What does fault tolerant Deep Learning need from MPI?

Deep Learning (DL) algorithms have become the de facto Machine Learning (ML) algorithm for large scale data analysis. DL algorithms are computationally expensive - even distributed DL implementations which use MPI require days of training (model learning) time on commonly studied datasets. Long running DL applications become susceptible to faults - requiring development of a fault tolerant system infrastructure, in addition to fault tolerant DL algorithms. This raises an important question: What is needed from MPI for de- signing fault tolerant DL implementations? In this paper, we address this problem for permanent faults. We motivate the need for a fault tolerant MPI specification by an in-depth consideration of recent innovations in DL algorithms and their properties, which drive the need for specific fault tolerance features. We present an in-depth discussion on the suitability of different parallelism types (model, data and hybrid); a need (or lack thereof) for check-pointing of any critical data structures; and most importantly, consideration for several fault tolerance proposals (user-level fault mitigation (ULFM), Reinit) in MPI and their applicability to fault tolerant DL implementations. We leverage a distributed memory implementation of Caffe, currently available under the Machine Learning Toolkit for Extreme Scale (MaTEx). We implement our approaches by ex- tending MaTEx-Caffe for using ULFM-based implementation. Our evaluation using the ImageNet dataset and AlexNet, and GoogLeNet neural network topologies demonstrates the effectiveness of the proposed fault tolerant DL implementation using OpenMPI based ULFM

Towards zero-waste recovery and zero-overhead checkpointing in ensemble data assimilation

Ensemble data assimilation is a powerful tool for increasing the accuracy of climatological states. It is based on combining observations with the results from numerical model simulations. The method comprises two steps, (1) the propagation, where the ensemble states are advanced by the numerical model and (2) the analysis, where the model states are corrected with observations. One bottleneck in ensemble data assimilation is circulating the ensemble states between the two steps. Often, the states are circulated using files. This article presents an extended implementation of Melissa-DA, an in-memory ensemble data assimilation framework, allowing zero-overhead checkpointing and recovery with few or zero recomputation. We hide the checkpoint creation using dedicated threads and MPI processes. We benchmark our implementation with up to 512 members simulating the Lorenz96 model using 10 9 gridpoints. We utilize up to 8 K processes and 8 TB of checkpoint data per cycle and reach a peak performance of 52 teraFLOPS.Part of the research presented here has received funding from the Horizon 2020 (H2020) funding framework under grant/award number: 824158; Energy oriented Centre of Excellence II (EoCoE-II).Peer ReviewedPostprint (author's final draft

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

Deduplication potential of HPC applications' checkpoints

© 2016 IEEE. HPC systems contain an increasing number of components, decreasing the mean time between failures. Checkpoint mechanisms help to overcome such failures for long-running applications. A viable solution to remove the resulting pressure from the I/O backends is to deduplicate the checkpoints. However, there is little knowledge about the potential to save I/Os for HPC applications by using deduplication within the checkpointing process. In this paper, we perform a broad study about the deduplication behavior of HPC application checkpointing and its impact on system design

Enabling On-Demand Database Computing with MIT SuperCloud Database Management System

The MIT SuperCloud database management system allows for rapid creation and flexible execution of a variety of the latest scientific databases, including Apache Accumulo and SciDB. It is designed to permit these databases to run on a High Performance Computing Cluster (HPCC) platform as seamlessly as any other HPCC job. It ensures the seamless migration of the databases to the resources assigned by the HPCC scheduler and centralized storage of the database files when not running. It also permits snapshotting of databases to allow researchers to experiment and push the limits of the technology without concerns for data or productivity loss if the database becomes unstable.Comment: 6 pages; accepted to IEEE High Performance Extreme Computing (HPEC) conference 2015. arXiv admin note: text overlap with arXiv:1406.492