Descripción del proyecto
Nuevos sistemas para las actuales aplicaciones basadas en datos
La infraestructura para la gestión de datos está creciendo rápidamente. Para obtener predicciones precisas, las aplicaciones modernas basadas en datos aprovechan las grandes y heterogéneas recolecciones de datos para desvelar patrones interesantes. También desarrollan robustos modelos de aprendizaje automático para realizar predicciones precisas. En consecuencia, los nuevos sistemas se han desarrollado con computaciones tradicionales y de alto rendimiento, y con la arquitectura de agrupaciones de «hardware» subyacentes. También existe una tendencia hacia complejos canales de análisis de datos que combinan diferentes sistemas. El proyecto DAPHNE, financiado con fondos europeos, definirá una infraestructura de sistemas extensible y abierta para canales de análisis de datos integrados. Desarrollará una implantación de referencia para abstracciones de lenguajes (API y lenguaje específico de dominio) y representaciones intermedias, así como técnicas de ejecución y compilación.
Objetivo
Modern data-driven applications leverage large, heterogeneous data collections to find interesting patterns, and build robust machine learning (ML) models for accurate predictions. Large data sizes and advanced analytics spurred the development and adoption of data-parallel computation frameworks like Apache Spark or Flink as well as distributed ML systems like MLlib, TensorFlow, or PyTorch. A key observation is that these new systems share many techniques with traditional high-performance computing (HPC), and the architecture of underlying HW clusters converges. Yet, the programming paradigms, cluster resource management, as well as data formats and representations differ substantially across data management, HPC, and ML software stacks. There is a trend though, toward complex data analysis pipelines that combine these different systems. Examples are workflows of distributed data pre-processing, tuned HPC libraries, and dedicated ML systems, but also HPC applications that leverage ML models for more cost-effective simulation. Major obstacles are (1) limited development productivity for integrated analysis pipelines due to different programming models, and separated cluster environments, (2) unnecessary data movement overhead and underutilization due to separate, statically provisioned clusters, and (3) lack of a common system infrastructure with good interoperability. For these reasons, DAPHNE’s overall objective is the definition of an open and extensible systems infrastructure for integrated data analysis pipelines. We aim at building a reference implementation of language abstractions (i.e. APIs and a domain-specific language), an intermediate representation, as well as compilation and runtime techniques with support for integrating and scheduling heterogeneous accelerator and storage devices. A variety of real-world, high-impact use cases, datasets, and a new benchmark will be used for qualitative and quantitative analysis compared to state-of-the-art.
Ámbito científico
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-ICT-2020-1
Régimen de financiación
RIA - Research and Innovation actionCoordinador
8010 Graz
Austria