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See detailRESIF 3.0: Toward a Flexible & Automated Management of User Software Environment on HPC facility
Varrette, Sébastien UL; Kieffer, Emmanuel UL; Pinel, Frederic UL et al

in ACM Practice and Experience in Advanced Research Computing (PEARC'21) (2021, July)

High Performance Computing (HPC) is increasingly identified as a strategic asset and enabler to accelerate the research and the business performed in all areas requiring intensive computing and large ... [more ▼]

High Performance Computing (HPC) is increasingly identified as a strategic asset and enabler to accelerate the research and the business performed in all areas requiring intensive computing and large-scale Big Data analytic capabilities. The efficient exploitation of heterogeneous computing resources featuring different processor architectures and generations, coupled with the eventual presence of GPU accelerators, remains a challenge. The University of Luxembourg operates since 2007 a large academic HPC facility which remains one of the reference implementation within the country and offers a cutting-edge research infrastructure to Luxembourg public research. The HPC support team invests a significant amount of time (i.e., several months of effort per year) in providing a software environment optimised for hundreds of users, but the complexity of HPC software was quickly outpacing the capabilities of classical software management tools. Since 2014, our scientific software stack is generated and deployed in an automated and consistent way through the RESIF framework, a wrapper on top of Easybuild and Lmod [5] meant to efficiently handle user software generation. A large code refactoring was performed in 2017 to better handle different software sets and roles across multiple clusters, all piloted through a dedicated control repository. With the advent in 2020 of a new supercomputer featuring a different CPU architecture, and to mitigate the identified limitations of the existing framework, we report in this state-of-practice article RESIF 3.0, the latest iteration of our scientific software management suit now relying on streamline Easybuild. It permitted to reduce by around 90% the number of custom configurations previously enforced by specific Slurm and MPI settings, while sustaining optimised builds coexisting for different dimensions of CPU and GPU architectures. The workflow for contributing back to the Easybuild community was also automated and a current work in progress aims at drastically decrease the building time of a complete software set generation. Overall, most design choices for our wrapper have been motivated by several years of experience in addressing in a flexible and convenient way the heterogeneous needs inherent to an academic environment aiming for research excellence. As the code base is available publicly, and as we wish to transparently report also the pitfalls and difficulties met, this tool may thus help other HPC centres to consolidate their own software management stack. [less ▲]

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See detailProtection of Personal Data in High Performance Computing Platform for Scientific Research Purposes
Paseri, Ludovica; Varrette, Sébastien UL; Bouvry, Pascal UL

in Proc. of the EU Annual Privacy Forum (APF) 2021 (2021, June)

The Open Science projects are also aimed at strongly encouraging the use of Cloud technologies and High Performance Computing (HPC), for the benefit of European researchers and universities. The emerging ... [more ▼]

The Open Science projects are also aimed at strongly encouraging the use of Cloud technologies and High Performance Computing (HPC), for the benefit of European researchers and universities. The emerging paradigm of Open Science enables an easier access to expert knowledge and material; however, it also raises some challenges regarding the protection of personal data, considering that part of the research data are personal data thus subjected to the EU’s General Data Protection Regulation (GDPR). This paper investigates the concept of scientific research in the field of data protection, with regard both to the European (GDPR) and national (Luxembourg Data Protection Law) legal framework for the compliance of the HPC technology. Therefore, it focuses on a case study, the HPC platform of the University of Luxembourg (ULHPC), to pinpoint the major data protection issues arising from the processing activities through HPC from the perspective of the HPC platform operators. Our study illustrates where the most problematic aspects of compliance lie. In this regard, possible solutions are also suggested, which mainly revolve around (1) standardisation of procedures; (2) cooperation at institutional level; (3) identification of guidelines for common challenges. This research is aimed to support legal researchers in the field of data protection, in order to help deepen the understanding of HPC technology’s challenges and universities and research centres holding an HPC platform for research purposes, which have to address the same issues. [less ▲]

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See detailPRACE Best Practice Guide 2021: Modern Accelerators
Bispo, João; Barbosa, Jorge G.; Filipe Silva, Pedro et al

Report (2021)

Hardware accelerators are special types of elements designed for boosting the performance of certain application regions requiring large amounts of numerical computations. Several factors contributed to ... [more ▼]

Hardware accelerators are special types of elements designed for boosting the performance of certain application regions requiring large amounts of numerical computations. Several factors contributed to broadening the use and furthering the adoption of these technologies in High-Performance Computing (HPC). One of such is the offered greater computational throughput as compared to stand-alone Central Processing Units (CPUs), which is driven by the highly parallel architectural design of accelerators. This is particularly important in the current era of ever-increasing computational demands featuring high reuse rates of compute-intensive operational patterns. Another contributing factor is that these specialized chips are also capable of delivering much higher compute performance as compared to CPUs under the same power budget, making these technologies even more appealing for system vendors and users. All these led HPC manufacturers and integrators to unleash further the potential of hardware accelerators for delivering the required compute performance more efficiently. In fact, this is one of the main reasons that the current Top500 list [1] continues to be enriched with various accelerated systems. The next generation of HPC systems will also see a considerable amount of accelerator technology used. As a matter of fact, two out of the three European High-Performance Computing Joint Undertaking (EuroHPC JU) [2] pre-exascale HPC sites have already announced that their supercomputers will be equipped with large amount of Graphics Processing Units (GPUs). Thus, in order to achieve a competitive application performance and to be able to use the underlying hardware infrastructure efficiently, HPC application developers should be familiar with various challenges associated with using and orchestrating vast amounts of accelerator devices while being acquainted with the available ecosystem of the supporting tools. This Best Practice Guide (BPG) extends the previously developed series of BPGs [3] by providing an update on new accelerator technologies to further support the European HPC user community in achieving outstanding performance records of their large-scale parallel applications. This guide follows the style of the previously published guide on "Modern Processors" [4], by providing a hybrid approach of a field guide and a textbook. The aim of this BPG is not to replace any of the available in depth textbooks and/or documentations of certain tools, but rather to provide a set of best practices that build upon the available literature and the expertise of authors involved to further ease the process of application porting and performance optimisation. This guide showcases the usability and possibilities of further application tuning given a specific accelerator technology, and does not provide any direct comparisons of different accelerator technologies involved. The guide provides a generic overview on various accelerators and their accompanying programming models/environments and thus should be viewed as complementary to the existing in-depth BPGs provided by hardware vendors that are typically specific to their own product. [less ▲]

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See detailUni.lu HPC Annual Report 2020
Varrette, Sébastien UL

Report (2021)

2020 was a challenging year for everyone that will stay in our memory. The pandemic disrupted our economies, societies, and all our best laid-out plans. However, COVID-19 also taught us several lessons ... [more ▼]

2020 was a challenging year for everyone that will stay in our memory. The pandemic disrupted our economies, societies, and all our best laid-out plans. However, COVID-19 also taught us several lessons for the future, in particular the (real) necessity to adapt, to be nimble and to expect the unexpected while supporting cutting-edge excellence in science with the best performing and most flexible tools to unleash research potential. One thing is certain - the strategic developments for accelerated digitalisation and the role that HPC will play to ensure a smarter and more connected University will be in focus in 2021 and the years to come. 2020 was thus a very fruitful and productive year for the ULHPC team which has seen unprecedented changes and challenges. [less ▲]

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See detailEdge Computing: An Overview of Framework and Applications
Krishnasamy, Ezhilmathi UL; Varrette, Sébastien UL; Mucciardi, Michael

Report (2020)

This report gives an overview of the Edge Computing paradigm and its applications. Indeed, with the advent of the Internet of Things (IoT) era, many electronic devices and sensors produce a vast volume of ... [more ▼]

This report gives an overview of the Edge Computing paradigm and its applications. Indeed, with the advent of the Internet of Things (IoT) era, many electronic devices and sensors produce a vast volume of data which should be processed in a timely manner and this novel computing model is nowadays seen as a pertinent answer to this open challenge. This report thus explains why Edge Computing is needed and how the edge architecture is typically structured. It further presents the technologies that help this cutting-edge model to function properly. Since Edge Computing involves a heterogeneous architecture, it requires to adapt to a few technological recommendations for optimal performance. In this context, this report reviews the latest hardware technology trends tied to Edge Computing developments and points out technical challenges implementing this innovative computing model. In particular, we analyse how High-Performance Computing and CloudComputing infrastructures can be efficiently organised to design an Edge Computing-based framework able to tackle cutting-edge issues solved by Artificial Intelligence techniques. Finally, this report presents selected real-world applications of the Edge Computing paradigm across multiple domains affecting our daily life, i.e., healthcare, smart city and grids, industry 4.0 and public safety [less ▲]

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See detailPRACE Best Practice Guide 2020: Modern Processors
Saastad, Ole Widar; Kapanova, Kristina; Markov, Stoyan et al

Report (2020)

This Best Practice Guide (BPG) extends the previously developed series of BPGs by providing an update on new technologies and systems for the further support of European High Performance Computing (HPC ... [more ▼]

This Best Practice Guide (BPG) extends the previously developed series of BPGs by providing an update on new technologies and systems for the further support of European High Performance Computing (HPC) user community in achieving a remarkable performance of their large-scale applications. It covers existing systems and aims to provide support for scientists to port, build and run their applications on these systems. While some benchmarking is part of this guide, the results provided are mainly an illustration of the different systems characteristics, and should not be used as guides for the comparison of systems presented nor should be used for system procurement considerations. Procurement and benchmarking are well covered by other PRACE work packages and are out of this BPG's discussion scope. This BPG document has grown to be a hybrid of field guide and a textbook approach. The system and processor coverage provide some relevant technical information for the users who need a deeper knowledge of the system in order to fully utilise the hardware. While the field guide approach provides hints and starting points for porting and building scientific software. For this, a range of compilers, libraries, debuggers, performance analysis tools, etc. are covered. While recommendation for compilers, libraries and flags are covered we acknowledge that there is no magic bullet as all codes are different. Unfortunately there is often no way around the trial and error approach. Some in-depth documentation of the covered processors is provided. This includes some background on the inner workings of the processors considered; the number of threads each core can handle; how these threads are implemented and how these threads (instruction streams) are scheduled onto different execution units within the core. In addition, this guide describes how the vector units with different lengths (256, 512 or in the case of SVE - variable and generally unknown until execution time) are implemented. As most of HPC work up to now has been done in 64 bit floating point the emphasis is on this data type, specially for vectors. In addition to the processor executing units, memory in its many levels of hierarchy is important. The different implementations of Non-Uniform Memory Access (NUMA) are also covered in this BPG. The guide gives a description of the hardware for a selection of relevant processors currently deployed in some PRACE HPC systems. It includes ARM64(Huawei/HiSilicon and Marvell) and x86-64 (AMD and Intel). It provides information on the programming models and development environment as well as information about porting programs. Furthermore it provides sections about strategies on how to analyze and improve the performance of applications. While this guide does not provide an update on all recent processors, some of the previous BPG releases do cover other processor architectures not discussed in this guide (e.g. Power architecture) and should be considered as a staring point for work. This guide aims also to increase the user awareness on energy and power consumption of individual applications by providing some analysis on usefulness of maximum CPU frequency scaling based on the type of application considered (e.g. CPU-bound, memory-bound, etc.). [less ▲]

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See detailPerformance Analysis of Distributed and Scalable Deep Learning
Mahon, S.; Varrette, Sébastien UL; Plugaru, Valentin UL et al

in 20th IEEE/ACM Intl. Symp. on Cluster, Cloud and Internet Computing (CCGrid'20) (2020, May)

With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). More ... [more ▼]

With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). More recently, a subset of ML known as Deep Learning (DL) raised an increased interest due to its inherent ability to tackle efficiently novel cognitive computing applications. DL allows computational models that are composed of multiple processing layers to learn in an automated way representations of data with multiple levels of abstraction, and can deliver higher predictive accuracy when trained on larger data sets. Based on Artificial Neural Networks (ANN), DL is now at the core of state of the art voice recognition systems (which enable easy control over e.g. Internet-of- Things (IoT) smart home appliances for instance), self-driving car engine, online recommendation systems. The ecosystem of DL frameworks is fast evolving, as well as the DL architectures that are shown to perform well on specialized tasks and to exploit GPU accelerators. For this reason, the frequent performance evaluation of the DL ecosystem is re- quired, especially since the advent of novel distributed training frameworks such as Horovod allowing for scalable training across multiple computing resources. In this paper, the scalability evaluation of the reference DL frameworks (Tensorflow, Keras, MXNet, and PyTorch) is performed over up-to-date High Performance Comput- ing (HPC) resources to compare the efficiency of differ- ent implementations across several hardware architectures (CPU and GPU). Experimental results demonstrate that the DistributedDataParallel features in the Pytorch library seem to be the most efficient framework for distributing the training process across many devices, allowing to reach a throughput speedup of 10.11 when using 12 NVidia Tesla V100 GPUs when training Resnet44 on the CIFAR10 dataset. [less ▲]

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See detailEvolving a Deep Neural Network Training Time Estimator
Pinel, Frédéric UL; Yin, Jian-xiong; Hundt, Christian UL et al

in Communications in Computer and Information Science (2020, February)

We present a procedure for the design of a Deep Neural Net- work (DNN) that estimates the execution time for training a deep neural network per batch on GPU accelerators. The estimator is destined to be ... [more ▼]

We present a procedure for the design of a Deep Neural Net- work (DNN) that estimates the execution time for training a deep neural network per batch on GPU accelerators. The estimator is destined to be embedded in the scheduler of a shared GPU infrastructure, capable of providing estimated training times for a wide range of network architectures, when the user submits a training job. To this end, a very short and simple representation for a given DNN is chosen. In order to compensate for the limited degree of description of the basic network representation, a novel co-evolutionary approach is taken to fit the estimator. The training set for the estimator, i.e. DNNs, is evolved by an evolutionary algorithm that optimizes the accuracy of the estimator. In the process, the genetic algorithm evolves DNNs, generates Python-Keras programs and projects them onto the simple representation. The genetic operators are dynamic, they change with the estimator’s accuracy in order to balance accuracy with generalization. Results show that despite the low degree of information in the representation and the simple initial design for the predictor, co-evolving the training set performs better than near random generated population of DNNs. [less ▲]

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See detailAutomatic Software Tuning of Parallel Programs for Energy-Aware Executions
Varrette, Sébastien UL; Pinel, Frédéric UL; Kieffer, Emmanuel UL et al

in Proc. of 13th Intl. Conf. on Parallel Processing and Applied Mathematics (PPAM 2019) (2019, December)

For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to ... [more ▼]

For large scale systems, such as data centers, energy efficiency has proven to be key for reducing capital, operational expenses and environmental impact. Power drainage of a system is closely related to the type and characteristics of workload that the device is running. For this reason, this paper presents an automatic software tuning method for parallel program generation able to adapt and exploit the hardware features available on a target computing system such as an HPC facility or a cloud system in a better way than traditional compiler infrastructures. We propose a search based approach combining both exact methods and approximated heuristics evolving programs in order to find optimized configurations relying on an ever-increasing number of tunable knobs i.e., code transformation and execution options (such as the num- ber of OpenMP threads and/or the CPU frequency settings). The main objective is to outperform the configurations generated by traditional compiling infrastructures for selected KPIs i.e., performance, energy and power usage (for both for the CPU and DRAM), as well as the runtime. First experimental results tied to the local optimization phase of the proposed framework are encouraging, demonstrating between 8% and 41% improvement for all considered metrics on a reference benchmark- ing application (i.e., Linpack). This brings novel perspectives for the global optimization step currently under investigation within the presented framework, with the ambition to pave the way toward automatic tuning of energy-aware applications beyond the performance of the current state-of-the-art compiler infrastructures. [less ▲]

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See detailLes blockchains en 50 questions: comprendre le fonctionnement et les enjeux de cette technologie innovante
Dumas, J.-G.; Lafourcade, P.; Tichit, A. et al

Book published by Dunod - 2eme (2019)

Une blockchain (chaîne de blocs) est une application informatique qui utilise des techniques cryptographiques permettant à des entités de réaliser entre elles des opérations sans l'intervention d'un tiers ... [more ▼]

Une blockchain (chaîne de blocs) est une application informatique qui utilise des techniques cryptographiques permettant à des entités de réaliser entre elles des opérations sans l'intervention d'un tiers de confiance (banques, notaires...). Les blockchains se répandent dans l'économie sous forme par exemple de nouvelles monnaies (bitcoins, ether...), mais aussi de contrats ou de certifications dans les assurances, dans les affaires (smart contracts), dans le droit... Ces technologies de rupture ne sont pas simples à comprendre que ce soit dans leur fonctionnement informatique (horodatage et immutabilité des blocs) ou dans leurs conséquences pour l'utilisateur (valeur de la preuve, recours possibles...). La meilleure façon d'aborder ce sujet complexe est de répondre point par point aux multiples questions que les utilisateurs potentiels se posent. [less ▲]

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See detailBest Practices for Cloud Migration and Service Level Agreement Compliances
Ibrahim, Abdallah Ali Zainelabden Abdallah UL; Varrette, Sébastien UL; Niessen, Frederic

Report (2019)

Dell Technologies is one of the oldest Information Technology (IT) companies that involved in ICT transformation. ICT transformation is the process of modifying and adjusting the companies IT systems and ... [more ▼]

Dell Technologies is one of the oldest Information Technology (IT) companies that involved in ICT transformation. ICT transformation is the process of modifying and adjusting the companies IT systems and infrastructure. IT transformation is a multi-layer interdisciplinary process which involves typically changes to network architecture, hardware, software, data protection, i.e how data is stored and accessed. The transformation of the business workload and IT systems is the process of rip and replace and Dell is now aiming at guiding their customers in this challenging process. Indeed, Dell Technologies is providing a broad range of IT solutions and services such as data storage, protection, servers and infrastructure, networking, and cloud solutions. Concerning the last type of offer, Dell is providing public, private and hybrid cloud solutions and also coupled with cloud consulting and management services. In this context, the main objective of this work is to help Dell Technologies to come up with a guidelines document for its customers detailing the standards, migration procedures and the importance of smart Information and Communications Technology (ICT) (Cloud Computing (CC)) involved in business transition towards cloud-based solutions. Of course, it is intended for this document to serve as a fair basis to evaluate the offers of multiple cloud providers, while helping to understand the provided Service Level Agreements (SLAs) and the way they are enforced and evaluated by using the International Organization for Standardization (ISO) standards’ such as ISO/IEC DIS 19086, Information Technology (IT)- CC Service Level Agreement (SLA) framework and ISO/IEC DIS 22624, IT- CC taxonomy based data handling for cloud services [less ▲]

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See detailAmazon Elastic Compute Cloud (EC2) versus In-House HPC Platform: A Cost Analysis
Emeras, Joseph; Varrette, Sébastien UL; Plugaru, Valentin UL et al

in IEEE Transactions on Cloud Computing (2019), 7(2), 456-468

Abstract—While High Performance Computing (HPC) centers continuously evolve to provide more computing power to their users, we observe a wish for the convergence between Cloud Computing (CC) and High ... [more ▼]

Abstract—While High Performance Computing (HPC) centers continuously evolve to provide more computing power to their users, we observe a wish for the convergence between Cloud Computing (CC) and High Performance Computing (HPC) platforms, with the commercial hope to see Cloud Computing (CC) infrastructures to eventually replace in-house facilities. If we exclude the performance point of view where many previous studies highlight a non-negligible overhead induced by the virtualization layer at the heart of every Cloud middleware when running a HPC workload, the question of the real cost-effectiveness is often left aside with the intuition that, most probably, the instances offered by the Cloud providers are competitive from a cost point of view. In this article, we wanted to assert (or infirm) this intuition by analyzing what composes the Total Cost of Ownership (TCO) of an in-house HPC facility operated internally since 2007. This Total Cost of Ownership (TCO) model is then used to compare with the induced cost that would have been required to run the same platform (and the same workload) over a competitive Cloud IaaS offer. Our approach to address this price comparison is three-fold. First we propose a theoretical price-performance model based on the study of the actual Cloud instances proposed by one of the major Cloud IaaS actors: Amazon Elastic Compute Cloud (EC2). Then, based on the HPC facility TCO analysis we propose a hourly price comparison between our in-house cluster and the equivalent EC2 instances. Finally, based on the experimental benchmarking on the local cluster and on the Cloud instances we propose an update of the former theoretical price model to reflect the real system performance. The results obtained advocate in general for the acquisition of an in-house HPC facility, which balances the common intuition in favor of Cloud Computing platforms, would they be provided by the reference Cloud provider worldwide. [less ▲]

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See detailA Full-Cost Model for Estimating the Energy Consumption of Computing Infrastructures
Orgerie, Anne-Cecile; Varrette, Sébastien UL

in Zomaya, A. Y; Carretero, J.; Jeannot, E. (Eds.) Ultrascale Computing Systems (2019)

Since its advent in the middle of the 2000’s, the Cloud Computing (CC) paradigm is increasingly advertised as a price-effective solution to many IT problems. This seems reasonable if we exclude the pure ... [more ▼]

Since its advent in the middle of the 2000’s, the Cloud Computing (CC) paradigm is increasingly advertised as a price-effective solution to many IT problems. This seems reasonable if we exclude the pure performance point of view as many studies highlight a non-negligible overhead induced by the virtualization layer at the heart of every Cloud middleware when subjected to an High Performance Computing (HPC) workload. When this is the case, traditional HPC and Ultrascale computing systems are required, and then comes the question of the real cost-effectiveness, especially when comparing to instances offered by the Cloud providers. In this section, and inspired by the work proposed in [1], we propose a Total Cost of Ownership (TCO) analysis of an in-house academic HPC facility of medium-size (in particular the one operated at the University of Luxembourg since 2007, or within the Grid’5000 project [2]), and compare it with the investment that would have been required to run the same platform (and the same workload) over a competitive Cloud IaaS offer. [less ▲]

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See detailSecurity, reliability and regulation compliance in Ultrascale Computing System
Bouvry, Pascal UL; Varrette, Sébastien UL; Wasim, Muhammad Umer UL et al

in Zomaya, A. Y.; Carretero, J.; Jeannot, E. (Eds.) Ultrascale Computing Systems (2019)

Ultrascale Computing Systems (UCSs) are envisioned as large-scale complex systems joining parallel and distributed computing systems that will be two to three orders of magnitude larger than today’s ... [more ▼]

Ultrascale Computing Systems (UCSs) are envisioned as large-scale complex systems joining parallel and distributed computing systems that will be two to three orders of magnitude larger than today’s systems (considering the number of Central Process Unit (CPU) cores). It is very challenging to find sustainable solutions for UCSs due to their scale and a wide range of possible applications and involved technologies. For example, we need to deal with heterogeneity and cross fertilization among HPC, large-scale distributed systems, and big data management. One of the challenges regarding sustainable UCSs is resilience. Another one, which attracted less interest in the literature but becomes more and more crucial with the expected convergence with the Cloud computing paradigm, is the notion of regulation in such system to assess the Quality of Service (QoS) and Service Level Agreement (SLA) proposed for the use of these platforms. This chapter covers both aspects through the reproduction of two articles: [1] and [2]. [less ▲]

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See detailEnergy aware ultrascale systems
Oleksiak, Ariel; Lefèvre, Laurent; Alonso, Pedro et al

in Carretero, J.; Jeannot, E.; Zomaya, A.Y. (Eds.) Ultrascale Computing Systems (2019)

Energy consumption is one of the main limiting factors for the design of ultrascale infrastructures. Multi-level hardware and software optimizations must be designed and explored in order to reduce energy ... [more ▼]

Energy consumption is one of the main limiting factors for the design of ultrascale infrastructures. Multi-level hardware and software optimizations must be designed and explored in order to reduce energy consumption for these largescale equipment. This chapter addresses the issue of energy efficiency of ultrascale systems in front of other quality metrics. The goal of this chapter is to explore the design of metrics, analysis, frameworks and tools for putting energy awareness and energy efficiency at the next stage. Significant emphasis will be placed on the idea of “energy complexity,” reflecting the synergies between energy efficiency and quality of service, resilience and performance, by studying computation power, communication/data sharing power, data access power, algorithm energy consumption, etc. [less ▲]

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See detailHybrid MPI+OpenMP Implementation of eXtended Discrete Element Method
Mainassara Chekaraou, Abdoul Wahid UL; Rousset, Alban UL; Besseron, Xavier UL et al

in Proc. of the 9th Workshop on Applications for Multi-Core Architectures (WAMCA'18), part of 30th Intl. Symp. on Computer Architecture and High Performance Computing (SBAC-PAD 2018) (2018, September)

The Extended Discrete Element Method (XDEM) is a novel and innovative numerical simulation technique that ex- tends classical Discrete Element Method (DEM) (which simulates the motion of granular material ... [more ▼]

The Extended Discrete Element Method (XDEM) is a novel and innovative numerical simulation technique that ex- tends classical Discrete Element Method (DEM) (which simulates the motion of granular material), by additional properties such as the chemical composition, thermodynamic state, stress/strain for each particle. It has been applied successfully to numerous industries involving the processing of granular materials such as sand, rock, wood or coke [16], [17]. In this context, computational simulation with (X)DEM has become a more and more essential tool for researchers and scientific engineers to set up and explore their experimental processes. However, increasing the size or the accuracy of a model requires the use of High Performance Computing (HPC) platforms over a parallelized implementation to accommodate the growing needs in terms of memory and computation time. In practice, such a parallelization is traditionally obtained using either MPI (distributed memory computing), OpenMP (shared memory computing) or hybrid approaches combining both of them. In this paper, we present the results of our effort to implement an OpenMP version of XDEM allowing hybrid MPI+OpenMP simulations (XDEM being already parallelized with MPI). Far from the basic OpenMP paradigm and recommendations (which simply summarizes by decorating the main computation loops with a set of OpenMP pragma), the OpenMP parallelization of XDEM required a fundamental code re-factoring and careful tuning in order to reach good performance. There are two main reasons for those difficulties. Firstly, XDEM is a legacy code devel- oped for more than 10 years, initially focused on accuracy rather than performance. Secondly, the particles in a DEM simulation are highly dynamic: they can be added, deleted and interaction relations can change at any timestep of the simulation. Thus this article details the multiple layers of optimization applied, such as a deep data structure profiling and reorganization, the usage of fast multithreaded memory allocators and of advanced process/thread-to-core pinning techniques. Experimental results evaluate the benefit of each optimization individually and validate the implementation using a real-world application executed on the HPC platform of the University of Luxembourg. Finally, we present our Hybrid MPI+OpenMP results with a 15%-20% performance gain and how it overcomes scalability limits (by increasing the number of compute cores without dropping of performances) of XDEM-based pure MPI simulations. [less ▲]

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See detailLes blockchains en 50 questions: comprendre le fonctionnement et les enjeux de cette technologie innovante
Dumas, J.-G.; Lafourcade, P.; Tichit, A. et al

Book published by Dunod - 1er (2018)

Depuis l’avènement du bitcoin, les innovations liées à la blockchain sont en plein essor. Cet ouvrage tente d’expliquer le fonctionnement de cette technologie innovante mais aussi ses applications au ... [more ▼]

Depuis l’avènement du bitcoin, les innovations liées à la blockchain sont en plein essor. Cet ouvrage tente d’expliquer le fonctionnement de cette technologie innovante mais aussi ses applications au travers de 50 questions comme: - Qu’est-ce qu’une blockchain ? - Quel est le lien entre bitcoin et blockchains ? - Qui sont les mineurs et que font-ils ? - Qu’est ce qu’un consensus ? - Quelle est la part des cryptomonnaies dans l’économie mondiale ? - Qu’est-ce qu’un contrat intelligent ? - Peut-on faire une blockchain sans bloc ? - Comment les blockchains vont changer le monde de demain ? Les réponses à toutes ces questions et à 42 autres sont dans ce livre. [less ▲]

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See detailPRESENCE: Monitoring and Modelling the Performance Metrics of Mobile Cloud SaaS Web Services
Ibrahim, Abdallah Ali Zainelabden Abdallah UL; Wasim, Muhammad Umer UL; Varrette, Sébastien UL et al

in Mobile Information Systems (2018), 2018(1351386),

Service Level Agreements (SLAs) are defining the quality of the services delivered from the Cloud Services Providers (CSPs) to the cloud customers. The services are delivered on a pay-per-use model. The ... [more ▼]

Service Level Agreements (SLAs) are defining the quality of the services delivered from the Cloud Services Providers (CSPs) to the cloud customers. The services are delivered on a pay-per-use model. The quality of the provided services is not guaranteed by the SLA because it is just a contract. The developments around mobile cloud computing and the advent of edge computing technologies are contributing to the diffusion of the cloud services and the multiplication of offers. Although the cloud services market is growing for the coming years, unfortunately, there is no standard mechanism which exists to verify and assure that delivered services satisfy the signed SLA agreement in an automatic way. The accurate monitoring and modelling of the provided Quality of Service (QoS) is also missing. In this context, we aim at offering an automatic framework named PRESENCE, to evaluate the QoS and SLA compliance of Web Services (WSs) offered across several CSPs. Yet unlike other approaches, PRESENCE aims at quantifying in a fair and by stealth way the performance and scalability of the delivered WS. This article focuses on the first experimental results obtained on the accurate modelisation of each individual performance metrics. Indeed, 19 generated models are provided, out of which 78.9% accurately represent the WS performance metrics for two representative SaaS web services used for the validation of the PRESENCE approach. This opens novel perspectives for assessing the SLA compliance of Cloud providers using the PRESENCE framework. [less ▲]

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See detailHigh Performance Computing and Big Data analytics in Luxembourg: Overview and Challenges in the EuroHPC horizon
Besseron, Xavier UL; Varrette, Sébastien UL

Presentation (2018, August)

Accelerating modelling and simulation in the data deluge era requires the appropriate hardware and infrastructure at scale. The University of Luxembourg is active since 2007 to develop its own ... [more ▼]

Accelerating modelling and simulation in the data deluge era requires the appropriate hardware and infrastructure at scale. The University of Luxembourg is active since 2007 to develop its own infrastructure and expertise in the HPC and BD domains. The current state of developments will be briefly reviewed in the context of the national and European HPC strategy in which Luxembourg is starting to play a role. [less ▲]

Detailed reference viewed: 221 (12 UL)