Task-based programming has proven to be a suitable model for high-performance computing (HPC) applications. Different implementations have been good demonstrators of this fact and have promoted the acceptance of task-based programming in the OpenMP standard. Furthermore, in recent years, Apache Spark has gained wide popularity in business and research environments as a programming model for addressing emerging big data problems. COMP Superscalar (COMPSs) is a task-based environment that tackles distributed computing (including Clouds) and is a good alternative for a task-based programming model for big data applications. This article describes why we consider that task-based programming models are a good approach for big data applications. ...
International audienceBig Data analytics frameworks (e.g., Apache Hadoop and Apache Spark) have been...
Distributed computing platforms are evolving to heterogeneous ecosystems with Clusters, Grids and Cl...
Abstract—In this paper we present a framework to enable data-intensive Spark workloads on MareNostru...
Task-based programming has proven to be a suitable model for high-performance computing (HPC) applic...
Recently, efforts have been made to bring together the areas of high-performance computing (HPC) and...
In the recent joint venture between High-Performance Computing (HPC) and Big-Data (BD) Ecosystems to...
One of the biggest challenges in the programming of scientific application is the efficient exploita...
COMPSs is a programming framework that aims to facilitate the parallelization of existing applicatio...
As dataset sizes increase, data analysis tasks in high performance computing (HPC) are increasingly ...
In this paper we present a framework to enable data-intensive Spark workloads on MareNostrum, a peta...
Along with the popularity of multicore and manycore, task-based dataflow programming models obtain g...
International audienceTask-based paradigm models can be an alternative to MPI. The user defines atom...
Este trabalho compara o desempenho e a estabilidade de dois arcabouços para o processamento de Big D...
Best paper award.International audienceSpark is being successfully used for big data parallel proces...
Typically called big data processing, analyzing large volumes of data from geographically distribute...
International audienceBig Data analytics frameworks (e.g., Apache Hadoop and Apache Spark) have been...
Distributed computing platforms are evolving to heterogeneous ecosystems with Clusters, Grids and Cl...
Abstract—In this paper we present a framework to enable data-intensive Spark workloads on MareNostru...
Task-based programming has proven to be a suitable model for high-performance computing (HPC) applic...
Recently, efforts have been made to bring together the areas of high-performance computing (HPC) and...
In the recent joint venture between High-Performance Computing (HPC) and Big-Data (BD) Ecosystems to...
One of the biggest challenges in the programming of scientific application is the efficient exploita...
COMPSs is a programming framework that aims to facilitate the parallelization of existing applicatio...
As dataset sizes increase, data analysis tasks in high performance computing (HPC) are increasingly ...
In this paper we present a framework to enable data-intensive Spark workloads on MareNostrum, a peta...
Along with the popularity of multicore and manycore, task-based dataflow programming models obtain g...
International audienceTask-based paradigm models can be an alternative to MPI. The user defines atom...
Este trabalho compara o desempenho e a estabilidade de dois arcabouços para o processamento de Big D...
Best paper award.International audienceSpark is being successfully used for big data parallel proces...
Typically called big data processing, analyzing large volumes of data from geographically distribute...
International audienceBig Data analytics frameworks (e.g., Apache Hadoop and Apache Spark) have been...
Distributed computing platforms are evolving to heterogeneous ecosystems with Clusters, Grids and Cl...
Abstract—In this paper we present a framework to enable data-intensive Spark workloads on MareNostru...