In this paper we present a framework to enable data-intensive Spark workloads on MareNostrum, a petascale supercomputer designed mainly for compute-intensive applications. As far as we know, this is the first attempt to investigate optimized deployment configurations of Spark on a petascale HPC setup. We detail the design of the framework and present some benchmark data to provide insights into the scalabilityof the system. We examine the impact of different configurations including parallelism, storage and networking alternatives, and we discuss several aspects in executing Big Data workloads on a computing system that is based on the compute-centric paradigm. Further, we derive conclusions aiming to pave the way towards systematic and optimized methodologies for fine-tuning data-intensive application on large clusters emphasizing on parallelism configurations.
['Ruben Tous', 'Anastasios Gounaris', 'Carlos Tripiana', 'Jordi Torres', 'Sergi Girona', 'Eduard Ayguadé', 'Jesús Labarta', 'Yolanda Becerra', 'David Carrera', 'Mateo Valero']
Spark deployment and performance evaluation on the MareNostrum supercomputer