Fast simulation techniques for microprocessor design space exploration

Designing a microprocessor is extremely time-consuming. Computer architects heavily rely on architectural simulators, e.g., to drive high-level design decisions during early stage design space exploration. The benefit of architectural simulators is that they yield relatively accurate performance results, are highly parameterizable and are very flexible to use. The downside, however, is that they are at least three or four orders of magnitude slower than real hardware execution. The current trend towards multicore processors exacerbates the problem; as the number of cores on a multicore processor increases, simulation speed has become a major concern in computer architecture research and development. In this dissertation, we propose and evaluate two simulation techniques that reduce the simulation time significantly: statistical simulation and interval simulation. Statistical simulation speeds up the simulation by reducing the number of dynamically executed instructions. First, we collect a number of program execution characteristics into a statistical profile. From this profile we can generate a synthetic trace that exhibits the same execution behavior but which has a much shorter trace length as compared to the original trace. Simulating this synthetic trace then yields a performance estimate. Interval simulation raises the level of abstraction in architectural simulation; it replaces the core-level cycle-accurate simulation model by a mechanistic analytical model. The analytical model builds on insights from interval analysis: miss events divide the smooth streaming of instructions into so called intervals. The model drives the timing by analyzing the type of the miss events and their latencies, instead of tracking the individual instructions as they propagate through the pipeline stages

Doctor of Philosophy

dissertationComputer programs have complex interactions with their underlying hardware, exhibiting complex behaviors as a result. It is critical to understand these programs, as they serve an importantrole: researchers use them to express new ideas in computer science, while many others derive production value from them. In both cases, program understanding leads to mastery over these functions, adding value to human endeavors. Memory behavior is one of the hallmarks of general program behavior: it represents the critical function of retrieving data for the program to work on; it often reflects the overall actions taken by the program, providing a signature of program behavior; and it is often an important performance bottleneck, as the the memory subsystem is typically much slower than the processor. These reasons justify an investigation into the memory behavior of programs. A memory reference trace is a list of memory transactions performed by a program at runtime, a rich data source capturing the whole of a program's interaction with the memory subsystem, and a clear starting point for investigating program memory behavior. However, such a trace is extremely difficult to interpret by mere inspection, as it consists solely of many, many addresses and operation codes, without any more structure or context. This dissertation proposes to use visualization to construct images and animations of the data within a reference trace, thereby visually transmitting structures and events as encoded in the trace. These visualization approaches are designed with different focuses, meant to expose various aspects of the trace. For instance, the time dimension of the reference traces can be handled either with animation, showing events as they occur, or by laying time out in a spatial dimension, giving a view of the entire history of the trace at once. The approaches also vary in their level of abstraction from the hardware: some are concretely connected to representations of the memory itself, while others are more free-form, using more abstract metaphors to highlight general behaviors and patterns, which in turn characterize the program behavior. Each approach delivers its own set of insights, as demonstrated in this dissertation

On the co-design of scientific applications and long vector architectures

The landscape of High Performance Computing (HPC) system architectures keeps expanding with new technologies and increased complexity. To improve the efficiency of next-generation compute devices, architects are looking for solutions beyond the commodity CPU approach. In 2021, the five most powerful supercomputers in the world use either GP-GPU (General-purpose computing on graphics processing units) accelerators or a customized CPU specially designed to target HPC applications. This trend is only expected to grow in the next years motivated by the compute demands of science and industry. As architectures evolve, the ecosystem of tools and applications must follow. The choices in the number of cores in a socket, the floating point-units per core and the bandwidth through the memory hierarchy among others, have a large impact in the power consumption and compute capabilities of the devices. To balance CPU and accelerators, designers require accurate tools for analyzing and predicting the impact of new architectural features on the performance of complex scientific applications at scale. In such a large design space, capturing and modeling with simulators the complex interactions between the system software and hardware components is a defying challenge. Moreover, applications must be able to exploit those designs with aggressive compute capabilities and memory bandwidth configurations. Algorithms and data structures will need to be redesigned accordingly to expose a high degree of data-level parallelism allowing them to scale in large systems. Therefore, next-generation computing devices will be the result of a co-design effort in hardware and applications supported by advanced simulation tools. In this thesis, we focus our work on the co-design of scientific applications and long vector architectures. We significantly extend a multi-scale simulation toolchain enabling accurate performance and power estimations of large-scale HPC systems. Through simulation, we explore the large design space in current HPC trends over a wide range of applications. We extract speedup and energy consumption figures analyzing the trade-offs and optimal configurations for each of the applications. We describe in detail the optimization process of two challenging applications on real vector accelerators, achieving outstanding operation performance and full memory bandwidth utilization. Overall, we provide evidence-based architectural and programming recommendations that will serve as hardware and software co-design guidelines for the next generation of specialized compute devices.El panorama de las arquitecturas de los sistemas para la Computación de Alto Rendimiento (HPC, de sus siglas en inglés) sigue expandiéndose con nuevas tecnologías y complejidad adicional. Para mejorar la eficiencia de la próxima generación de dispositivos de computación, los arquitectos están buscando soluciones más allá de las CPUs. En 2021, los cinco supercomputadores más potentes del mundo utilizan aceleradores gráficos aplicados a propósito general (GP-GPU, de sus siglas en inglés) o CPUs diseñadas especialmente para aplicaciones HPC. En los próximos años, se espera que esta tendencia siga creciendo motivada por las demandas de más potencia de computación de la ciencia y la industria. A medida que las arquitecturas evolucionan, el ecosistema de herramientas y aplicaciones les debe seguir. Las decisiones eligiendo el número de núcleos por zócalo, las unidades de coma flotante por núcleo y el ancho de banda a través de la jerarquía de memoría entre otros, tienen un gran impacto en el consumo de energía y las capacidades de cómputo de los dispositivos. Para equilibrar las CPUs y los aceleradores, los diseñadores deben utilizar herramientas precisas para analizar y predecir el impacto de nuevas características de la arquitectura en el rendimiento de complejas aplicaciones científicas a gran escala. Dado semejante espacio de diseño, capturar y modelar con simuladores las complejas interacciones entre el software de sistema y los componentes de hardware es un reto desafiante. Además, las aplicaciones deben ser capaces de explotar tales diseños con agresivas capacidades de cómputo y ancho de banda de memoria. Los algoritmos y estructuras de datos deberán ser rediseñadas para exponer un alto grado de paralelismo de datos permitiendo así escalarlos en grandes sistemas. Por lo tanto, la siguiente generación de dispósitivos de cálculo será el resultado de un esfuerzo de codiseño tanto en hardware como en aplicaciones y soportado por avanzadas herramientas de simulación. En esta tesis, centramos nuestro trabajo en el codiseño de aplicaciones científicas y arquitecturas vectoriales largas. Extendemos significativamente una serie de herramientas para la simulación multiescala permitiendo así obtener estimaciones de rendimiento y potencia de sistemas HPC de gran escala. A través de simulaciones, exploramos el gran espacio de diseño de las tendencias actuales en HPC sobre un amplio rango de aplicaciones. Extraemos datos sobre la mejora y el consumo energético analizando las contrapartidas y las configuraciones óptimas para cada una de las aplicaciones. Describimos en detalle el proceso de optimización de dos aplicaciones en aceleradores vectoriales, obteniendo un rendimiento extraordinario a nivel de operaciones y completa utilización del ancho de memoria disponible. Con todo, ofrecemos recomendaciones empíricas a nivel de arquitectura y programación que servirán como instrucciones para diseñar mejor hardware y software para la siguiente generación de dispositivos de cálculo especializados.Postprint (published version

Exploring Superpage Promotion Policies for Efficient Address Translation

Address translation performance for modern applications depends heavily upon the number of translation entries cached in the hardware TLB (translation look-aside buffer). Therefore, the efficiency of address translation relies directly on the TLB hit rate. The number of TLB entries continues to fall further behind the growth of memory consumption for modern applications. Superpages, which are pages with larger sizes, can increase the efficiency of the TLB by enabling each translation entry to cover a larger memory region. Without requiring more TLB entries, using superpages can increase the TLB hit rate and benefit address translation. However, using superpages can bring overhead. The TLB uses a single dirty bit to mark a page as dirty during address translation before modifying the page, so the granularity of the dirty bit corresponds to the coverage of the translation entry. As a result, the OS (operating system) will pay extra I/O effort when it allocates or writes an underutilized superpage back to disk. Such extra overhead can easily surpass the address translation benefits of superpages. This thesis discusses the performance trade-offs of superpages by exploring the design space of superpage promotion policies in the OS. A data collection infrastructure is built based on QEMU with kernel instrumentation on FreeBSD to collaboratively collect both memory accesses and kernel events. Then, the TLB behavior of Intel Skylake x86 family processors is simulated. The simulation has been validated to be faithful and consistent with the real-world performance. Last, this thesis evaluates and compares both TLB performance benefits and I/O overheads among the superpage promotion policies to discuss the trade-offs in the design space

Architectural Support for High-Performance, Power-Efficient and Secure Multiprocessor Systems

High performance systems have been widely adopted in many fields and the demand for better performance is constantly increasing. And the need of powerful yet flexible systems is also increasing to meet varying application requirements from diverse domains. Also, power efficiency in high performance computing has been one of the major issues to be resolved. The power density of core components becomes significantly higher, and the fraction of power supply in total management cost is dominant. Providing dependability is also a main concern in large-scale systems since more hardware resources can be abused by attackers. Therefore, designing high-performance, power-efficient and secure systems is crucial to provide adequate performance as well as reliability to users. Adhering to using traditional design methodologies for large-scale computing systems has a limit to meet the demand under restricted resource budgets. Interconnecting a large number of uniprocessor chips to build parallel processing systems is not an efficient solution in terms of performance and power. Chip multiprocessor (CMP) integrates multiple processing cores and caches on a chip and is thought of as a good alternative to previous design trends. In this dissertation, we deal with various design issues of high performance multiprocessor systems based on CMP to achieve both performance and power efficiency while maintaining security. First, we propose a fast and secure off-chip interconnects through minimizing network overheads and providing an efficient security mechanism. Second, we propose architectural support for fast and efficient memory protection in CMP systems, making the best use of the characteristics in CMP environments and multi-threaded workloads. Third, we propose a new router design for network-on-chip (NoC) based on a new memory technique. We introduce hybrid input buffers that use both SRAM and STT-MRAM for better performance as well as power efficiency. Simulation results show that the proposed schemes improve the performance of off-chip networks through reducing the message size by 54% on average. Also, the schemes diminish the overheads of bounds checking operations, thus enhancing the overall performance by 11% on average. Adopting hybrid buffers in NoC routers contributes to increasing the network throughput up to 21%