A differentiated proposal of three dimension i/o performance characterization model focusing on storage environments

The I/O bottleneck remains a central issue in high-performance environments. Cloud computing, high-performance computing (HPC) and big data environments share many underneath difficulties to deliver data at a desirable time rate requested by high-performance applications. This increases the possibility of creating bottlenecks throughout the application feeding process by bottom hardware devices located in the storage system layer. In the last years, many researchers have been proposed solutions to improve the I/O architecture considering different approaches. Some of them take advantage of hardware devices while others focus on a sophisticated software approach. However, due to the complexity of dealing with high-performance environments, creating solutions to improve I/O performance in both software and hardware is challenging and gives researchers many opportunities. Classifying these improvements in different dimensions allows researchers to understand how these improvements have been built over the years and how it progresses. In addition, it also allows future efforts to be directed to research topics that have developed at a lower rate, balancing the general development process. This research present a three-dimension characterization model for classifying research works on I/O performance improvements for large scale storage computing facilities. This classification model can also be used as a guideline framework to summarize researches providing an overview of the actual scenario. We also used the proposed model to perform a systematic literature mapping that covered ten years of research on I/O performance improvements in storage environments. This study classified hundreds of distinct researches identifying which were the hardware, software, and storage systems that received more attention over the years, which were the most researches proposals elements and where these elements were evaluated. In order to justify the importance of this model and the development of solutions that targets I/O performance improvements, we evaluated a subset of these improvements using a a real and complete experimentation environment, the Grid5000. Analysis over different scenarios using a synthetic I/O benchmark demonstrates how the throughput and latency parameters behaves when performing different I/O operations using distinct storage technologies and approaches.O gargalo de E/S continua sendo um problema central em ambientes de alto desempenho. Os ambientes de computação em nuvem, computação de alto desempenho (HPC) e big data compartilham muitas dificuldades para fornecer dados em uma taxa de tempo desejável solicitada por aplicações de alto desempenho. Isso aumenta a possibilidade de criar gargalos em todo o processo de alimentação de aplicativos pelos dispositivos de hardware inferiores localizados na camada do sistema de armazenamento. Nos últimos anos, muitos pesquisadores propuseram soluções para melhorar a arquitetura de E/S considerando diferentes abordagens. Alguns deles aproveitam os dispositivos de hardware, enquanto outros se concentram em uma abordagem sofisticada de software. No entanto, devido à complexidade de lidar com ambientes de alto desempenho, criar soluções para melhorar o desempenho de E/S em software e hardware é um desafio e oferece aos pesquisadores muitas oportunidades. A classificação dessas melhorias em diferentes dimensões permite que os pesquisadores entendam como essas melhorias foram construídas ao longo dos anos e como elas progridem. Além disso, também permite que futuros esforços sejam direcionados para tópicos de pesquisa que se desenvolveram em menor proporção, equilibrando o processo geral de desenvolvimento. Esta pesquisa apresenta um modelo de caracterização tridimensional para classificar trabalhos de pesquisa sobre melhorias de desempenho de E/S para instalações de computação de armazenamento em larga escala. Esse modelo de classificação também pode ser usado como uma estrutura de diretrizes para resumir as pesquisas, fornecendo uma visão geral do cenário real. Também usamos o modelo proposto para realizar um mapeamento sistemático da literatura que abrangeu dez anos de pesquisa sobre melhorias no desempenho de E/S em ambientes de armazenamento. Este estudo classificou centenas de pesquisas distintas, identificando quais eram os dispositivos de hardware, software e sistemas de armazenamento que receberam mais atenção ao longo dos anos, quais foram os elementos de proposta mais pesquisados e onde esses elementos foram avaliados. Para justificar a importância desse modelo e o desenvolvimento de soluções que visam melhorias no desempenho de E/S, avaliamos um subconjunto dessas melhorias usando um ambiente de experimentação real e completo, o Grid5000. Análises em cenários diferentes usando um benchmark de E/S sintética demonstra como os parâmetros de vazão e latência se comportam ao executar diferentes operações de E/S usando tecnologias e abordagens distintas de armazenamento

Scaling and Resilience in Numerical Algorithms for Exascale Computing

The first Petascale supercomputer, the IBM Roadrunner, went online in 2008. Ten years later, the community is now looking ahead to a new generation of Exascale machines. During the decade that has passed, several hundred Petascale capable machines have been installed worldwide, yet despite the abundance of machines, applications that scale to their full size remain rare. Large clusters now routinely have 50.000+ cores, some have several million. This extreme level of parallelism, that has allowed a theoretical compute capacity in excess of a million billion operations per second, turns out to be difficult to use in many applications of practical interest. Processors often end up spending more time waiting for synchronization, communication, and other coordinating operations to complete, rather than actually computing. Component reliability is another challenge facing HPC developers. If even a single processor fail, among many thousands, the user is forced to restart traditional applications, wasting valuable compute time. These issues collectively manifest themselves as low parallel efficiency, resulting in waste of energy and computational resources. Future performance improvements are expected to continue to come in large part due to increased parallelism. One may therefore speculate that the difficulties currently faced, when scaling applications to Petascale machines, will progressively worsen, making it difficult for scientists to harness the full potential of Exascale computing. The thesis comprises two parts. Each part consists of several chapters discussing modifications of numerical algorithms to make them better suited for future Exascale machines. In the first part, the use of Parareal for Parallel-in-Time integration techniques for scalable numerical solution of partial differential equations is considered. We propose a new adaptive scheduler that optimize the parallel efficiency by minimizing the time-subdomain length without making communication of time-subdomains too costly. In conjunction with an appropriate preconditioner, we demonstrate that it is possible to obtain time-parallel speedup on the nonlinear shallow water equation, beyond what is possible using conventional spatial domain-decomposition techniques alone. The part is concluded with the proposal of a new method for constructing Parallel-in-Time integration schemes better suited for convection dominated problems. In the second part, new ways of mitigating the impact of hardware failures are developed and presented. The topic is introduced with the creation of a new fault-tolerant variant of Parareal. In the chapter that follows, a C++ Library for multi-level checkpointing is presented. The library uses lightweight in-memory checkpoints, protected trough the use of erasure codes, to mitigate the impact of failures by decreasing the overhead of checkpointing and minimizing the compute work lost. Erasure codes have the unfortunate property that if more data blocks are lost than parity codes created, the data is effectively considered unrecoverable. The final chapter contains a preliminary study on partial information recovery for incomplete checksums. Under the assumption that some meta knowledge exists on the structure of the data encoded, we show that the data lost may be recovered, at least partially. This result is of interest not only in HPC but also in data centers where erasure codes are widely used to protect data efficiently

Resilience for large ensemble computations

With the increasing power of supercomputers, ever more detailed models of physical systems can be simulated, and ever larger problem sizes can be considered for any kind of numerical system. During the last twenty years the performance of the fastest clusters went from the teraFLOPS domain (ASCI RED: 2.3 teraFLOPS) to the pre-exaFLOPS domain (Fugaku: 442 petaFLOPS), and we will soon have the first supercomputer with a peak performance cracking the exaFLOPS (El Capitan: 1.5 exaFLOPS). Ensemble techniques experience a renaissance with the availability of those extreme scales. Especially recent techniques, such as particle filters, will benefit from it. Current ensemble methods in climate science, such as ensemble Kalman filters, exhibit a linear dependency between the problem size and the ensemble size, while particle filters show an exponential dependency. Nevertheless, with the prospect of massive computing power come challenges such as power consumption and fault-tolerance. The mean-time-between-failures shrinks with the number of components in the system, and it is expected to have failures every few hours at exascale. In this thesis, we explore and develop techniques to protect large ensemble computations from failures. We present novel approaches in differential checkpointing, elastic recovery, fully asynchronous checkpointing, and checkpoint compression. Furthermore, we design and implement a fault-tolerant particle filter with pre-emptive particle prefetching and caching. And finally, we design and implement a framework for the automatic validation and application of lossy compression in ensemble data assimilation. Altogether, we present five contributions in this thesis, where the first two improve state-of-the-art checkpointing techniques, and the last three address the resilience of ensemble computations. The contributions represent stand-alone fault-tolerance techniques, however, they can also be used to improve the properties of each other. For instance, we utilize elastic recovery (2nd contribution) for mitigating resiliency in an online ensemble data assimilation framework (3rd contribution), and we built our validation framework (5th contribution) on top of our particle filter implementation (4th contribution). We further demonstrate that our contributions improve resilience and performance with experiments on various architectures such as Intel, IBM, and ARM processors.Amb l’increment de les capacitats de còmput dels supercomputadors, es poden simular models de sistemes físics encara més detallats, i es poden resoldre problemes de més grandària en qualsevol tipus de sistema numèric. Durant els últims vint anys, el rendiment dels clústers més ràpids ha passat del domini dels teraFLOPS (ASCI RED: 2.3 teraFLOPS) al domini dels pre-exaFLOPS (Fugaku: 442 petaFLOPS), i aviat tindrem el primer supercomputador amb un rendiment màxim que sobrepassa els exaFLOPS (El Capitan: 1.5 exaFLOPS). Les tècniques d’ensemble experimenten un renaixement amb la disponibilitat d’aquestes escales tan extremes. Especialment les tècniques més noves, com els filtres de partícules, se¿n beneficiaran. Els mètodes d’ensemble actuals en climatologia, com els filtres d’ensemble de Kalman, exhibeixen una dependència lineal entre la mida del problema i la mida de l’ensemble, mentre que els filtres de partícules mostren una dependència exponencial. No obstant, juntament amb les oportunitats de poder computar massivament, apareixen desafiaments com l’alt consum energètic i la necessitat de tolerància a errors. El temps de mitjana entre errors es redueix amb el nombre de components del sistema, i s’espera que els errors s’esdevinguin cada poques hores a exaescala. En aquesta tesis, explorem i desenvolupem tècniques per protegir grans càlculs d’ensemble d’errors. Presentem noves tècniques en punts de control diferencials, recuperació elàstica, punts de control totalment asincrònics i compressió de punts de control. A més, dissenyem i implementem un filtre de partícules tolerant a errors amb captació i emmagatzematge en caché de partícules de manera preventiva. I finalment, dissenyem i implementem un marc per la validació automàtica i l’aplicació de compressió amb pèrdua en l’assimilació de dades d’ensemble. En total, en aquesta tesis presentem cinc contribucions, les dues primeres de les quals milloren les tècniques de punts de control més avançades, mentre que les tres restants aborden la resiliència dels càlculs d’ensemble. Les contribucions representen tècniques independents de tolerància a errors; no obstant, també es poden utilitzar per a millorar les propietats de cadascuna. Per exemple, utilitzem la recuperació elàstica (segona contribució) per a mitigar la resiliència en un marc d’assimilació de dades d’ensemble en línia (tercera contribució), i construïm el nostre marc de validació (cinquena contribució) sobre la nostra implementació del filtre de partícules (quarta contribució). A més, demostrem que les nostres contribucions milloren la resiliència i el rendiment amb experiments en diverses arquitectures, com processadors Intel, IBM i ARM.Postprint (published version