A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be executed on specialized hardware such graphical processing units (GPUs) in a distributed manner. In the academic field, researchers get access to this kind of resources through High Performance Computing (HPC) clusters. This kind of infrastructures make the training of these models difficult due to their multi-user nature and limited user permission. In addition, different HPC clusters may possess different peculiarities that can entangle the research cycle (e.g., libraries dependencies). In this paper we develop a workflow and methodology for the distributed training of deep learning models in HPC clusters which provides researchers with a series of novel advantages. It relies on udocker as containerization tool and on Horovod as library for the distribution of the models across multiple GPUs. udocker does not need any special permission, allowing researchers to run the entire workflow without relying on any administrator. Horovod ensures the efficient distribution of the training independently of the deep learning framework used. Additionally, due to containerization and specific features of the workflow, it provides researchers with a cluster-agnostic way of running their models. The experiments carried out show that the workflow offers good scalability in the distributed training of the models and that it easily adapts to different clusters.Comment: Under review for Cluster Computin

A container-based workflow for distributed training of deep learning algorithms in HPC clusters

Deep learning has been postulated as a solution for numerous problems in different branches of science. Given the resource-intensive nature of these models, they often need to be executed on specialized hardware such graphical processing units (GPUs) in a distributed manner. In the academic field, researchers get access to this kind of resources through High Performance Computing (HPC) clusters. This kind of infrastructures make the training of these models difficult due to their multi-user nature and limited user permission. In addition, different HPC clusters may possess different peculiarities that can entangle the research cycle (e.g., libraries dependencies). In this paper we develop a workflow and methodology for the distributed training of deep learning models in HPC clusters which provides researchers with a series of novel advantages. It relies on udocker as containerization tool and on Horovod as library for the distribution of the models across multiple GPUs. udocker does not need any special permission, allowing researchers to run the entire workflow without relying on any administrator. Horovod ensures the efficient distribution of the training independently of the deep learning framework used. Additionally, due to containerization and specific features of the workflow, it provides researchers with a cluster-agnostic way of running their models. The experiments carried out show that the workflow offers good scalability in the distributed training of the models and that it easily adapts to different clusters

Serverless Computing Strategies on Cloud Platforms

[ES] Con el desarrollo de la Computación en la Nube, la entrega de recursos virtualizados a través de Internet ha crecido enormemente en los últimos años. Las Funciones como servicio (FaaS), uno de los modelos de servicio más nuevos dentro de la Computación en la Nube, permite el desarrollo e implementación de aplicaciones basadas en eventos que cubren servicios administrados en Nubes públicas y locales. Los proveedores públicos de Computación en la Nube adoptan el modelo FaaS dentro de su catálogo para proporcionar computación basada en eventos altamente escalable para las aplicaciones. Por un lado, los desarrolladores especializados en esta tecnología se centran en crear marcos de código abierto serverless para evitar el bloqueo con los proveedores de la Nube pública. A pesar del desarrollo logrado por la informática serverless, actualmente hay campos relacionados con el procesamiento de datos y la optimización del rendimiento en la ejecución en los que no se ha explorado todo el potencial. En esta tesis doctoral se definen tres estrategias de computación serverless que permiten evidenciar los beneficios de esta tecnología para el procesamiento de datos. Las estrategias implementadas permiten el análisis de datos con la integración de dispositivos de aceleración para la ejecución eficiente de aplicaciones científicas en plataformas cloud públicas y locales. En primer lugar, se desarrolló la plataforma CloudTrail-Tracker. CloudTrail-Tracker es una plataforma serverless de código abierto basada en eventos para el procesamiento de datos que puede escalar automáticamente hacia arriba y hacia abajo, con la capacidad de escalar a cero para minimizar los costos operativos. Seguidamente, se plantea la integración de GPUs en una plataforma serverless local impulsada por eventos para el procesamiento de datos escalables. La plataforma admite la ejecución de aplicaciones como funciones severless en respuesta a la carga de un archivo en un sistema de almacenamiento de ficheros, lo que permite la ejecución en paralelo de las aplicaciones según los recursos disponibles. Este procesamiento es administrado por un cluster Kubernetes elástico que crece y decrece automáticamente según las necesidades de procesamiento. Ciertos enfoques basados en tecnologías de virtualización de GPU como rCUDA y NVIDIA-Docker se evalúan para acelerar el tiempo de ejecución de las funciones. Finalmente, se implementa otra solución basada en el modelo serverless para ejecutar la fase de inferencia de modelos de aprendizaje automático previamente entrenados, en la plataforma de Amazon Web Services y en una plataforma privada con el framework OSCAR. El sistema crece elásticamente de acuerdo con la demanda y presenta una escalado a cero para minimizar los costes. Por otra parte, el front-end proporciona al usuario una experiencia simplificada en la obtención de la predicción de modelos de aprendizaje automático. Para demostrar las funcionalidades y ventajas de las soluciones propuestas durante esta tesis se recogen varios casos de estudio que abarcan diferentes campos del conocimiento como la analítica de aprendizaje y la Inteligencia Artificial. Esto demuestra que la gama de aplicaciones donde la computación serverless puede aportar grandes beneficios es muy amplia. Los resultados obtenidos avalan el uso del modelo serverless en la simplificación del diseño de arquitecturas para el uso intensivo de datos en aplicaciones complejas.[CA] Amb el desenvolupament de la Computació en el Núvol, el lliurament de recursos virtualitzats a través d'Internet ha crescut granment en els últims anys. Les Funcions com a Servei (FaaS), un dels models de servei més nous dins de la Computació en el Núvol, permet el desenvolupament i implementació d'aplicacions basades en esdeveniments que cobreixen serveis administrats en Núvols públics i locals. Els proveïdors de computació en el Núvol públic adopten el model FaaS dins del seu catàleg per a proporcionar a les aplicacions computació altament escalable basada en esdeveniments. D'una banda, els desenvolupadors especialitzats en aquesta tecnologia se centren en crear marcs de codi obert serverless per a evitar el bloqueig amb els proveïdors del Núvol públic. Malgrat el desenvolupament alcançat per la informàtica serverless, actualment hi ha camps relacionats amb el processament de dades i l'optimització del rendiment d'execució en els quals no s'ha explorat tot el potencial. En aquesta tesi doctoral es defineixen tres estratègies informàtiques serverless que permeten demostrar els beneficis d'aquesta tecnologia per al processament de dades. Les estratègies implementades permeten l'anàlisi de dades amb a integració de dispositius accelerats per a l'execució eficient d'aplicacion scientífiques en plataformes de Núvol públiques i locals. En primer lloc, es va desenvolupar la plataforma CloudTrail-Tracker. CloudTrail-Tracker és una plataforma de codi obert basada en esdeveniments per al processament de dades serverless que pot escalar automáticament cap amunt i cap avall, amb la capacitat d'escalar a zero per a minimitzar els costos operatius. A continuació es planteja la integració de GPUs en una plataforma serverless local impulsada per esdeveniments per al processament de dades escalables. La plataforma admet l'execució d'aplicacions com funcions severless en resposta a la càrrega d'un arxiu en un sistema d'emmagatzemaments de fitxers, la qual cosa permet l'execució en paral·lel de les aplicacions segon sels recursos disponibles. Este processament és administrat per un cluster Kubernetes elàstic que creix i decreix automàticament segons les necessitats de processament. Certs enfocaments basats en tecnologies de virtualització de GPU com rCUDA i NVIDIA-Docker s'avaluen per a accelerar el temps d'execució de les funcions. Finalment s'implementa una altra solució basada en el model serverless per a executar la fase d'inferència de models d'aprenentatge automàtic prèviament entrenats en la plataforma de Amazon Web Services i en una plataforma privada amb el framework OSCAR. El sistema creix elàsticament d'acord amb la demanda i presenta una escalada a zero per a minimitzar els costos. D'altra banda el front-end proporciona a l'usuari una experiència simplificada en l'obtenció de la predicció de models d'aprenentatge automàtic. Per a demostrar les funcionalitats i avantatges de les solucions proposades durant esta tesi s'arrepleguen diversos casos d'estudi que comprenen diferents camps del coneixement com l'analítica d'aprenentatge i la Intel·ligència Artificial. Això demostra que la gamma d'aplicacions on la computació serverless pot aportar grans beneficis és molt àmplia. Els resultats obtinguts avalen l'ús del model serverless en la simplificació del disseny d'arquitectures per a l'ús intensiu de dades en aplicacions complexes.[EN] With the development of Cloud Computing, the delivery of virtualized resources over the Internet has greatly grown in recent years. Functions as a Service (FaaS), one of the newest service models within Cloud Computing, allows the development and implementation of event-based applications that cover managed services in public and on-premises Clouds. Public Cloud Computing providers adopt the FaaS model within their catalog to provide event-driven highly-scalable computing for applications. On the one hand, developers specialized in this technology focus on creating open-source serverless frameworks to avoid the lock-in with public Cloud providers. Despite the development achieved by serverless computing, there are currently fields related to data processing and execution performance optimization where the full potential has not been explored. In this doctoral thesis three serverless computing strategies are defined that allow to demonstrate the benefits of this technology for data processing. The implemented strategies allow the analysis of data with the integration of accelerated devices for the efficient execution of scientific applications on public and on-premises Cloud platforms. Firstly, the CloudTrail-Tracker platform was developed to extract and process learning analytics in the Cloud. CloudTrail-Tracker is an event-driven open-source platform for serverless data processing that can automatically scale up and down, featuring the ability to scale to zero for minimizing the operational costs. Next, the integration of GPUs in an event-driven on-premises serverless platform for scalable data processing is discussed. The platform supports the execution of applications as serverless functions in response to the loading of a file in a file storage system, which allows the parallel execution of applications according to available resources. This processing is managed by an elastic Kubernetes cluster that automatically grows and shrinks according to the processing needs. Certain approaches based on GPU virtualization technologies such as rCUDA and NVIDIA-Docker are evaluated to speed up the execution time of the functions. Finally, another solution based on the serverless model is implemented to run the inference phase of previously trained machine learning models on theAmazon Web Services platform and in a private platform with the OSCAR framework. The system grows elastically according to demand and is scaled to zero to minimize costs. On the other hand, the front-end provides the user with a simplified experience in obtaining the prediction of machine learning models. To demonstrate the functionalities and advantages of the solutions proposed during this thesis, several case studies are collected covering different fields of knowledge such as learning analytics and Artificial Intelligence. This shows the wide range of applications where serverless computing can bring great benefits. The results obtained endorse the use of the serverless model in simplifying the design of architectures for the intensive data processing in complex applications.Naranjo Delgado, DM. (2021). Serverless Computing Strategies on Cloud Platforms [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/160916TESI

SparkFlow : towards high-performance data analytics for Spark-based genome analysis

The recent advances in DNA sequencing technology triggered next-generation sequencing (NGS) research in full scale. Big Data (BD) is becoming the main driver in analyzing these large-scale bioinformatic data. However, this complicated process has become the system bottleneck, requiring an amalgamation of scalable approaches to deliver the needed performance and hide the deployment complexity. Utilizing cutting-edge scientific workflows can robustly address these challenges. This paper presents a Spark-based alignment workflow called SparkFlow for massive NGS analysis over singularity containers. SparkFlow is highly scalable, reproducible, and capable of parallelizing computation by utilizing data-level parallelism and load balancing techniques in HPC and Cloud environments. The proposed workflow capitalizes on benchmarking two state-of-art NGS workflows, i.e., BaseRecalibrator and ApplyBQSR. SparkFlow realizes the ability to accelerate large-scale cancer genomic analysis by scaling vertically (HyperThreading) and horizontally (provisions on-demand). Our result demonstrates a trade-off inevitably between the targeted applications and processor architecture. SparkFlow achieves a decisive improvement in NGS computation performance, throughput, and scalability while maintaining deployment complexity. The paper’s findings aim to pave the way for a wide range of revolutionary enhancements and future trends within the High-performance Data Analytics (HPDA) genome analysis realm.Postprin

Unified Container Environments for Scientific Cluster Scenarios

Providing runtime dependencies for computational workflows in shared environments, like HPC clusters, requires appropriate management efforts from users and administrators. Users of a cluster define the software stack required for a workflow to execute successfully, while administrators maintain the mechanisms to offer libraries and applications in different versions and combinations for the users to have maximum flexibility. The Environment Modules system is the tool of choice on bwForCluster BinAC for this purpose. In this paper, we present a solution to execute a workflow which relies on a software stack not available via Environment Modules on BinAC. The paper describes the usage of a containerized, user-defined software stack for this particular problem using the Singularity and Docker container platforms. Additionally, we present a solution for the reproducible provisioning of identical software stacks across HPC and non-HPC environments. The approach uses a Docker image as the basis for a Singularity container. This allows users to define arbitrary software stacks giving them the ability to execute their workflows across different environments, from local workstations to HPC clusters. This approach provides identical versions of software and libraries across all environments