The case for a Hardware Filesystem

As secondary storage devices get faster with flash based solid state drives (SSDs) and emerging technologies like phase change memories (PCM), overheads in system software like operating system (OS) and filesystem become prominent and may limit the potential performance improvements. Moreover, with rapidly increasing on-chip core count, monolithic operating systems will face scalability issues on these many-core chips. Future operating systems are likely to have a distributed nature, with a separation of operating system services amongst cores. Also, general purpose processors are known to be both performance and power inefficient while executing operating system code. In the domain of High Performance Computing with FPGAs too, relying on the OS for file I/O transactions using slow embedded processors, hinders performance. Migrating the filesystem into a dedicated hardware core, has the potential of improving the performance of data-intensive applications by bypassing the OS stack to provide higher bandwdith and reduced latency while accessing disks. To test the feasibility of this idea, an FPGA-based Hardware Filesystem (HWFS) was designed with five basic operations (open, read, write, delete and seek). Furthermore, multi-disk and RAID-0 (striping) support has been implemented as an option in the filesystem. In order to reduce design complexity and facilitate easier testing of the HWFS, a RAM disk was used initially. The filesystem core has been integrated and tested with a hardware application core (BLAST) as well as a multi-node FPGA network to provide remote-disk access. Finally, a SATA IP core was developed and directly integrated with HWFS to test with SSDs. For evaluation, HWFS's performance was compared to an Ext2 filesystem, both on an FPGA-based soft processor as well as a modern AMD Opteron Linux server with sequential and random workloads. Results prove that the Hardware Filesystem and supporting infrastructure provide substantial performance improvement over software only systems. The system is also resource efficient consuming less than 3% of logic and 5% of the Block RAMs of a Xilinx Virtex-6 chip

Monitoring, analysis and optimisation of I/O in parallel applications

High performance computing (HPC) is changing the way science is performed in the 21st Century; experiments that once took enormous amounts of time, were dangerous and often produced inaccurate results can now be performed and refined in a fraction of the time in a simulation environment. Current generation supercomputers are running in excess of 1016 floating point operations per second, and the push towards exascale will see this increase by two orders of magnitude. To achieve this level of performance it is thought that applications may have to scale to potentially billions of simultaneous threads, pushing hardware to its limits and severely impacting failure rates. To reduce the cost of these failures, many applications use checkpointing to periodically save their state to persistent storage, such that, in the event of a failure, computation can be restarted without significant data loss. As computational power has grown by approximately 2x every 18 ? 24 months, persistent storage has lagged behind; checkpointing is fast becoming a bottleneck to performance. Several software and hardware solutions have been presented to solve the current I/O problem being experienced in the HPC community and this thesis examines some of these. Specifically, this thesis presents a tool designed for analysing and optimising the I/O behaviour of scientific applications, as well as a tool designed to allow the rapid analysis of one software solution to the problem of parallel I/O, namely the parallel log-structured file system (PLFS). This thesis ends with an analysis of a modern Lustre file system under contention from multiple applications and multiple compute nodes running the same problem through PLFS. The results and analysis presented outline a framework through which application settings and procurement decisions can be made

Energy Concerns with HPC Systems and Applications

For various reasons including those related to climate changes, {\em energy} has become a critical concern in all relevant activities and technical designs. For the specific case of computer activities, the problem is exacerbated with the emergence and pervasiveness of the so called {\em intelligent devices}. From the application side, we point out the special topic of {\em Artificial Intelligence}, who clearly needs an efficient computing support in order to succeed in its purpose of being a {\em ubiquitous assistant}. There are mainly two contexts where {\em energy} is one of the top priority concerns: {\em embedded computing} and {\em supercomputing}. For the former, power consumption is critical because the amount of energy that is available for the devices is limited. For the latter, the heat dissipated is a serious source of failure and the financial cost related to energy is likely to be a significant part of the maintenance budget. On a single computer, the problem is commonly considered through the electrical power consumption. This paper, written in the form of a survey, we depict the landscape of energy concerns in computer activities, both from the hardware and the software standpoints.Comment: 20 page

A reference model for integrated energy and power management of HPC systems

Optimizing a computer for highest performance dictates the efficient use of its limited resources. Computers as a whole are rather complex. Therefore, it is not sufficient to consider optimizing hardware and software components independently. Instead, a holistic view to manage the interactions of all components is essential to achieve system-wide efficiency. For High Performance Computing (HPC) systems, today, the major limiting resources are energy and power. The hardware mechanisms to measure and control energy and power are exposed to software. The software systems using these mechanisms range from firmware, operating system, system software to tools and applications. Efforts to improve energy and power efficiency of HPC systems and the infrastructure of HPC centers achieve perpetual advances. In isolation, these efforts are unable to cope with the rising energy and power demands of large scale systems. A systematic way to integrate multiple optimization strategies, which build on complementary, interacting hardware and software systems is missing. This work provides a reference model for integrated energy and power management of HPC systems: the Open Integrated Energy and Power (OIEP) reference model. The goal is to enable the implementation, setup, and maintenance of modular system-wide energy and power management solutions. The proposed model goes beyond current practices, which focus on individual HPC centers or implementations, in that it allows to universally describe any hierarchical energy and power management systems with a multitude of requirements. The model builds solid foundations to be understandable and verifiable, to guarantee stable interaction of hardware and software components, for a known and trusted chain of command. This work identifies the main building blocks of the OIEP reference model, describes their abstract setup, and shows concrete instances thereof. A principal aspect is how the individual components are connected, interface in a hierarchical manner and thus can optimize for the global policy, pursued as a computing center's operating strategy. In addition to the reference model itself, a method for applying the reference model is presented. This method is used to show the practicality of the reference model and its application. For future research in energy and power management of HPC systems, the OIEP reference model forms a cornerstone to realize --- plan, develop and integrate --- innovative energy and power management solutions. For HPC systems themselves, it supports to transparently manage current systems with their inherent complexity, it allows to integrate novel solutions into existing setups, and it enables to design new systems from scratch. In fact, the OIEP reference model represents a basis for holistic efficient optimization.Computer auf höchstmögliche Rechenleistung zu optimieren bedingt Effizienzmaximierung aller limitierenden Ressourcen. Computer sind komplexe Systeme. Deshalb ist es nicht ausreichend, Hardware und Software isoliert zu betrachten. Stattdessen ist eine Gesamtsicht des Systems notwendig, um die Interaktionen aller Einzelkomponenten zu organisieren und systemweite Optimierungen zu ermöglichen. Für Höchstleistungsrechner (HLR) ist die limitierende Ressource heute ihre Leistungsaufnahme und der resultierende Gesamtenergieverbrauch. In aktuellen HLR-Systemen sind Energie- und Leistungsaufnahme programmatisch auslesbar als auch direkt und indirekt steuerbar. Diese Mechanismen werden in diversen Softwarekomponenten von Firmware, Betriebssystem, Systemsoftware bis hin zu Werkzeugen und Anwendungen genutzt und stetig weiterentwickelt. Durch die Komplexität der interagierenden Systeme ist eine systematische Optimierung des Gesamtsystems nur schwer durchführbar, als auch nachvollziehbar. Ein methodisches Vorgehen zur Integration verschiedener Optimierungsansätze, die auf komplementäre, interagierende Hardware- und Softwaresysteme aufbauen, fehlt. Diese Arbeit beschreibt ein Referenzmodell für integriertes Energie- und Leistungsmanagement von HLR-Systemen, das „Open Integrated Energy and Power (OIEP)“ Referenzmodell. Das Ziel ist ein Referenzmodell, dass die Entwicklung von modularen, systemweiten energie- und leistungsoptimierenden Sofware-Verbunden ermöglicht und diese als allgemeines hierarchisches Managementsystem beschreibt. Dies hebt das Modell von bisherigen Ansätzen ab, welche sich auf Einzellösungen, spezifischen Software oder die Bedürfnisse einzelner Rechenzentren beschränken. Dazu beschreibt es Grundlagen für ein planbares und verifizierbares Gesamtsystem und erlaubt nachvollziehbares und sicheres Delegieren von Energie- und Leistungsmanagement an Untersysteme unter Aufrechterhaltung der Befehlskette. Die Arbeit liefert die Grundlagen des Referenzmodells. Hierbei werden die Einzelkomponenten der Software-Verbunde identifiziert, deren abstrakter Aufbau sowie konkrete Instanziierungen gezeigt. Spezielles Augenmerk liegt auf dem hierarchischen Aufbau und der resultierenden Interaktionen der Komponenten. Die allgemeine Beschreibung des Referenzmodells erlaubt den Entwurf von Systemarchitekturen, welche letztendlich die Effizienzmaximierung der Ressource Energie mit den gegebenen Mechanismen ganzheitlich umsetzen können. Hierfür wird ein Verfahren zur methodischen Anwendung des Referenzmodells beschrieben, welches die Modellierung beliebiger Energie- und Leistungsverwaltungssystemen ermöglicht. Für Forschung im Bereich des Energie- und Leistungsmanagement für HLR bildet das OIEP Referenzmodell Eckstein, um Planung, Entwicklung und Integration von innovativen Lösungen umzusetzen. Für die HLR-Systeme selbst unterstützt es nachvollziehbare Verwaltung der komplexen Systeme und bietet die Möglichkeit, neue Beschaffungen und Entwicklungen erfolgreich zu integrieren. Das OIEP Referenzmodell bietet somit ein Fundament für gesamtheitliche effiziente Systemoptimierung

Development and Performance Evaluation of Network Function Virtualization Services in 5G Multi-Access Edge Computing

L'abstract è presente nell'allegato / the abstract is in the attachmen

JTIT

kwartalni

Fifth NASA Goddard Conference on Mass Storage Systems and Technologies

This document contains copies of those technical papers received in time for publication prior to the Fifth Goddard Conference on Mass Storage Systems and Technologies held September 17 - 19, 1996, at the University of Maryland, University Conference Center in College Park, Maryland. As one of an ongoing series, this conference continues to serve as a unique medium for the exchange of information on topics relating to the ingestion and management of substantial amounts of data and the attendant problems involved. This year's discussion topics include storage architecture, database management, data distribution, file system performance and modeling, and optical recording technology. There will also be a paper on Application Programming Interfaces (API) for a Physical Volume Repository (PVR) defined in Version 5 of the Institute of Electrical and Electronics Engineers (IEEE) Reference Model (RM). In addition, there are papers on specific archives and storage products

Fourth NASA Goddard Conference on Mass Storage Systems and Technologies

This report contains copies of all those technical papers received in time for publication just prior to the Fourth Goddard Conference on Mass Storage and Technologies, held March 28-30, 1995, at the University of Maryland, University College Conference Center, in College Park, Maryland. This series of conferences continues to serve as a unique medium for the exchange of information on topics relating to the ingestion and management of substantial amounts of data and the attendant problems involved. This year's discussion topics include new storage technology, stability of recorded media, performance studies, storage system solutions, the National Information infrastructure (Infobahn), the future for storage technology, and lessons learned from various projects. There also will be an update on the IEEE Mass Storage System Reference Model Version 5, on which the final vote was taken in July 1994