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

A Kalman Based Hybrid Precoding for Multi-User Millimeter Wave MIMO Systems

Millimeter-wave communication in the 60-GHz band requires large antenna arrays at both the transmit and receive terminals to achieve beamforming gains, in order to counteract the high pathloss. Fully digital techniques are infeasible with large antenna arrays due to hardware constraints at such frequencies, while purely analog solutions suffer severe performance limitations. Hybrid analog/digital beamforming is a promising solution, especially, when extended to a multi-user scenario. This paper conveys three main contributions: 1) a Kalman-based formulation for hybrid analog/digital precoding in multi-user environment is proposed; 2) an analytical expression of the error between the transmitted and estimated data is formulated, so that the Kalman algorithm at the base station does not require information on the estimated data at the mobile stations, and instead, relies only on the precoding/combining matrix; and 3) an iterative solution is designed for the hybrid precoding scheme with affordable complexity. Simulation results confirm significant improvement of the proposed approach in terms of both bit error rate and spectral efficiency–achieving almost 7 b/s/Hz, at 20 dB with 10 channel paths with respect to the analog-only beamsteering, and almost 1 b/s/Hz with respect to the hybrid minimum mean square error precoding under the same conditions

Reduced complexity Kalman filtering for phase recovery in XPIC systems

Reduced-complexity Kalman-based algorithms are proposed to recover the phase of cross-polar interference cancellation (XPIC) receivers in microwave radio relay links. In particular, two completely independent radio frequency (RF) transceiver chains are considered for the two different polarizations, in order to have the maximum flexibility to connect different single carrier transceivers to dual-polarized antennas. A one-state Kalman model is proposed, which is of low complexity and thus suitable for a modern higher data rates M-ary quadrature amplitude modulation (M-QAM) receiver. Moreover, a further reduced complexity version is developed that uses a lower amount of information to recover the phase at the receiver, as well as a downsampling procedure to speed up the Kalman algorithm, and an alternative error computation that is essential to ease the Kalman implementation. It is worth noting that the three last simplifications are general and can be applied not only to a one-state Kalman model. Simulation results compare the proposed simplified Kalman solutions to typical phase-locked loop (PLL) algorithms proving their comparable performance with the benefit of lower complexity. Finally, the relationships between the Extended Kalman and the PLL approaches are investigated. The obtained relation is essential for the cross-polar phase recovery, since, as far as the authors know, there are not closed-form solutions for the PLL parameter optimization in cross schemes. (C) 2018 Elsevier B.V. All rights reserved

Aeronautical engineering: A cumulative index to a continuing bibliography

This bibliography is a cumulative index to the abstracts contained in NASA SP-7037 (197) through NASA SP-7037 (208) of Aeronautical Engineering: A Continuing Bibliography. NASA SP-7037 and its supplements have been compiled through the cooperative efforts of the American Institute of Aeronautics and Astronautics (AIAA) and the National Aeronautics and Space Administration (NASA). This cumulative index includes subject, personal author, corporate source, foreign technology, contract, report number, and accession number indexes

DSP algorithms for MIMO based Systems

Multiple Input Multiple Output (MIMO) systems are an emerging wireless communication technology that gained popularity due to its capability to enhance spectral efficiency and reliability. Although MIMO enhances system capacity and performance, it could be challenging due to the high number of antennas at both the transmitter and receiver. It has been therefore one of the popular research areas during the last decade, meeting ever-increasing demands of data rates. Nevertheless, serving multiple terminals simultaneously is challenging due to interference among them. The main goal of this research is to mitigate interference among users, gain better energy and spectral efficiency by employing different digital signal processing (DSP) based algorithms in the multi-user MIMO communication paradigm. Moreover, we have investigated novel algorithms in order to mitigate inter-terminal interference by employing directional beams. In order to do so, it is imperative to perform channel estimation, which can be obtained by using time or frequency duplexing, although, with an increased number of antennas in large scale MIMO (massive MIMO), the problem becomes very complicated in both types of duplexing schemes. The research problem of reducing training and feedback overhead can be addressed properly if high dimensional signals are reduced to low dimensional, by taking the compressive sensing (CS) paradigm into account. A framework is proposed to reduce training and feedback overhead by considering the MIMO channel as sparse in mobile communication. Another important issue in modern MIMO communication systems is related to phase recovery. For this purpose, a reduced complexity Kalman filtering based solution is proposed to address phase recovery problem in cross-polar interference cancellation (XPIC) system, which can be viewed as MIMO 2 x 2 channels. Another interesting application of MIMO based systems is presented for multiple implants in the intra-body network, which utilized beamforming techniques to communicate in an energy-efficient manner. The comparison with state of the art methods is also exhibited. The research work conducted in this thesis addresses theoretical, methodological and empirical contributions to MIMO based system research problem and attempted to achieve better performance by employing different DSP based algorithms.Multiple Input Multiple Output (MIMO) systems are an emerging wireless communication technology that gained popularity due to its capability to enhance spectral efficiency and reliability. Although MIMO enhances system capacity and performance, it could be challenging due to the high number of antennas at both the transmitter and receiver. It has been therefore one of the popular research areas during the last decade, meeting ever-increasing demands of data rates. Nevertheless, serving multiple terminals simultaneously is challenging due to interference among them. The main goal of this research is to mitigate interference among users, gain better energy and spectral efficiency by employing different digital signal processing (DSP) based algorithms in the multi-user MIMO communication paradigm. Moreover, we have investigated novel algorithms in order to mitigate inter-terminal interference by employing directional beams. In order to do so, it is imperative to perform channel estimation, which can be obtained by using time or frequency duplexing, although, with an increased number of antennas in large scale MIMO (massive MIMO), the problem becomes very complicated in both types of duplexing schemes. The research problem of reducing training and feedback overhead can be addressed properly if high dimensional signals are reduced to low dimensional, by taking the compressive sensing (CS) paradigm into account. A framework is proposed to reduce training and feedback overhead by considering the MIMO channel as sparse in mobile communication. Another important issue in modern MIMO communication systems is related to phase recovery. For this purpose, a reduced complexity Kalman filtering based solution is proposed to address the phase recovery problem in cross-polar interference cancellation (XPIC) system, which can be viewed as MIMO 2 x 2 channels. Another interesting application of MIMO based systems is presented for multiple implants in the intra-body network which utilized beamforming techniques to communicate in an energy-efficient manner. The comparison with state of the art methods is also exhibited. The research work conducted in this thesis addresses theoretical, methodological and empirical contributions to MIMO based system research problem and attempted to achieve better performance by employing different DSP based algorithms