Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Just-in-time Hardware generation for abstracted reconfigurable computing

This thesis addresses the use of reconfigurable hardware in computing platforms, in order to harness the performance benefits of dedicated hardware whilst maintaining the flexibility associated with software. Although the reconfigurable computing concept is not new, the low level nature of the supporting tools normally used, together with the consequent limited level of abstraction and resultant lack of backwards compatibility, has prevented the widespread adoption of this technology. In addition, bandwidth and architectural limitations, have seriously constrained the potential improvements in performance. A review of existing approaches and tools flows is conducted to highlight the current problems being faced in this field. The objective of the work presented in this thesis is to introduce a radically new approach to reconfigurable computing tool flows. The runtime based tool flow introduces complete abstraction between the application developer and the underlying hardware. This new technique eliminates the ease of use and backwards compatibility issues that have plagued the reconfigurable computing concept, and could pave the way for viable mainstream reconfigurable computing platforms. An easy to use, cycle accurate behavioural modelling system is also presented, which was used extensively during the early exploration of new concepts and architectures. Some performance improvements produced by the new reconfigurable computing tool flow, when applied to both a MIPS based embedded platform, and the Cray XDl, are also presented. These results are then analyzed and the hardware and software factors affecting the performance increases that were obtained are discussed, together with potential techniques that could be used to further increase the performance of the system. Lastly a heterogenous computing concept is proposed, in which, a computer system, containing multiple types of computational resource is envisaged, each having their own strengths and weaknesses (e.g. DSPs, CPUs, FPGAs). A revolutionary new method of fully exploiting the potential of such a system, whilst maintaining scalability, backwards compatibility, and ease of use is also presented

Mitosis based speculative multithreaded architectures

In the last decade, industry made a right-hand turn and shifted towards multi-core processor designs, also known as Chip-Multi-Processors (CMPs), in order to provide further performance improvements under a reasonable power budget, design complexity, and validation cost. Over the years, several processor vendors have come out with multi-core chips in their product lines and they have become mainstream, with the number of cores increasing in each processor generation. Multi-core processors improve the performance of applications by exploiting Thread Level Parallelism (TLP) while the Instruction Level Parallelism (ILP) exploited by each individual core is limited. These architectures are very efficient when multiple threads are available for execution. However, single-thread sections of code (single-thread applications and serial sections of parallel applications) pose important constraints on the benefits achieved by parallel execution, as pointed out by Amdahl’s law. Parallel programming, even with the help of recently proposed techniques like transactional memory, has proven to be a very challenging task. On the other hand, automatically partitioning applications into threads may be a straightforward task in regular applications, but becomes much harder for irregular programs, where compilers usually fail to discover sufficient TLP. In this scenario, two main directions have been followed in the research community to take benefit of multi-core platforms: Speculative Multithreading (SpMT) and Non-Speculative Clustered architectures. The former splits a sequential application into speculative threads, while the later partitions the instructions among the cores based on data-dependences but avoid large degree of speculation. Despite the large amount of research on both these approaches, the proposed techniques so far have shown marginal performance improvements. In this thesis we propose novel schemes to speed-up sequential or lightly threaded applications in multi-core processors that effectively address the main unresolved challenges of previous approaches. In particular, we propose a SpMT architecture, called Mitosis, that leverages a powerful software value prediction technique to manage inter-thread dependences, based on pre-computation slices (p-slices). Thanks to the accuracy and low cost of this technique, Mitosis is able to effectively parallelize applications even in the presence of frequent dependences among threads. We also propose a novel architecture, called Anaphase, that combines the best of SpMT schemes and clustered architectures. Anaphase effectively exploits ILP, TLP and Memory Level Parallelism (MLP), thanks to its unique finegrain thread decomposition algorithm that adapts to the available parallelism in the application.Postprint (published version

Mitosis based speculative multithreaded architectures

In the last decade, industry made a right-hand turn and shifted towards multi-core processor designs, also known as Chip-Multi-Processors (CMPs), in order to provide further performance improvements under a reasonable power budget, design complexity, and validation cost. Over the years, several processor vendors have come out with multi-core chips in their product lines and they have become mainstream, with the number of cores increasing in each processor generation. Multi-core processors improve the performance of applications by exploiting Thread Level Parallelism (TLP) while the Instruction Level Parallelism (ILP) exploited by each individual core is limited. These architectures are very efficient when multiple threads are available for execution. However, single-thread sections of code (single-thread applications and serial sections of parallel applications) pose important constraints on the benefits achieved by parallel execution, as pointed out by Amdahl’s law. Parallel programming, even with the help of recently proposed techniques like transactional memory, has proven to be a very challenging task. On the other hand, automatically partitioning applications into threads may be a straightforward task in regular applications, but becomes much harder for irregular programs, where compilers usually fail to discover sufficient TLP. In this scenario, two main directions have been followed in the research community to take benefit of multi-core platforms: Speculative Multithreading (SpMT) and Non-Speculative Clustered architectures. The former splits a sequential application into speculative threads, while the later partitions the instructions among the cores based on data-dependences but avoid large degree of speculation. Despite the large amount of research on both these approaches, the proposed techniques so far have shown marginal performance improvements. In this thesis we propose novel schemes to speed-up sequential or lightly threaded applications in multi-core processors that effectively address the main unresolved challenges of previous approaches. In particular, we propose a SpMT architecture, called Mitosis, that leverages a powerful software value prediction technique to manage inter-thread dependences, based on pre-computation slices (p-slices). Thanks to the accuracy and low cost of this technique, Mitosis is able to effectively parallelize applications even in the presence of frequent dependences among threads. We also propose a novel architecture, called Anaphase, that combines the best of SpMT schemes and clustered architectures. Anaphase effectively exploits ILP, TLP and Memory Level Parallelism (MLP), thanks to its unique finegrain thread decomposition algorithm that adapts to the available parallelism in the application

Memory architectures for exaflop computing systems

Most computing systems are heavily dependent on their main memories, as their primary storage, or as an intermediate cache for slower storage systems (HDDs). The capacity of memory systems, as well as their performance, have a direct impact on overall computing capabilities of the system, and are also major contributors to its initial and operating costs. Dynamic Random Access Memory (DRAM) technology has been dominating the main memory landscape since its beginnings in 1970s until today. However, due to DRAM's inherent limitations, its steady rate of development has saturated over the past decade, creating a disparity between CPU and main memory performance, known as the memory wall. Modern parallel architectures, such as High-Performance Computing (HPC) clusters and manycore solutions, create even more stress on their memory systems. It is not trivial to estimate memory requirements that these systems will have in the future, and if DRAM technology would be able to meet them, or we would need to look for a novel memory solution. This thesis attempts to give insight in the most important technological challenges that future memory systems need to address, in order to meet the ever growing requirements of users and their applications, in manycore and HPC context. We try to describe the limitations of DRAM, as the dominant technology in today's main memory systems, that may impede performance or increase cost of future systems. We discuss some of the emerging memory technologies, and by comparing them with DRAM, we try to estimate their potential usage in future memory systems. The thesis evaluates the requirements of manycore scientific applications, in terms of memory bandwidth and footprint, and estimates how these requirements may change in the future. With this evaulation in mind, we propose a hybrid memory solution that employs DRAM and PCM, as well as several page placement and page migration policies, to bridge the gap between fast and small DRAM and larger but slower non-volatile memory. As the aforementioned evaluations required custom software solutions, we present tools we produced over the course of this PhD, which continue to be used in Heterogeneous Computer Architectures group in Barcelona Supercomputing Center. First, Limpio - a LIghtweight MPI instrumentatiOn framework, that provides an interface for low-overhead instrumentation and profiling of MPI applications with user-defined routines. Second, MemTraceMPI, a Valgrind tool, used to produce memory access traces of MPI applications, with several innovative concepts included (filter-cache, iteration tracing, compressed trace files).La mayoría de los sistemas de computación dependen en gran medida de sus principales recuerdos, como su almacenamiento primario, o como un caché intermedio para sistemas de almacenamiento más lentos (discos duros). La capacidad de los sistemas de memoria, así como su rendimiento, tienen un impacto directo en las capacidades globales de computación del sistema, y también son los principales contribuyentes a sus costos iniciales y de operación. Tecnología Dynamic Random Access memoria (DRAM) ha estado dominando el principal paisaje de memoria desde sus inicios en 1970 hasta la actualidad. Sin embargo, debido a las limitaciones inherentes de DRAM, su tasa constante de desarrollo ha saturado durante la última década, creando una disparidad entre la CPU y el rendimiento de la memoria principal, conocido como el muro de la memoria. Arquitecturas modernas paralelas, como la computación (HPC) de alto rendimiento y soluciones manycore, crear aún más presión sobre sus sistemas de memoria. No es trivial para estimar los requisitos de memoria que estos sistemas tendrán en el futuro, y si la tecnología DRAM sería capaz de cumplir con ellas, o que tendría que buscar una solución de memoria novela. En esta tesis se intenta dar una idea de los más importantes retos tecnológicos que los sistemas de memoria futuras deben abordar, con el fin de satisfacer las necesidades cada vez mayores de los usuarios y sus aplicaciones, en Manycore y HPC contexto. Intentamos describir las limitaciones de memoria DRAM, como la tecnología dominante en los sistemas de memoria principal de hoy en día, que pueden impedir el rendimiento o el aumento de los costos de los sistemas futuros. Se discuten algunas de las tecnologías de memoria emergentes, y comparándolos con DRAM, tratamos de estimar su uso potencial en sistemas de memoria futuras. La tesis evalúa los requisitos de las aplicaciones científicas manycore, en términos de ancho de banda de memoria y huella, y estima cómo estos requisitos pueden cambiar en el futuro. Con esta evaulation en mente, proponemos una solución de memoria híbrida que emplea DRAM y PCM, así como varias políticas de colocación de la página y la página de la migración, para cerrar la brecha entre la DRAM rápido y pequeño y más grande pero la memoria más lenta no volátil. Como las evaluaciones mencionadas necesarias soluciones de software personalizadas, se presentan las herramientas que hemos producido en el transcurso de esta tesis doctoral, que se siguen utilizando en el grupo heterogéneo de computadoras Arquitecturas en Barcelona Supercomputing Center. En primer lugar, Limpio - un marco MPI Instrumentación ligero, que proporciona una interfaz para la instrumentación de baja sobrecarga y perfilado de aplicaciones MPI con rutinas definidas por el usuario. En segundo lugar, MemTraceMPI, una herramienta Valgrind, utilizado para producir los rastros de acceso a memoria de aplicaciones MPI, con varios conceptos innovadores incluido (filtro-cache, trazado iteración, archivos de seguimiento comprimido)