DROM: Enabling Efficient and Effortless Malleability for Resource Managers

In the design of future HPC systems, research in resource management is showing an increasing interest in a more dynamic control of the available resources. It has been proven that enabling the jobs to change the number of computing resources at run time, i.e. their malleability, can significantly improve HPC system performance. However, job schedulers and applications typically do not support malleability due to the common belief that it introduces additional programming complexity and performance impact. This paper presents DROM, an interface that provides efficient malleability with no effort for program developers. The running application is enabled to adapt the number of threads to the number of assigned computing resources in a completely transparent way to the user through the integration of DROM with standard programming models, such as OpenMP/OmpSs, and MPI. We designed the APIs to be easily used by any programming model, application and job scheduler or resource manager. Our experimental results from two realistic use cases analysis, based on malleability by reducing the number of cores a job is using per node and jobs co-allocation, show the potential of DROM for improving the performance of HPC systems. In particular, the workload of two MPI+OpenMP neuro-simulators are tested, reporting improvement in system metrics, such as total run time and average response time, up to 8% and 48%, respectively.This work is partially supported by the Span- ish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project, by the Generalitat de Catalunya (contract 2017-SGR-1414) and from the European Union’s Horizon 2020 under grant agreement No 785907 (HBP SGA2)Peer ReviewedPostprint (author's final draft

RFaaS: RDMA-Enabled FaaS Platform for Serverless High-Performance Computing

The rigid MPI programming model and batch scheduling dominate high-performance computing. While clouds brought new levels of elasticity into the world of computing, supercomputers still suffer from low resource utilization rates. To enhance supercomputing clusters with the benefits of serverless computing, a modern cloud programming paradigm for pay-as-you-go execution of stateless functions, we present rFaaS, the first RDMA-aware Function-as-a-Service (FaaS) platform. With hot invocations and decentralized function placement, we overcome the major performance limitations of FaaS systems and provide low-latency remote invocations in multi-tenant environments. We evaluate the new serverless system through a series of microbenchmarks and show that remote functions execute with negligible performance overheads. We demonstrate how serverless computing can bring elastic resource management into MPI-based high-performance applications. Overall, our results show that MPI applications can benefit from modern cloud programming paradigms to guarantee high performance at lower resource costs

Gestión de recursos energéticamente eficiente para aplicaciones paralelas basadas en tareas en entornos multi-aplicación

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 28/01/2021The end of Dennard scaling, as well as the arrival of the post-Moore era, has meant a big change in the way performance and energy efficiency are achieved by modern processors. From a constant increase of the clock frequency as the main method to increase performance at the beginning of the 2000s, the increase in the number of cores inside processors running at relatively conservative frequencies has stabilised as the current trend to increase both performance and energy efficiency. The increase of the heterogeneity in the systems, both inside the processors comprising different types of cores (e.g., big LITTLE architectures) or adding specific compute units (like multimedia extensions), as well as in the platform by the addition of other specific compute units (like GPUs), offering different performance and energy-efficiency trade-offs. Together with the increase in the number of cores, the processor evolution has been accompanied by the addition of different techologies that allow processors to adapt dynamically to the changes in the environment and running aplications. Among others, techiniques like dynamic voltage and frequiency scaling, power capping or cache partitioning are widely used nowadays to increase the performance and/or energy-efficiency...El fin del escalado de Dennard, así como la llegada de la era post-Moore ha supuesto una gran revolución en la forma de obtener el rendimiento y eficiencia energética en los procesadores modernos. Desde un incremento constante en la frecuencia relativamente moderadas se ha impuesto como la tendencia actual para incrementar tanto el rendimiento como la eficiencia energética. El aumento del número de núcleos dentro del procesado ha venido acompañado en los últimos años por el aumento de la heterogeneidad en la plataforma, tanto dentro del procesador incorporando distintos tipos de núcleos en el mismo procesador (e.g., la arquitectura big.LITTLE) como añadiendo unidades de cómputo específicas (e.g., extensiones multimedia), como la incorporación de otros elementos de computo específicos, ofreciendo diferentes grados de rendimiento y eficiencia energética. La evolución de los procesadores no solo ha venido dictada por el aumento del número de núcleos, sino que ha venido acompañada por la incorporación de diferentes técnicas permitiendo la adaptación de las arquitecturas de forma dinámica al entorno así como a las aplicaciones en ejecución. Entre otras, técnicas como el escalado de frecuencia, la limitación de consumo o el particionado de la memoria caché son ampliamente utilizadas en la actualidad como métodos para incrementar el consumo y/o la eficiencia energética...Fac. de InformáticaTRUEunpu

A DYNAMIC HETEROGENEOUS MULTI-CORE ARCHITECTURE

Ph.DDOCTOR OF PHILOSOPH

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

Market-Based Resourse Management for Many-Core Systems

101 σ.Αντικείμενο της διπλωματικής αποτελεί η μελέτη και η ανάπτυξη μιας κλιμακώσιμης και κατανεμημένης πλατφόρμας (framework) διαχείρισης πόρων σε χρόνο εκτέλεσης για συστήματα πολλαπλών πυρήνων. Σε αυτήν την πλατφόρμα η διαχείριση πόρων είναι βασισμένη σε μοντέλα, τα οποία είναι εμπνευσμένα από την οικονομία. Παρουσιάζεται ένας διαχειριστής πόρων, ο οποίος προσφέρει ένα περιβάλλον διαχείρισης πόρων και εφαρμογών καθ ́ όλη τη διάρκεια ζωής τους, στο οποίο η κατανομή και δρομολόγηση των εφαρμογών στους πόρους πραγματοποιείται με αλγόριθμους βασισμένους σε κανόνες αγοράς. Η αποδοτικότητα κάθε μοντέλου αξιολογείται βάσει της πτώσης της αξιοπιστίας των πόρων (μετρική MTTF-Mean Time To Failure).The purpose of this diploma thesis is the design and development of a scalable and distributed run-time resource management framework for Many-core systems. In this framework, resource management is based on economy-inspired models. The presented resource management framework offers an environment that manages both application tasks and resources at run-time, matches and distributes application tasks across resources with algorithms which are based on market principles. The efficiency of each model is evaluated with respect to resource reliability degradation (metric MTTF-Mean Time to Failure).Θεμιστοκλής Γ. Μελισσάρη

Performance Improvement of Multithreaded Java Applications Execution on Multiprocessor Systems

El disseny del llenguatge Java, que inclou aspectes importants com són la seva portabilitat i neutralitat envers l'arquitectura, les seves capacitats multithreading, la seva familiaritat (degut a la seva semblança amb C/C++), la seva robustesa, les seves capacitats en seguretat i la seva naturalesa distribuïda, fan que sigui un llenguatge potencialment interessant per ser utilitzat en entorns paral·lels com són els entorns de computació d'altes prestacions (HPC), on les aplicacions poden treure profit del suport que ofereix Java a l'execució multithreaded per realitzar càlculs en paral·lel, o en entorns e-business, on els servidors Java multithreaded (que segueixen l'especificació J2EE) poden treure profit de les capacitats multithreading de Java per atendre de manera concurrent un gran nombre de peticions.No obstant, l'ús de Java per la programació paral·lela ha d'enfrontar-se a una sèrie de problemes que fàcilment poden neutralitzar el guany obtingut amb l'execució en paral·lel. El primer problema és el gran overhead provocat pel suport de threads de la JVM quan s'utilitzen threads per executar feina de gra fi, quan es crea un gran nombre de threads per suportar l'execució d'una aplicació o quan els threads interaccionen estretament mitjançant mecanismes de sincronització. El segon problema és la degradació en el rendiment produïda quan aquestes aplicacions multithreaded s'executen en sistemes paral·lels multiprogramats. La principal causa d'aquest problemes és la manca de comunicació entre l'entorn d'execució i les aplicacions, la qual pot induir a les aplicacions a fer un ús descoordinat dels recursos disponibles.Aquesta tesi contribueix amb la definició d'un entorn per analitzar i comprendre el comportament de les aplicacions Java multithreaded. La contribució principal d'aquest entorn és que la informació de tots els nivells involucrats en l'execució (aplicació, servidor d'aplicacions, JVM i sistema operatiu) està correlada. Aquest fet és molt important per entendre com aquest tipus d'aplicacions es comporten quan s'executen en entorns que inclouen servidors i màquines virtuals, donat que l'origen dels problemes de rendiment es pot trobar en qualsevol d'aquests nivells o en la seva interacció.Addicionalment, i basat en el coneixement adquirit mitjançant l'entorn d'anàlisis proposat, aquesta tesi contribueix amb mecanismes i polítiques de planificació orientats cap a l'execució eficient d'aplicacions Java multithreaded en sistemes multiprocessador considerant les interaccions i la coordinació dels mecanismes i les polítiques de planificació en els diferents nivells involucrats en l'execució. La idea bàsica consisteix en permetre la cooperació entre les aplicacions i l'entorn d'execució en la gestió de recursos establint una comunicació bi-direccional entre les aplicacions i el sistema. Per una banda, les aplicacions demanen a l'entorn d'execució la quantitat de recursos que necessiten. Per altra banda, l'entorn d'execució pot ser inquirit en qualsevol moment per les aplicacions ser informades sobre la seva assignació de recursos. Aquesta tesi proposa que les aplicacions utilitzin la informació proporcionada per l'entorn d'execució per adaptar el seu comportament a la quantitat de recursos que tenen assignats (aplicacions auto-adaptables). Aquesta adaptació s'assoleix en aquesta tesi per entorns HPC per mitjà de la mal·leabilitat de les aplicacions, i per entorns e-business amb una proposta de control de congestió que fa control d'admissió basat en la diferenciació de connexions SSL per prevenir la degradació del rendiment i mantenir la Qualitat de Servei (QoS).Els resultats de l'avaluació demostren que subministrar recursos de manera dinàmica a les aplicacions auto-adaptables en funció de la seva demanda millora el rendiment de les aplicacions Java multithreaded tant en entorns HPC com en entorns e-business. Mentre disposar d'aplicacions auto-adaptables evita la degradació del rendiment, el subministrament dinàmic de recursos permet satisfer els requeriments de les aplicacions en funció de la seva demanda i adaptar-se a la variabilitat de les seves necessitats de recursos. D'aquesta manera s'aconsegueix una millor utilització dels recursos donat que els recursos que no utilitza una aplicació determinada poden ser distribuïts entre les altres aplicacions.The design of the Java language, which includes important aspects such as its portability and architecture neutrality, its multithreading facilities, its familiarity (due to its resemblance with C/C++), its robustness, its security capabilities and its distributed nature, makes it a potentially interesting language to be used in parallel environments such as high performance computing (HPC) environments, where applications can benefit from the Java multithreading support for performing parallel calculations, or e-business environments, where multithreaded Java application servers (i.e. following the J2EE specification) can take profit of Java multithreading facilities to handle concurrently a large number of requests.However, the use of Java for parallel programming has to face a number of problems that can easily offset the gain due to parallel execution. The first problem is the large overhead incurred by the threading support available in the JVM when threads are used to execute fine-grained work, when a large number of threads are created to support the execution of the application or when threads closely interact through synchronization mechanisms. The second problem is the performance degradation occurred when these multithreaded applications are executed in multiprogrammed parallel systems. The main issue that causes these problems is the lack of communication between the execution environment and the applications, which can cause these applications to make an uncoordinated use of the available resources.This thesis contributes with the definition of an environment to analyze and understand the behavior of multithreaded Java applications. The main contribution of this environment is that all levels in the execution (application, application server, JVM and operating system) are correlated. This is very important to understand how this kind of applications behaves when executed on environments that include servers and virtual machines, because the origin of performance problems can reside in any of these levels or in their interaction.In addition, and based on the understanding gathered using the proposed analysis environment, this thesis contributes with scheduling mechanisms and policies oriented towards the efficient execution of multithreaded Java applications on multiprocessor systems considering the interactions and coordination between scheduling mechanisms and policies at the different levels involved in the execution. The basis idea consists of allowing the cooperation between the applications and the execution environment in the resource management by establishing a bi-directional communication path between the applications and the underlying system. On one side, the applications request to the execution environment the amount of resources they need. On the other side, the execution environment can be requested at any time by the applications to inform them about their resource assignments. This thesis proposes that applications use the information provided by the execution environment to adapt their behavior to the amount of resources allocated to them (self-adaptive applications). This adaptation is accomplished in this thesis for HPC environments through the malleability of the applications, and for e-business environments with an overload control approach that performs admission control based on SSL connections differentiation for preventing throughput degradation and maintaining Quality of Service (QoS).The evaluation results demonstrate that providing resources dynamically to self-adaptive applications on demand improves the performance of multithreaded Java applications as in HPC environments as in e-business environments. While having self-adaptive applications avoids performance degradation, dynamic provision of resources allows meeting the requirements of the applications on demand and adapting to their changing resource needs. In this way, better resource utilization is achieved because the resources not used by some application may be distributed among other applications

Methods to Improve Applicability and Efficiency of Distributed Data-Centric Compute Frameworks

The success of modern applications depends on the insights they collect from their data repositories. Data repositories for such applications currently exceed exabytes and are rapidly increasing in size, as they collect data from varied sources - web applications, mobile phones, sensors and other connected devices. Distributed storage and data-centric compute frameworks have been invented to store and analyze these large datasets. This dissertation focuses on extending the applicability and improving the efficiency of distributed data-centric compute frameworks

Optimization techniques for adaptability in MPI application

The first version of MPI (Message Passing Interface) was released in 1994. At that time, scientific applications for HPC (High Performance Computing) were characterized by a static execution environment. These applications usually had regular computation and communication patterns, operated on dense data structures accessed with good data locality, and ran on homogeneous computing platforms. For these reasons, MPI has become the de facto standard for developing scientific parallel applications for HPC during the last decades. In recent years scientific applications have evolved in order to cope with several challenges posed by different fields of engineering, economics and medicine among others. These challenges include large amounts of data stored in irregular and sparse data structures with poor data locality to be processed in parallel (big data), algorithms with irregular computation and communication patterns, and heterogeneous computing platforms (grid, cloud and heterogeneous cluster). On the other hand, over the last years MPI has introduced relevant improvements and new features in order to meet the requirements of dynamic execution environments. Some of them include asynchronous non-blocking communications, collective I/O routines and the dynamic process management interface introduced in MPI 2.0. The dynamic process management interface allows the application to spawn new processes at runtime and enable communication with them. However, this feature has some technical limitations that make the implementation of malleable MPI applications still a challenge. This thesis proposes FLEX-MPI, a runtime system that extends the functionalities of the MPI standard library and features optimization techniques for adaptability of MPI applications to dynamic execution environments. These techniques can significantly improve the performance and scalability of scientific applications and the overall efficiency of the HPC system on which they run. Specifically, FLEX-MPI focuses on dynamic load balancing and performance-aware malleability for parallel applications. The main goal of the design and implementation of the adaptability techniques is to efficiently execute MPI applications on a wide range of HPC platforms ranging from small to large-scale systems. Dynamic load balancing allows FLEX-MPI to adapt the workload assignments at runtime to the performance of the computing elements that execute the parallel application. On the other hand, performance-aware malleability leverages the dynamic process management interface of MPI to change the number of processes of the application at runtime. This feature allows to improve the performance of applications that exhibit irregular computation patterns and execute in computing systems with dynamic availability of resources. One of the main features of these techniques is that they do not require user intervention nor prior knowledge of the underlying hardware. We have validated and evaluated the performance of the adaptability techniques with three parallel MPI benchmarks and different execution environments with homogeneous and heterogeneous cluster configurations. The results show that FLEXMPI significantly improves the performance of applications when running with the support of dynamic load balancing and malleability, along with a substantial enhancement of their scalability and an improvement of the overall system efficiency.La primera versión de MPI (Message Passing Interface) fue publicada en 1994, cuando la base común de las aplicaciones científicas para HPC (High Performance Computing) se caracterizaba por un entorno de ejecución estático. Dichas aplicaciones presentaban generalmente patrones regulares de cómputo y comunicaciones, accesos a estructuras de datos densas con alta localidad, y ejecución sobre plataformas de computación homogéneas. Esto ha hecho que MPI haya sido la alternativa más adecuada para la implementación de aplicaciones científicas para HPC durante más de 20 años. Sin embargo, en los últimos años las aplicaciones científicas han evolucionado para adaptarse a diferentes retos propuestos por diferentes campos de la ingeniería, la economía o la medicina entre otros. Estos nuevos retos destacan por características como grandes cantidades de datos almacenados en estructuras de datos irregulares con baja localidad para el análisis en paralelo (big data), algoritmos con patrones irregulares de cómputo y comunicaciones, e infraestructuras de computación heterogéneas (cluster heterogéneos, grid y cloud). Por otra parte, MPI ha evolucionado significativamente en cada una de sus sucesivas versiones, siendo algunas de las mejoras más destacables presentadas hasta la reciente versión 3.0 las operaciones de comunicación asíncronas no bloqueantes, rutinas de E/S colectiva, y la interfaz de procesos dinámicos presentada en MPI 2.0. Esta última proporciona un procedimiento para la creación de procesos en tiempo de ejecución de la aplicación. Sin embargo, la implementación de la interfaz de procesos dinámicos por parte de las diferentes distribuciones de MPI aún presenta numerosas limitaciones que condicionan el desarrollo de aplicaciones maleables en MPI. Esta tesis propone FLEX-MPI, un sistema que extiende las funcionalidades de la librería MPI y proporciona técnicas de optimización para la adaptación de aplicaciones MPI a entornos de ejecución dinámicos. Las técnicas integradas en FLEX-MPI permiten mejorar el rendimiento y escalabilidad de las aplicaciones científicas y la eficiencia de las plataformas sobre las que se ejecutan. Entre estas técnicas destacan el balanceo de carga dinámico y maleabilidad para aplicaciones MPI. El diseño e implementación de estas técnicas está dirigido a plataformas de cómputo HPC de pequeña a gran escala. El balanceo de carga dinámico permite a las aplicaciones adaptar de forma eficiente su carga de trabajo a las características y rendimiento de los elementos de procesamiento sobre los que se ejecutan. Por otro lado, la técnica de maleabilidad aprovecha la interfaz de procesos dinámicos de MPI para modificar el número de procesos de la aplicación en tiempo de ejecución, una funcionalidad que permite mejorar el rendimiento de aplicaciones con patrones irregulares o que se ejecutan sobre plataformas de cómputo con disponibilidad dinámica de recursos. Una de las principales características de estas técnicas es que no requieren intervención del usuario ni conocimiento previo de la arquitectura sobre la que se ejecuta la aplicación. Hemos llevado a cabo un proceso de validación y evaluación de rendimiento de las técnicas de adaptabilidad con tres diferentes aplicaciones basadas en MPI, bajo diferentes escenarios de computación homogéneos y heterogéneos. Los resultados demuestran que FLEX-MPI permite obtener un significativo incremento del rendimiento de las aplicaciones, unido a una mejora sustancial de la escalabilidad y un aumento de la eficiencia global del sistema.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Francisco Fernández Rivera.- Secretario: Florín Daniel Isaila.- Vocal: María Santos Pérez Hernánde

An Integrated Modeling Framework for Managing the Deployment and Operation of Cloud Applications

Cloud computing can help Software as a Service (SaaS) providers to take advantage of the sheer number of cloud benefits such as, agility, continuity, cost reduction, autonomy, and easy management of resources. To reap the benefits, SaaS providers should create their applications to utilize the cloud platform capabilities. However, this is a daunting task. First, it requires a full understanding of the service offerings from different providers, and the meta-data artifacts required by each provider to configure the platform to efficiently deploy, run and manage the application. Second, it involves complex decisions that are specified by different stakeholders. Examples include, financial decisions (e.g., selecting a platform to reduces costs), architectural decisions (e.g., partition the application to maximize scalability), and operational decisions (e.g., distributing modules to insure availability and porting the application to other platforms). Finally, while each stakeholder may conduct a certain type of change to address a specific concern, the impact of a change may span multiple models and influence the decisions of several stakeholders. These factors motivate the need for: (i) a new architectural view model that focuses on service operation and reflects the cloud stakeholder perspectives, and (ii) a novel framework that facilitates providing holistic as well as partial architectural views, and generating the required platform artifacts by fragmenting the model into artifacts that can be easily modified separately. This PhD research devises a novel architecture framework, "The 5+1 Architectural View Model", for cloud applications, in which each view corresponds to a different perspective on cloud application deployment. The architectural framework is realized as a cloud modeling framework, called "StratusML", which consists of a modeling language that uses layers to specify the cloud configuration space, and a transformation engine to generate the configuration space artifacts. The usefulness and practical applicability of StratusML to model multi-cloud and multi-tenant applications have been demonstrated though a representative domain example. Moreover, to automate the framework evolution as new concerns and cloud platforms emerge, this research work introduces also a novel schema matching technique, called "Liberate". Liberate supports the process of domain model creation, evolution, and transformations. Liberate helps solve the vendor lock-in problem by reducing the manual efforts required to map complex correspondences between cloud schemas whose domain concepts do not share linguistic similarities. The evaluation of Liberate shows its superiority in the cloud domain over existing schema matching approaches