calls. Finally, the thesis presents a loop scheduling loop transformation for avoiding network hotspots and the oversubscription of nodes. In contrast to previous work that use static coalescing, prefetching, limited privatization, and caching, the solutions presented in this thesis focus cover all the aspect of fine-grained communication, including reducing the number of calls generated by the compiler and minimizing the overhead of the inspector-executor optimization. A performance evaluation with various microbenchmarks and benchmarks, aiming at predicting scaling and absolute performance numbers of a Power 775 machine, indicates that applications with regular accesses can achieve up to 180% of the performance of hand-optimized versions, while in applications with irregular accesses the transformations are expected to yield from 1.12X up to 6.3X speedup. The loop scheduling shows performance gains from 3-25% for NAS FT and bucket-sort benchmarks, and up to 3.4X speedup for the microbenchmarks

Analytical modelling for the performance prediction and optimisation of near-neighbour structured grid hydrodynamics

The advent of modern High Performance Computing (HPC) has facilitated the use of powerful supercomputing machines that have become the backbone of data analysis and simulation. With such a variety of software and hardware available today, understanding how well such machines can perform is key for both efficient use and future planning. With significant costs and multi-year turn-around times, procurement of a new HPC architecture can be a significant undertaking. In this work, we introduce one such measure to capture the performance of such machines – analytical performance models. These models provide a mathematical representation of the behaviour of an application in the context of how its various components perform for an architecture. By parameterising its workload in such a way that the time taken to compute can be described in relation to one or more benchmarkable statistics, this allows for a reusable representation of an application that can be applied to multiple architectures. This work goes on to introduce one such benchmark of interest, Hydra. Hydra is a benchmark 3D Eulerian structured mesh hydrocode implemented in Fortran, with which the explosive compression of materials, shock waves, and the behaviour of materials at the interface between components can be investigated. We assess its scaling behaviour and use this knowledge to construct a performance model that accurately predicts the runtime to within 15% across three separate machines, each with its own distinct characteristics. Further, this work goes on to explore various optimisation techniques, some of which see a marked speedup in the overall walltime of the application. Finally, another software application of interest with similar behaviour patterns, PETSc, is examined to demonstrate how different applications can exhibit similar modellable patterns

HUNTing the overlap

Abstract Hiding communication latency is an important optimization for parallel programs. Programmers or compilers achieve this by using non-blocking communication primitives and overlapping communication with computation or other communication operations. Using non-blocking communication raises two issues: performance and programmability. In terms of performance, optimizers need to find a good communication schedule and are sometimes constrained by lack of full application knowledge. In terms of programmability, efficiently managing nonblocking communication can prove cumbersome for complex applications. In this paper we present the design principles of HUNT, a runtime system designed to search and exploit some of the available overlap present at execution time in UPC programs. Using virtual memory support, our runtime implements demand-driven synchronization for data involved in communication operations. It also employs message decomposition and scheduling heuristics to transparently improve the non-blocking behavior of applications. We provide a user level implementation of HUNT on a variety of modern high performance computing systems. Results indicate that our approach is successful in finding some of the overlap available at execution time. While system and application characteristics influence performance, perhaps the determining factor is the time taken by the CPU to execute a signal handler. Demand driven synchronization at execution time eliminates the need for the explicit management of non-blocking communication. Besides increasing programmer productivity, this feature also simplifies compiler analysis for communication optimizations

HUNTing the Overlap

Hiding communication latency is an important optimization for parallel programs. Programmers or compilers achieve this by using non-blocking communication primitives and overlapping communication with computation or other communication operations. Using non-blocking communication raises two issues: performance and programmability.In terms of performance, optimizers need to find a good communication schedule and are sometimes constrained by lack of full application knowledge. In terms of programmability, efficiently managing non-blocking communication can prove cumbersome for complex applications.In this paper we present the design principles of HUNT, a runtime system designed to search and exploit some of the available overlap present at execution time in UPC programs. Using virtual memory support, our runtime implements demand-driven synchronizationfor data involved in communication operations. It also employs messagede composition and scheduling heuristics to transparently improve the non-blocking behavior of applications.We provide a user level implementation of HUNT on a variety of modern high performance computing systems. Results indicate that our approach is successful in finding some of the overlap available at execution time. While system and application characteristics influence performance, perhaps the determining factor is the time taken by the CPU to execute a signal handler. Demand driven synchronization at execution time eliminates the need for the explicit management of non-blocking communication. Besides increasing programmer productivity, this feature also simplifies compiler analysis for communication optimizations