RPPM : Rapid Performance Prediction of Multithreaded workloads on multicore processors

Analytical performance modeling is a useful complement to detailed cycle-level simulation to quickly explore the design space in an early design stage. Mechanistic analytical modeling is particularly interesting as it provides deep insight and does not require expensive offline profiling as empirical modeling. Previous work in mechanistic analytical modeling, unfortunately, is limited to single-threaded applications running on single-core processors. This work proposes RPPM, a mechanistic analytical performance model for multi-threaded applications on multicore hardware. RPPM collects microarchitecture-independent characteristics of a multi-threaded workload to predict performance on a previously unseen multicore architecture. The profile needs to be collected only once to predict a range of processor architectures. We evaluate RPPM's accuracy against simulation and report a performance prediction error of 11.2% on average (23% max). We demonstrate RPPM's usefulness for conducting design space exploration experiments as well as for analyzing parallel application performance

On the acceleration of wavefront applications using distributed many-core architectures

In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P). Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures

Modeling high-performance wormhole NoCs for critical real-time embedded systems

Manycore chips are a promising computing platform to cope with the increasing performance needs of critical real-time embedded systems (CRTES). However, manycores adoption by CRTES industry requires understanding task's timing behavior when their requests use manycore's network-on-chip (NoC) to access hardware shared resources. This paper analyzes the contention in wormhole-based NoC (wNoC) designs - widely implemented in the high-performance domain - for which we introduce a new metric: worst-contention delay (WCD) that captures wNoC impact on worst-case execution time (WCET) in a tighter manner than the existing metric, worst-case traversal time (WCTT). Moreover, we provide an analytical model of the WCD that requests can suffer in a wNoC and we validate it against wNoC designs resembling those in the Tilera-Gx36 and the Intel-SCC 48-core processors. Building on top of our WCD analytical model, we analyze the impact on WCD that different design parameters such as the number of virtual channels, and we make a set of recommendations on what wNoC setups to use in the context of CRTES.Peer ReviewedPostprint (author's final draft

ANALYTICAL MODEL FOR CHIP MULTIPROCESSOR MEMORY HIERARCHY DESIGN AND MAMAGEMENT

Continued advances in circuit integration technology has ushered in the era of chip multiprocessor (CMP) architectures as further scaling of the performance of conventional wide-issue superscalar processor architectures remains hard and costly. CMP architectures take advantageof Moore¡¯s Law by integrating more cores in a given chip area rather than a single fastyet larger core. They achieve higher performance with multithreaded workloads. However,CMP architectures pose many new memory hierarchy design and management problems thatmust be addressed. For example, how many cores and how much cache capacity must weintegrate in a single chip to obtain the best throughput possible? Which is more effective,allocating more cache capacity or memory bandwidth to a program?This thesis research develops simple yet powerful analytical models to study two newmemory hierarchy design and resource management problems for CMPs. First, we considerthe chip area allocation problem to maximize the chip throughput. Our model focuses onthe trade-off between the number of cores, cache capacity, and cache management strategies.We find that different cache management schemes demand different area allocation to coresand cache to achieve their maximum performance. Second, we analyze the effect of cachecapacity partitioning on the bandwidth requirement of a given program. Furthermore, ourmodel considers how bandwidth allocation to different co-scheduled programs will affect theindividual programs¡¯ performance. Since the CMP design space is large and simulating only one design point of the designspace under various workloads would be extremely time-consuming, the conventionalsimulation-based research approach quickly becomes ineffective. We anticipate that ouranalytical models will provide practical tools to CMP designers and correctly guide theirdesign efforts at an early design stage. Furthermore, our models will allow them to betterunderstand potentially complex interactions among key design parameters

QoS Driven Coordinated Management of Resources to Save Energy in Multi-Core Systems

Reducing the energy consumption of computing systems is a necessary endeavor. However, saving energy should not come at the expense of degrading user experience. To this end, in this thesis, we assume that applications running on multi-core processors are associated with a quality-of-service (QoS) target in terms of performance constraints. This way, hardware resources can be throttled to minimize energy expenditure without violating the QoS requirements. Typical resource management schemes control different resources such as processor cores and on-chip cache memory independently. These approaches are not effective under performance constraints for all applications. Therefore, this thesis presents multi-core resource management schemes that coordinately control several resources in a unified algorithm. This way, the resource manger can find trade-offs between resource allocations to different applications to reduce system-level energy consumption, while still meeting the QoS targets expressed as performance constraints for every application. Implementing a coordinated resource management scheme that dynamically adapts to varying run time behavior of a multi-programmed workload without any prior knowledge about the applications is a challenging task. Two different schemes are presented in this thesis to address this challenge. Both schemes are invoked at regular intervals during program execution. They employ simple and, yet, sufficiently accurate analytical models and a novel hardware technique to predict the effect of different resource allocations on performance and energy for each application. Using a heuristic method, the multi-dimensional system configuration space is pruned in several levels to find the optimum resource settings, with respect to energy efficiency, in a negligible time. In the first scheme a resource management algorithm is presented that coordinates the control of voltage-frequency (VF) of each processor core with partitioning of the on-chip cache space. In the second scheme, a re-configurable processor is considered in which sections of the core micro-architectural resources can be dynamically deactivated to save energy. The resource manager can reactivate these sections, at the proper time, to increase instruction and memory level parallelism (ILP/MLP). This introduces new trade-offs between processor core size, VF settings, and the allocation of cache space for each application. By exploiting these trade-offs, the second scheme improves the energy savings compared to the first scheme considerably. The proposed schemes are evaluated using a novel simulation framework. This framework estimates the effect of different resource management algorithms on full execution of benchmark applications in a multi-programmed workload. According to the experimental results, the proposed schemes can save up to 18% of system energy while respecting the performance constraints of all applications. The average energy savings are 6% and 10% with the first and second schemes, respectively. Further experiments on the first scheme shows that energy savings can potentially improve up to 29% if the users can tolerate a bounded reduction in performance that leads to 40% longer execution time

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

Optimizing energy-efficiency for multi-core packet processing systems in a compiler framework

Network applications become increasingly computation-intensive and the amount of traffic soars unprecedentedly nowadays. Multi-core and multi-threaded techniques are thus widely employed in packet processing system to meet the changing requirement. However, the processing power cannot be fully utilized without a suitable programming environment. The compilation procedure is decisive for the quality of the code. It can largely determine the overall system performance in terms of packet throughput, individual packet latency, core utilization and energy efficiency. The thesis investigated compilation issues in networking domain first, particularly on energy consumption. And as a cornerstone for any compiler optimizations, a code analysis module for collecting program dependency is presented and incorporated into a compiler framework. With that dependency information, a strategy based on graph bi-partitioning and mapping is proposed to search for an optimal configuration in a parallel-pipeline fashion. The energy-aware extension is specifically effective in enhancing the energy-efficiency of the whole system. Finally, a generic evaluation framework for simulating the performance and energy consumption of a packet processing system is given. It accepts flexible architectural configuration and is capable of performingarbitrary code mapping. The simulation time is extremely short compared to full-fledged simulators. A set of our optimization results is gathered using the framework