Descripción del proyecto
Un almacenamiento adaptativo inteligente para impulsar la informática de alto rendimiento
La creciente necesidad de procesar conjuntos de datos extremadamente grandes es el motor que impulsa la transición a la informática de alto rendimiento. Sin embargo, las jerarquías planas del almacenamiento de datos con un sistema central de archivos paralelo están demostrando no ser adecuadas. Las nuevas jerarquías de almacenamiento de varios niveles podrían satisfacer las necesidades de las aplicaciones con uso intensivo de datos, pero actualmente carecen de mecanismos de control adecuados para los recursos disponibles. El equipo del proyecto ADMIRE, financiado con fondos europeos, tiene previsto desarrollar un sistema de almacenamiento adaptable que debería permitir a los sistemas informáticos de alto rendimiento ofrecer un rendimiento muy alto y aumentar el rendimiento de las aplicaciones. El objetivo es mejorar significativamente el tiempo de ejecución de las aplicaciones en ámbitos como la previsión meteorológica, la teledetección y el aprendizaje profundo.
Objetivo
The growing need to process extremely large data sets is one of the main drivers for building exascale HPC systems today. However, the flat storage hierarchies found in classic HPC architectures no longer satisfy the performance requirements of data-processing applications. Uncoordinated file access in combination with limited bandwidth make the centralised back-end parallel file system a serious bottleneck. At the same time, emerging multi-tier storage hierarchies come with the potential to remove this barrier. But maximising performance still requires careful control to avoid congestion and balance computational with storage performance. Unfortunately, appropriate interfaces and policies for managing such an enhanced I/O stack are still lacking.
The main objective of the ADMIRE project is to establish this control by creating an active I/O stack that dynamically adjusts computation and storage requirements through intelligent global coordination, malleability of computation and I/O, and the scheduling of storage resources along all levels of the storage hierarchy. To achieve this, we will develop a software-defined framework based on the principles of scalable monitoring and control, separated control and data paths, and the orchestration of key system components and applications through embedded control points.
Our software-only solution will allow the throughput of HPC systems and the performance of individual applications to be substantially increased – and consequently energy consumption to be decreased – by taking advantage of fast and power-efficient node-local storage tiers using novel, European ad-hoc storage systems and in-transit/in-situ processing facilities. Furthermore, our enhanced I/O stack will offer quality-of-service (QoS) and resilience. An integrated and operational prototype will be validated with several use cases from various domains, including climate/weather, life sciences, physics, remote sensing, and deep learning.
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RIA - Research and Innovation actionCoordinador
28903 Getafe (Madrid)
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