Making the case for reforming the I/O software stack of extreme-scale systems

This work was supported in part by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research, under Contract No. DE-AC02-05CH11231. This research has been partially funded by the Spanish Ministry of Science and Innovation under grant TIN2010-16497 “Input/Output techniques for distributed and high-performance computing environments”. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number 328582

A multi-tier cached I/O architecture for massively parallel supercomputers

Recent advances in storage technologies and high performance interconnects have made possible in the last years to build, more and more potent storage systems that serve thousands of nodes. The majority of storage systems of clusters and supercomputers from Top 500 list are managed by one of three scalable parallel file systems: GPFS, PVFS, and Lustre. Most large-scale scientific parallel applications are written in Message Passing Interface (MPI), which has become the de-facto standard for scalable distributed memory machines. One part of the MPI standard is related to I/O and has among its main goals the portability and efficiency of file system accesses. All of the above mentioned parallel file systems may be accessed also through the MPI-IO interface. The I/O access patterns of scientific parallel applications often consist of accesses to a large number of small, non-contiguous pieces of data. For small file accesses the performance is dominated by the latency of network transfers and disks. Parallel scientific applications lead to interleaved file access patterns with high interprocess spatial locality at the I/O nodes. Additionally, scientific applications exhibit repetitive behaviour when a loop or a function with loops issues I/O requests. When I/O access patterns are repetitive, caching and prefetching can effectively mask their access latency. These characteristics of the access patterns motivated several researchers to propose parallel I/O optimizations both at library and file system levels. However, these optimizations are not always integrated across different layers in the systems. In this dissertation we propose a novel generic parallel I/O architecture for clusters and supercomputers. Our design is aimed at large-scale parallel architectures with thousands of compute nodes. Besides acting as middleware for existing parallel file systems, our architecture provides on-line virtualization of storage resources. Another objective of this thesis is to factor out the common parallel I/O functionality from clusters and supercomputers in generic modules in order to facilitate porting of scientific applications across these platforms. Our solution is based on a multi-tier cache architecture, collective I/O, and asynchronous data staging strategies hiding the latency of data transfer between cache tiers. The thesis targets to reduce the file access latency perceived by the data-intensive parallel scientific applications by multi-layer asynchronous data transfers. In order to accomplish this objective, our techniques leverage the multi-core architectures by overlapping computation with communication and I/O in parallel threads. Prototypes of our solutions have been deployed on both clusters and Blue Gene supercomputers. Performance evaluation shows that the combination of collective strategies with overlapping of computation, communication, and I/O may bring a substantial performance benefit for access patterns common for parallel scientific applications.-----------------------------------------------------------------------------------------------------------------------------En los últimos años se ha observado un incremento sustancial de la cantidad de datos producidos por las aplicaciones científicas paralelas y de la necesidad de almacenar estos datos de forma persistente. Los sistemas de ficheros paralelos como PVFS, Lustre y GPFS han ofrecido una solución escalable para esta demanda creciente de almacenamiento. La mayoría de las aplicaciones científicas son escritas haciendo uso de la interfaz de paso de mensajes (MPI), que se ha convertido en un estándar de-facto de programación para las arquitecturas de memoria distribuida. Las aplicaciones paralelas que usan MPI pueden acceder a los sistemas de ficheros paralelos a través de la interfaz ofrecida por MPI-IO. Los patrones de acceso de las aplicaciones científicas paralelas consisten en un gran número de accesos pequeños y no contiguos. Para tamaños de acceso pequeños, el rendimiento viene limitado por la latencia de las transferencias de red y disco. Además, las aplicaciones científicas llevan a cabo accesos con una alta localidad espacial entre los distintos procesos en los nodos de E/S. Adicionalmente, las aplicaciones científicas presentan típicamente un comportamiento repetitivo. Cuando los patrones de acceso de E/S son repetitivos, técnicas como escritura demorada y lectura adelantada pueden enmascarar de forma eficiente las latencias de los accesos de E/S. Estas características han motivado a muchos investigadores en proponer optimizaciones de E/S tanto a nivel de biblioteca como a nivel del sistema de ficheros. Sin embargo, actualmente estas optimizaciones no se integran siempre a través de las distintas capas del sistema. El objetivo principal de esta tesis es proponer una nueva arquitectura genérica de E/S paralela para clusters y supercomputadores. Nuestra solución está basada en una arquitectura de caches en varias capas, una técnica de E/S colectiva y estrategias de acceso asíncronas que ocultan la latencia de transferencia de datos entre las distintas capas de caches. Nuestro diseño está dirigido a arquitecturas paralelas escalables con miles de nodos de cómputo. Además de actuar como middleware para los sistemas de ficheros paralelos existentes, nuestra arquitectura debe proporcionar virtualización on-line de los recursos de almacenamiento. Otro de los objeticos marcados para esta tesis es la factorización de las funcionalidades comunes en clusters y supercomputadores, en módulos genéricos que faciliten el despliegue de las aplicaciones científicas a través de estas plataformas. Se han desplegado distintos prototipos de nuestras soluciones tanto en clusters como en supercomputadores. Las evaluaciones de rendimiento demuestran que gracias a la combicación de las estratégias colectivas de E/S y del solapamiento de computación, comunicación y E/S, se puede obtener una sustancial mejora del rendimiento en los patrones de acceso anteriormente descritos, muy comunes en las aplicaciones paralelas de caracter científico

Survey of storage systems for high-performance computing

In current supercomputers, storage is typically provided by parallel distributed file systems for hot data and tape archives for cold data. These file systems are often compatible with local file systems due to their use of the POSIX interface and semantics, which eases development and debugging because applications can easily run both on workstations and supercomputers. There is a wide variety of file systems to choose from, each tuned for different use cases and implementing different optimizations. However, the overall application performance is often held back by I/O bottlenecks due to insufficient performance of file systems or I/O libraries for highly parallel workloads. Performance problems are dealt with using novel storage hardware technologies as well as alternative I/O semantics and interfaces. These approaches have to be integrated into the storage stack seamlessly to make them convenient to use. Upcoming storage systems abandon the traditional POSIX interface and semantics in favor of alternative concepts such as object and key-value storage; moreover, they heavily rely on technologies such as NVM and burst buffers to improve performance. Additional tiers of storage hardware will increase the importance of hierarchical storage management. Many of these changes will be disruptive and require application developers to rethink their approaches to data management and I/O. A thorough understanding of today's storage infrastructures, including their strengths and weaknesses, is crucially important for designing and implementing scalable storage systems suitable for demands of exascale computing

Týr: Stockage Massif Transactionnel à Hautes-Performances

As the computational power used by large-scale applications increases, the amount of data they need to manipulate tends to increase as well. A wide range of such applications requires robust and flexible storage support for atomic, durable and concurrent transactions. Historically, databases have provided the de facto solution to transactional data management, but they have forced applications to drop control over data layout and access mechanisms, while remaining unable to meet the scale requirements of Big Data. More recently, key-value stores have been introduced to address these issues. However, this solution does not provide transactions, or only restricted transaction support, compelling users to carefully coordinate access to data in order to avoid race conditions, partial writes, overwrites, and other hard problems that cause erratic behaviour. We argue there is a gap between existing storage solutions and application requirements that limits the design of transaction-oriented data-intensive applications. In this paper we introduce Týr, a massively parallel distributed transactional blob storage system. A key feature behind Týr is its novel multi-versioning management designed to keep the metadata overhead as low as possible while still allowing fast queries or updates and preserving transaction semantics. Its share-nothing architecture ensures minimal contention and provides low latency for large numbers of concurrent requests. Týr is the first blob storage system to provide sequential consistency and high throughput, while enabling unforeseen transaction support. Experiments with a real-life application from the CERN LHC show Týr throughput outperforming state-of-the-art solutions by more than 100%.À mesure que la puissance de calcul utilisée par des applications à grande échelle augmente, le volume de données qu’elles manipulent tend à augmenter également. Une grande partie de ces applications nécessite un système de stockage robuste et flexible permettant l’exécution de transactions de manière concurrente. Antérieurement, les bases de données furent la solution de facto pour la gestion des données transactionnelles, mais elles empêchent les applications de contrôler l’organisation du stockage des données ainsi que l’accés à ces données, tout en restant incapables de répondre aux contraintes posées par les données massives. Plus récemment, des systèmes de stockage clé-valeur ont été créés pour répondre à cette problématique. Cependant, ces solutions ne fournissent pas de support des transactions, ou seulement un support partiel, imposant aux utilisateurs de coordonner avec soin l’accès aux données afin d’éviter tout état de concurrence, écritures partielles, surécritures, ainsi que d’autres problèmes à l’origine d’un comportement erratique des applications. Nous soutenons qu’il existe un fossé entre les solutions de stockage actuelles et les besoins des utilisateurs, ce qui limite la conception des applications transactionnelles gérant des volumes massifs de données. Dans ce document, nous présentons Týr, un système de stockage de blobs distribué et transactionnel. Une des caractéristiques principales de Týr est sa gestion des versions novatrice conçue pour permettre un accès rapide tant en lecture qu’en écriture aux données tout en gardant une sémantique transactionnelle et en nécessitant une faible surcharge de métadonnées. Son architecture décentralisée garantit une contention minimale et permet une faible latence avec un nombre important de requêtes concurrentes. Týr est le permier système de stockage de blobs à fournir à la fois une consistence séquentielle et un débit élevé, tout en apportant le support des transactions. Les expériences réalisées avec une application réelle du CERN LHC montrent que le débit de Týr surpasse celui des solutions actuelles de plus de 100%

Programming Abstractions for Data Locality

The goal of the workshop and this report is to identify common themes and standardize concepts for locality-preserving abstractions for exascale programming models. Current software tools are built on the premise that computing is the most expensive component, we are rapidly moving to an era that computing is cheap and massively parallel while data movement dominates energy and performance costs. In order to respond to exascale systems (the next generation of high performance computing systems), the scientific computing community needs to refactor their applications to align with the emerging data-centric paradigm. Our applications must be evolved to express information about data locality. Unfortunately current programming environments offer few ways to do so. They ignore the incurred cost of communication and simply rely on the hardware cache coherency to virtualize data movement. With the increasing importance of task-level parallelism on future systems, task models have to support constructs that express data locality and affinity. At the system level, communication libraries implicitly assume all the processing elements are equidistant to each other. In order to take advantage of emerging technologies, application developers need a set of programming abstractions to describe data locality for the new computing ecosystem. The new programming paradigm should be more data centric and allow to describe how to decompose and how to layout data in the memory.Fortunately, there are many emerging concepts such as constructs for tiling, data layout, array views, task and thread affinity, and topology aware communication libraries for managing data locality. There is an opportunity to identify commonalities in strategy to enable us to combine the best of these concepts to develop a comprehensive approach to expressing and managing data locality on exascale programming systems. These programming model abstractions can expose crucial information about data locality to the compiler and runtime system to enable performance-portable code. The research question is to identify the right level of abstraction, which includes techniques that range from template libraries all the way to completely new languages to achieve this goal

File system metadata virtualization

The advance of computing systems has brought new ways to use and access the stored data that push the architecture of traditional file systems to its limits, making them inadequate to handle the new needs. Current challenges affect both the performance of high-end computing systems and its usability from the applications perspective. On one side, high-performance computing equipment is rapidly developing into large-scale aggregations of computing elements in the form of clusters, grids or clouds. On the other side, there is a widening range of scientific and commercial applications that seek to exploit these new computing facilities. The requirements of such applications are also heterogeneous, leading to dissimilar patterns of use of the underlying file systems. Data centres have tried to compensate this situation by providing several file systems to fulfil distinct requirements. Typically, the different file systems are mounted on different branches of a directory tree, and the preferred use of each branch is publicised to users. A similar approach is being used in personal computing devices. Typically, in a personal computer, there is a visible and clear distinction between the portion of the file system name space dedicated to local storage, the part corresponding to remote file systems and, recently, the areas linked to cloud services as, for example, directories to keep data synchronized across devices, to be shared with other users, or to be remotely backed-up. In practice, this approach compromises the usability of the file systems and the possibility of exploiting all the potential benefits. We consider that this burden can be alleviated by determining applicable features on a per-file basis, and not associating them to the location in a static, rigid name space. Moreover, usability would be further increased by providing multiple dynamic name spaces that could be adapted to specific application needs. This thesis contributes to this goal by proposing a mechanism to decouple the user view of the storage from its underlying structure. The mechanism consists in the virtualization of file system metadata (including both the name space and the object attributes) and the interposition of a sensible layer to take decisions on where and how the files should be stored in order to benefit from the underlying file system features, without incurring on usability or performance penalties due to inadequate usage. This technique allows to present multiple, simultaneous virtual views of the name space and the file system object attributes that can be adapted to specific application needs without altering the underlying storage configuration. The first contribution of the thesis introduces the design of a metadata virtualization framework that makes possible the above-mentioned decoupling; the second contribution consists in a method to improve file system performance in large-scale systems by using such metadata virtualization framework; finally, the third contribution consists in a technique to improve the usability of cloud-based storage systems in personal computing devices.Postprint (published version