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With the advent of more powerful Quantum Computers, the need for larger Quantum Simulations has boosted. As the amount of resources grows exponentially with size of the target system Tensor Networks emerge as an optimal framework with which we represent Quantum States in tensor factorizations. As the extent of a tensor network increases, so does the size of intermediate tensors requiring HPC tools for their manipulation. Simulations of medium-sized circuits cannot fit on local memory, and solutions for distributed contraction of tensors are scarce. In this work we present RosneT, a library for distributed, out-of-core block tensor algebra. We use the PyCOMPSs programming model to transform tensor operations into a collection of tasks handled by the COMPSs runtime, targeting executions in existing and upcoming Exascale supercomputers. We report results validating our approach showing good scalability in simulations of Quantum circuits of up to 53 qubits., 8 pages, 13 figures

Task-based programming models offer a flexible way to express the unstructured parallelism patterns of nowadays complex applications. This expressive capability is required to achieve maximum possible performance for applications that are executed in distributed execution platforms. In current task-based workflows, tasks are launched for execution when their data dependencies are satisfied. However, even though the data dependencies of a certain task might have been already produced, the execution of this task will be delayed until its predecessor tasks completely finish their execution. As a consequence of this approach of releasing dependencies, the amount of parallelism inherent in applications is limited and performance improvement opportunities are wasted. To mitigate this limitation, we propose an eager approach for releasing data dependencies. Following this approach, the execution of tasks will not be delayed until their predecessor tasks completely finish their execution, instead, tasks will be launched for execution as soon as their data requirements are available. Hence, more parallelism is exposed and applications can achieve higher levels of performance by overlapping the execution of tasks. Towards achieving this goal, in this paper we propose applying two changes to task-based workflow systems. First, modifying the dependency relationships of tasks to be specified not only in terms of predecessor and successor tasks but also in terms of the data that caused these dependencies. Second, triggering the release of dependencies as soon as a predecessor task generates the output data instead of having to wait until the end of the predecessor execution to release all of its dependencies. We realize this proposal using PyCOMPSs: a task-based programming model for parallelizing Python applications. Our experiments show that using an eager approach for releasing dependencies achieves more than 50% performance improvement in the total execution time as compared to the default approach of releasing dependencies. This work is partially supported by the European Union through the Horizon 2020 research and innovation programme under contracts 721865 (EXPERTISE Project) by the Spanish Government (SEV2015-0493,TIN2015-65316-P) and the Generalitat de Catalunya (contract 2014-SGR1051).

Progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources that follow a cycle composed of pre-processing steps for data curation and preparation for subsequent computing steps, and later analysis and analytics steps applied to the results. However, scientific workflows are currently fragmented in multiple components, with different processes for computing and data management, and with gaps in the viewpoints of the user profiles involved. Our vision is that future workflow environments and tools for the development of scientific workflows should follow a holistic approach, where both data and computing are integrated in a single flow built on simple, high-level interfaces. The topics of research that we propose involve novel ways to express the workflows that integrate the different data and compute processes, dynamic runtimes to support the execution of the workflows in complex and heterogeneous computing infrastructures in an efficient way, both in terms of performance and energy. These infrastructures include highly distributed resources, from sensors and instruments, and devices in the edge, to High-Performance Computing and Cloud computing resources. This paper presents our vision to develop these workflow environments and also the steps we are currently following to achieve it., Comment: 10 pages, 6 figures, in proceedings of 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

The low computing power of mobile devices impedes the development of mobile applications with a heavy computing load. Mobile Cloud Computing (MCC) has emerged as the solution to this by connecting mobile devices with the “infinite” computing power of the Cloud. As mobile devices typically communicate over untrusted networks, it becomes necessary to secure the communications to avoid privacy-sensitive data breaches. This paper presents work on implementing MCC applications with secure communications. For that purpose, we built on COMPSs-Mobile, a redesigned implementation of the COMP Superscalar (COMPSs) framework aiming to MCC platorms. COMPSs-Mobile automatically exploits the parallelism inherent in an application and orchestrates its execution on loosely-coupled distributed environment. To avoid a vendor lock-in, this extension leverages on the Generic Security Services Application Program Interface (GSSAPI) (RFC2743) as a generic way to access security services to provide communications with authentication, secrecy and integrity. Besides, GSSAPI allows applications to take profit of more advanced features, such as Federated Identity or Single Sign-On, which the underlying security framework could provide. To validate the practicality of the proposal, we use Kerberos as the security services provider to implement SSO; however, applications do not authenticate themselves and require users to obtain and place the credentials beforehand. To evaluate the performance, we conducted some tests running an application on a smartphone offloading tasks to a private cloud. Our results show that the overhead of securing the communications is acceptable., This work has been supported by the Spanish Government (contracts TIN2012-34557, TIN2015-65316-P and grants BES-2013-067167, EEBB-I-15-09808 of the Research Training Program and SEV-2011-00067 of Severo Ochoa Program), by Generalitat de Catalunya (contract 2014-SGR-1051) and by the European Commission (ASCETiC project, FP7-ICT-2013.1.2 contract 610874). The second author was partially supported by the European Commission’s Horizon2020 program under grant agreement 653965 (AARC).

Day after day, cloud technologies are more and more adopted by very diverse types of stakeholders, and this success creates a side-effect problem: the energy spent by this kind of infrastructures is growing bigger every day. With the objective of reducing energy consumption when programming applications for cloud infrastructures, we have implemented energy-aware mechanisms in the COMPSs Programming Model, inside the context of the ASCETiC Project. In this paper, we demonstrate that application-level scheduling can have a big impact on the energy consumed by an application when executed in a heterogeneous cloud. We have implemented an energy-aware scheduling mechanism in COMPSs, together with a versioning technique, and we have run experiments with a use case coming from the real estate sector that proves our hypotheses.

The advent of Cloud and the popularization of mobile devices have led us to a shift in computing access. Computing users will have an interaction display while the real computation will be performed remotely, in the Cloud. COMPSs-Mobile is a framework that aims to ease the development of energy-efficient and high-performing applications for this environment. The framework provides an infrastructure-unaware programming model that allows developers to code regular Android applications that, transparently, are parallelized, and partially offloaded to remote resources. This paper gives an overview of the programming model and describes the internal components of the toolkit which supports it focusing on the offloading and checkpointing mechanisms. It also presents the results of some tests conducted to evaluate the behavior of the solution and to measure the potential benefits in Android applications.

COMPSs is a programming framework that aims to facilitate the parallelization of existing applications written in Java, C/C++ and Python scripts. For that purpose, it offers a simple programming model based on sequential development in which the user is mainly responsible for identifying the functions to be executed as asynchronous parallel tasks and annotating them with annotations or standard Python decorators. A runtime system is in charge of exploiting the inherent concurrency of the code, automatically detecting and enforcing the data dependencies between tasks and spawning these tasks to the available resources, which can be nodes in a cluster, clouds or grids. In cloud environments, COMPSs provides scalability and elasticity features allowing the dynamic provision of resources. This work has been supported by the following institutions: the Spanish Government with grant SEV-2011-00067 of the Severo Ochoa Program and contract Computacion de Altas Prestaciones VI (TIN2012-34557); by the SGR programme (2014-SGR-1051) of the Catalan Government; by the project The Human Brain Project, funded by the European Commission under contract 604102; by the ASCETiC project funded by the European Commission under contract 610874; by the EUBrazilCloudConnect project funded by the European Commission under contract 614048; and by the Intel-BSC Exascale Lab collaboration.

The rise of virtualized and distributed infrastructures has led to new challenges to accomplish the effective use of compute resources through the design and orchestration of distributed applications. As legacy, monolithic applications are replaced with service-oriented applications, questions arise about the steps to be taken in order to maximize the usefulness of the infrastructures and to provide users with tools for the development and execution of distributed applications. One of the issues to be solved is the existence of multiple cloud solutions that are not interoperable, which forces the user to be locked to a specific provider or to continuously adapt applications. With the objective of simplifying the programmers challenges, ServiceSs provides a straightforward programming model and an execution framework that helps on abstracting applications from the actual execution environment. This paper presents how ServiceSs transparently interoperates with multiple providers implementing the appropriate interfaces to execute scientific applications on federated clouds. © 2013 Springer Science+Business Media Dordrecht., This work has been supported by the Spanish Ministry of Science and Innovation (contracts TIN2012-34557, TIN2007-60625, CSD2007-00050 and CAC2007-00052), by Generalitat de Catalunya (contract 2009-SGR-980), by the European Commission (OPTIMIS project, Grant Agreement Number: 257115, EUBrazilOpenBio project, Grant agreement no. 288754) and by the grant SEV-2011-00067 of Severo Ochoa Program, awarded by the Spanish Government

The advent of Cloud computing has given to researchers the ability to access resources that satisfy their growing needs, which could not be satisfied by traditional computing resources such as PCs and locally managed clusters. On the other side, such ability, has opened new challenges for the execution of their computational work and the managing of massive amounts of data into resources provided by different private and public infrastructures. COMP Superscalar (COMPSs) is a programming framework that provides a programming model and a runtime that ease the development of applications for distributed environments and their execution on a wide range of computational infrastructures. COMPSs has been recently extended in order to be interoperable with several cloud technologies like Amazon, OpenNebula, Emotive and other OCCI compliant offerings. This paper presents the extensions of this interoperability layer to support the execution of COMPSs applications into the Windows Azure Platform. The framework has been evaluated through the porting of a data mining workflow to COMPSs and the execution on an hybrid testbed.

While object-oriented programming (OOP) and parallelism originated as separate areas, there have been many attempts to bring those paradigms together. Few of them, though, meet the challenge of programming for parallel architectures and distributed platforms: offering good development expressiveness while not hindering application performance. This work presents the introduction of OOP in a parallel programming model for Java applications which targets productivity. In this model, one can develop a Java application in a totally sequential fashion, without using any new library or language construct, thus favouring programmability. We show how this model offers a good trade-off between ease of programming and runtime performance. A comparison with other approaches is provided, evaluating the key aspects of the model and discussing some results for a set of the NAS parallel benchmarks.

The aim of the AI-SPRINT project is to implement a design and runtime framework to accelerate the development of AI applications whose components are spread across the edge-cloud computing continuum. AI-SPRINT tools will allow trading-off application performance (in terms of end-to-end latency or throughput), energy efficiency, and AI models accuracy while providing security and privacy guarantees. This document, in particular, describes the first release and evaluation of the design tools of the AI-SPRINT platform, while the second release and evaluation are due at M24. The first release includes programming abstractions that hide the communications across components and transparently implement the parallelization of the compute-intensive part of the application; quality annotations to enrich applications with constraints on performance and security; performance models to support AI components execution time prediction (both for inference and training tasks); AI Models Network Architecture Search providing solutions to enable developers with limited Machine Learning (ML) expertise to train high-quality models specific to their needs also in terms of Quality of Service (QoS) requirements; Applications design space exploration tools to evaluate multiple alternative candidate deployments for complex applications involving many components. The main results of this release include: The implementation of a prototype application for the AI-SPRINT Healthcare use case that adopts a parallel version of the Cascade Support Vector Machine (CSVM) algorithm to train a model that classifies a dataset to identify atrial fibrillations possibly related to the heart stroke. This implementation adopts the programming model of AI-SPRINT using PyCOMPSs and the dislib distributed ML library, reaching an average f1-score of 0.667 in discriminating atrial fibrillation from normal recordings using the time-frequency features of a balanced selection of the PhysioNet CinC Challenge 2017 available data with shuffled 80/20 training and test sets splits repeated 10 times. The design of QoS annotations corresponding to performance and security constraints. The AISPRINT API will include parsing tools which will extract the decorated functions from the code of the application and produce semi-automatically1 the input required by the AI-SPRINT toolchain (including design, deployment and monitoring). Tools for the definition of performance models and profiling tools based on a ML library, that allows to predict the execution time of inference and training jobs, through feature selection, and hyperparameter tuning. A component for automatic neural architecture search (POPNAS) to automate the search for the best Deep Neural Network (DNN) architecture for a given classification/regression task. Also leveraging the performance models, the algorithm searches for the best network configuration to achieve the higher accuracy in the lowest possible time by searching the Pareto front of the time-accuracy trade-off. A tool (SPACE4AI-D) for the automatic design space exploration in order to minimize the execution cost of the AI application while providing response time guarantees. The output of this tool determines the optimal component placement, resource selection and the optimal number of nodes/VMs which helps the developer to find the optimal placement. For each of the above-mentioned components, an evaluation section is also provided to discuss preliminary results on the adopted technologies and to evaluate the integration of the components according to the project’s milestones., {"references":["Project deliverable","Open Access"]}

The aim of the AI-SPRINT “Artificial intelligence in Secure PRIvacy-preserving computing coNTinuum” project is to develop a platform composed of design and runtime management tools to seamlessly design, partition and operate Artificial Intelligence (AI) applications among the current plethora of cloud-based solutions and AI-based sensor devices (i.e., devices with intelligence and data processing capabilities), providing resource efficiency, performance, data privacy, and security guarantees. The aim of this deliverable is to review the state-of-the-art in techniques used in the development and operation of AI applications in computing continua and the related technologies. The deliverable starts with an overview of AI applications and edge computing market trends. It examines the standardisation and opensource landscape, where many of the emerging standards are mainly focused on the network layer, also highlighting opportunities for open-source community involvement to expand technology availability. Then, the deliverable provides a background on AI applications design, also considering some advanced design trends (e.g., Network Architecture Search, Federated Learning, Deep Neural Networks partitioning) introducing the software development frameworks and hardware solutions which allow to run such applications in a computing continuum. We then report an overview of the state-of-the-art of the solutions offered by the main cloud market players and the European providers. We also extensively discuss existing solutions for applications deployment, monitoring, runtime management, and scheduling considering the emerging Function as a Service paradigm. In the last part of the deliverable, we report an overview of the performance modelling solutions, security, and privacy problems for AI applications in edge environments. It is found that some areas, such as component placement and design space exploration with privacy preservation and performance guarantees, are fairly under-developed, since solutions tailored for AI applications are lacking, and thus offer an opportunity for innovation., {"references":["Project deliverable","Open Access"]}