Vector coprocessor sharing techniques for multicores: performance and energy gains

Vector Processors (VPs) created the breakthroughs needed for the emergence of computational science many years ago. All commercial computing architectures on the market today contain some form of vector or SIMD processing. Many high-performance and embedded applications, often dealing with streams of data, cannot efficiently utilize dedicated vector processors for various reasons: limited percentage of sustained vector code due to substantial flow control; inherent small parallelism or the frequent involvement of operating system tasks; varying vector length across applications or within a single application; data dependencies within short sequences of instructions, a problem further exacerbated without loop unrolling or other compiler optimization techniques. Additionally, existing rigid SIMD architectures cannot tolerate efficiently dynamic application environments with many cores that may require the runtime adjustment of assigned vector resources in order to operate at desired energy/performance levels. To simultaneously alleviate these drawbacks of rigid lane-based VP architectures, while also releasing on-chip real estate for other important design choices, the first part of this research proposes three architectural contexts for the implementation of a shared vector coprocessor in multicore processors. Sharing an expensive resource among multiple cores increases the efficiency of the functional units and the overall system throughput. The second part of the dissertation regards the evaluation and characterization of the three proposed shared vector architectures from the performance and power perspectives on an FPGA (Field-Programmable Gate Array) prototype. The third part of this work introduces performance and power estimation models based on observations deduced from the experimental results. The results show the opportunity to adaptively adjust the number of vector lanes assigned to individual cores or processing threads in order to minimize various energy-performance metrics on modern vector- capable multicore processors that run applications with dynamic workloads. Therefore, the fourth part of this research focuses on the development of a fine-to-coarse grain power management technique and a relevant adaptive hardware/software infrastructure which dynamically adjusts the assigned VP resources (number of vector lanes) in order to minimize the energy consumption for applications with dynamic workloads. In order to remove the inherent limitations imposed by FPGA technologies, the fifth part of this work consists of implementing an ASIC (Application Specific Integrated Circuit) version of the shared VP towards precise performance-energy studies involving high- performance vector processing in multicore environments

Sur la conception de solveurs linéaires hybrides pour les architectures parallèles modernes

In the context of this thesis, our focus is on numerical linear algebra, more precisely on solution of large sparse systems of linear equations. We focus on designing efficient parallel implementations of MaPHyS, an hybrid linear solver based on domain decomposition techniques. First we investigate the MPI+threads approach. In MaPHyS, the first level of parallelism arises from the independent treatment of the various subdomains. The second level is exploited thanks to the use of multi-threaded dense and sparse linear algebra kernels involved at the subdomain level. Such an hybrid implementation of an hybrid linear solver suitably matches the hierarchical structure of modern supercomputers and enables a trade-off between the numerical and parallel performances of the solver. We demonstrate the flexibility of our parallel implementation on a set of test examples. Secondly, we follow a more disruptive approach where the algorithms are described as sets of tasks with data inter-dependencies that leads to a directed acyclic graph (DAG) representation. The tasks are handled by a runtime system. We illustrate how a first task-based parallel implementation can be obtained by composing task-based parallel libraries within MPI processes throught a preliminary prototype implementation of our hybrid solver. We then show how a task-based approach fully abstracting the hardware architecture can successfully exploit a wide range of modern hardware architectures. We implemented a full task-based Conjugate Gradient algorithm and showed that the proposed approach leads to very high performance on multi-GPU, multicore and heterogeneous architectures.Dans le contexte de cette thèse, nous nous focalisons sur des algorithmes pour l’algèbre linéaire numérique, plus précisément sur la résolution de grands systèmes linéaires creux. Nous mettons au point des méthodes de parallélisation pour le solveur linéaire hybride MaPHyS. Premièrement nous considerons l'aproche MPI+threads. Dans MaPHyS, le premier niveau de parallélisme consiste au traitement indépendant des sous-domaines. Le second niveau est exploité grâce à l’utilisation de noyaux multithreadés denses et creux au sein des sous-domaines. Une telle implémentation correspond bien à la structure hiérarchique des supercalculateurs modernes et permet un compromis entre les performances numériques et parallèles du solveur. Nous démontrons la flexibilité de notre implémentation parallèle sur un ensemble de cas tests. Deuxièmement nous considérons un approche plus innovante, où les algorithmes sont décrits comme des ensembles de tâches avec des inter-dépendances, i.e., un graphe de tâches orienté sans cycle (DAG). Nous illustrons d’abord comment une première parallélisation à base de tâches peut être obtenue en composant des librairies à base de tâches au sein des processus MPI illustrer par un prototype d’implémentation préliminaire de notre solveur hybride. Nous montrons ensuite comment une approche à base de tâches abstrayant entièrement le matériel peut exploiter avec succès une large gamme d’architectures matérielles. À cet effet, nous avons implanté une version à base de tâches de l’algorithme du Gradient Conjugué et nous montrons que l’approche proposée permet d’atteindre une très haute performance sur des architectures multi-GPU, multicoeur ainsi qu’hétérogène

Multicore-optimized wavefront diamond blocking for optimizing stencil updates

The importance of stencil-based algorithms in computational science has focused attention on optimized parallel implementations for multilevel cache-based processors. Temporal blocking schemes leverage the large bandwidth and low latency of caches to accelerate stencil updates and approach theoretical peak performance. A key ingredient is the reduction of data traffic across slow data paths, especially the main memory interface. In this work we combine the ideas of multi-core wavefront temporal blocking and diamond tiling to arrive at stencil update schemes that show large reductions in memory pressure compared to existing approaches. The resulting schemes show performance advantages in bandwidth-starved situations, which are exacerbated by the high bytes per lattice update case of variable coefficients. Our thread groups concept provides a controllable trade-off between concurrency and memory usage, shifting the pressure between the memory interface and the CPU. We present performance results on a contemporary Intel processor

A Model-based Design Framework for Application-specific Heterogeneous Systems

The increasing heterogeneity of computing systems enables higher performance and power efficiency. However, these improvements come at the cost of increasing the overall complexity of designing such systems. These complexities include constructing implementations for various types of processors, setting up and configuring communication protocols, and efficiently scheduling the computational work. The process for developing such systems is iterative and time consuming, with no well-defined performance goal. Current performance estimation approaches use source code implementations that require experienced developers and time to produce. We present a framework to aid in the design of heterogeneous systems and the performance tuning of applications. Our framework supports system construction: integrating custom hardware accelerators with existing cores into processors, integrating processors into cohesive systems, and mapping computations to processors to achieve overall application performance and efficient hardware usage. It also facilitates effective design space exploration using processor models (for both existing and future processors) that do not require source code implementations to estimate performance. We evaluate our framework using a variety of applications and implement them in systems ranging from low power embedded systems-on-chip (SoC) to high performance systems consisting of commercial-off-the-shelf (COTS) components. We show how the design process is improved, reducing the number of design iterations and unnecessary source code development ultimately leading to higher performing efficient systems

A Multi-core processor for hard real-time systems

The increasing demand for new functionalities in current and future hard real-time embedded systems, like the ones deployed in automotive and avionics industries, is driving an increment in the performance required in current embedded processors. Multi-core processors represent a good design solution to cope with such higher performance requirements due to their better performance-per-watt ratio while maintaining the core design simple. Moreover, multi-cores also allow executing mixed-criticality level workloads composed of tasks with and without hard real-time requirements, maximizing the utilization of the hardware resources while guaranteeing low cost and low power consumption. Despite those benefits, current multi-core processors are less analyzable than single-core ones due to the interferences between different tasks when accessing hardware shared resources. As a result, estimating a meaningful Worst-Case Execution Time (WCET) estimation - i.e. to compute an upper bound of the application's execution time - becomes extremely difficult, if not even impossible, because the execution time of a task may change depending on the other threads running at the same time. This makes the WCET of a task dependent on the set of inter-task interferences introduced by the co-running tasks. Providing a WCET estimation independent from the other tasks (time composability property) is a key requirement in hard real-time systems. This thesis proposes a new multi-core processor design in which time composability is achieved, hence enabling the use of multi-cores in hard real-time systems. With our proposals the WCET estimation of a HRT is independent from the other co-running tasks. To that end, we design a multi-core processor in which the maximum delay a request from a Hard Real-time Task (HRT), accessing a hardware shared resource can suffer due to other tasks is bounded: our processor guarantees that a request to a shared resource cannot be delayed longer than a given Upper Bound Delay (UBD). In addition, the UBD allows identifying the impact that different processor configurations may have on the WCET by determining the sensitivity of a HRT to different resource allocations. This thesis proposes an off-line task allocation algorithm (called IA3: Interference-Aware Allocation Algorithm), that allocates tasks in a task set based on the HRT's sensitivity to different resource allocations. As a result the hardware shared resources used by HRTs are minimized, by allowing Non Hard Real-time Tasks (NHRTs) to use the rest of resources. Overall, our proposals provide analyzability for the HRTs allowing NHRTs to be executed into the same chip without any effect on the HRTs. The previous first two proposals of this thesis focused on supporting the execution of multi-programmed workloads with mixed-criticality levels (composed of HRTs and NHRTs). Higher performance could be achieved by implementing multi-threaded applications. As a first step towards supporting hard real-time parallel applications, this thesis proposes a new hardware/software approach to guarantee a predictable execution of software pipelined parallel programs. This thesis also investigates a solution to verify the timing correctness of HRTs without requiring any modification in the core design: we design a hardware unit which is interfaced with the processor and integrated into a functional-safety aware methodology. This unit monitors the execution time of a block of instructions and it detects if it exceeds the WCET. Concretely, we show how to handle timing faults on a real industrial automotive platform.La creciente demanda de nuevas funcionalidades en los sistemas empotrados de tiempo real actuales y futuros en industrias como la automovilística y la de aviación, está impulsando un incremento en el rendimiento necesario en los actuales procesadores empotrados. Los procesadores multi-núcleo son una solución eficiente para obtener un mayor rendimiento ya que aumentan el rendimiento por vatio, manteniendo el diseño del núcleo simple. Por otra parte, los procesadores multi-núcleo también permiten ejecutar cargas de trabajo con niveles de tiempo real mixtas (formadas por tareas de tiempo real duro y laxo así como tareas sin requerimientos de tiempo real), maximizando así la utilización de los recursos de procesador y garantizando el bajo consumo de energía. Sin embargo, a pesar los beneficios mencionados anteriormente, los actuales procesadores multi-núcleo son menos analizables que los de un solo núcleo debido a las interferencias surgidas cuando múltiples tareas acceden simultáneamente a los recursos compartidos del procesador. Como resultado, la estimación del peor tiempo de ejecución (conocido como WCET) - es decir, una cota superior del tiempo de ejecución de la aplicación - se convierte en extremadamente difícil, si no imposible, porque el tiempo de ejecución de una tarea puede cambiar dependiendo de las otras tareas que se estén ejecutando concurrentemente. Determinar una estimación del WCET independiente de las otras tareas es un requisito clave en los sistemas empotrados de tiempo real duro. Esta tesis propone un nuevo diseño de procesador multi-núcleo en el que el tiempo de ejecución de las tareas se puede componer, lo que permitirá el uso de procesadores multi-núcleo en los sistemas de tiempo real duro. Para ello, diseñamos un procesador multi-núcleo en el que la máxima demora que puede sufrir una petición de una tarea de tiempo real duro (HRT) para acceder a un recurso hardware compartido debido a otras tareas está acotado, tiene un límite superior (UBD). Además, UBD permite identificar el impacto que las diferentes posibles configuraciones del procesador pueden tener en el WCET, mediante la determinación de la sensibilidad en la variación del tiempo de ejecución de diferentes reservas de recursos del procesador. Esta tesis propone un algoritmo estático de reserva de recursos (llamado IA3), que asigna tareas a núcleos en función de dicha sensibilidad. Como resultado los recursos compartidos del procesador usados por tareas HRT se reducen al mínimo, permitiendo que las tareas sin requerimiento de tiempo real (NHRTs) puedas beneficiarse del resto de recursos. Por lo tanto, las propuestas presentadas en esta tesis permiten el análisis del WCET para tareas HRT, permitiendo así mismo la ejecución de tareas NHRTs en el mismo procesador multi-núcleo, sin que estas tengan ningún efecto sobre las tareas HRT. Las propuestas presentadas anteriormente se centran en el soporte a la ejecución de múltiples cargas de trabajo con diferentes niveles de tiempo real (HRT y NHRTs). Sin embargo, un mayor rendimiento puede lograrse mediante la transformación una tarea en múltiples sub-tareas paralelas. Esta tesis propone una nueva técnica, con soporte del procesador y del sistema operativo, que garantiza una ejecución analizable del modelo de ejecución paralela software pipelining. Esta tesis también investiga una solución para verificar la corrección del WCET de HRT sin necesidad de ninguna modificación en el diseño de la base: un nuevo componente externo al procesador se conecta a este sin necesidad de modificarlo. Esta nueva unidad monitorea el tiempo de ejecución de un bloque de instrucciones y detecta si se excede el WCET. Esta unidad permite detectar fallos de sincronización en sistemas de computación utilizados en automóviles

Thread Scheduling Mechanisms for Multiple-Context Parallel Processors

Scheduling tasks to efficiently use the available processor resources is crucial to minimizing the runtime of applications on shared-memory parallel processors. One factor that contributes to poor processor utilization is the idle time caused by long latency operations, such as remote memory references or processor synchronization operations. One way of tolerating this latency is to use a processor with multiple hardware contexts that can rapidly switch to executing another thread of computation whenever a long latency operation occurs, thus increasing processor utilization by overlapping computation with communication. Although multiple contexts are effective for tolerating latency, this effectiveness can be limited by memory and network bandwidth, by cache interference effects among the multiple contexts, and by critical tasks sharing processor resources with less critical tasks. This thesis presents techniques that increase the effectiveness of multiple contexts by intelligently scheduling threads to make more efficient use of processor pipeline, bandwidth, and cache resources. This thesis proposes thread prioritization as a fundamental mechanism for directing the thread schedule on a multiple-context processor. A priority is assigned to each thread either statically or dynamically and is used by the thread scheduler to decide which threads to load in the contexts, and to decide which context to switch to on a context switch. We develop a multiple-context model that integrates both cache and network effects, and shows how thread prioritization can both maintain high processor utilization, and limit increases in critical path runtime caused by multithreading. The model also shows that in order to be effective in bandwidth limited applications, thread prioritization must be extended to prioritize memory requests. We show how simple hardware can prioritize the running of threads in the multiple contexts, and the issuing of requests to both the local memory and the network. Simulation experiments show how thread prioritization is used in a variety of applications. Thread prioritization can improve the performance of synchronization primitives by minimizing the number of processor cycles wasted in spinning and devoting more cycles to critical threads. Thread prioritization can be used in combination with other techniques to improve cache performance and minimize cache interference between different working sets in the cache. For applications that are critical path limited, thread prioritization can improve performance by allowing processor resources to be devoted preferentially to critical threads. These experimental results show that thread prioritization is a mechanism that can be used to implement a wide range of scheduling policies