Section: Research Program

Research axis 1: Convergence of HPC and Big Data

The tools and cultures of High Performance Computing and Big Data Analytics have evolved in divergent ways. This is to the detriment of both. However, big computations still generate and are needed to analyze Big Data. As scientific research increasingly depends on both high-speed computing and data analytics, the potential interoperability and scaling convergence of these two ecosystems is crucial to the future.

Our objective is premised on the idea that we must explore the ways in which the major challenges associated with Big Data analytics intersect with, impact, and potentially change the directions now in progress for achieving Exascale computing.

In particular, a key milestone will be to achieve convergence through common abstractions and techniques for data storage and processing in support of complex workflows combining simulations and analytics. Such application workflows will need such a convergence to run on hybrid infrastructures combining HPC systems and clouds (potentially in extension to edge devices, in a complete digital continuum).


This axis is addressed in close collaboration with María Pérez (UPM), Rob Ross (ANL), Toni Cortes (BSC), Several groups at Argonne National Laboratory and NCSA (Franck Cappello, Rob Ross, Bill Kramer, Tom Peterka).

Relevant groups with similar interests are the following ones.

  • The group of Jack Dongarra, Innovative Computing Laboratory at University of Tennessee, who is leading international efforts for the convergence of Exascale Computing and Big Data.

  • The group of Satoshi Matsuoka, RIKEN, working on system software for clouds and HPC.

  • The group of Ian Foster, Argonne National Laboratory, working on on-demand data analytics and storage for extreme-scale simulations and experiments.

High-performance storage for concurrent Big Data applications

Storage is a plausible pathway to convergence. In this context, we plan to focus on the needs of concurrent Big Data applications that require high-performance storage, as well as transaction support. Although blobs (binary large objects) are an increasingly popular storage model for such applications, state-of-the-art blob storage systems offer no transaction semantics. This demands users to coordinate data access carefully in order to avoid race conditions, inconsistent writes, overwrites and other problems that cause erratic behavior.

There is a gap between existing storage solutions and application requirements, which limits the design of transaction-oriented applications. In this context, one idea on which we plan to focus our efforts is exploring how blob storage systems could provide built-in, multiblob transactions, while retaining sequential consistency and high throughput under heavy access concurrency.

The early principles of this research direction have already raised interest from our partners at ANL (Rob Ross) and UPM (María Pérez) for potential collaborations. In this direction, the acceptance of our paper on the Týr transactional blob storage system as a Best Student Paper Award Finalist at the SC16 conference [10] is a very encouraging step.

Towards unified data processing techniques for Extreme Computing and Big Data applications

In the high-performance computing area (HPC), the need to get fast and relevant insights from massive amounts of data generated by extreme-scale computations led to the emergence of in situ processing. It allows data to be visualized and processed in real-time on the supercomputer generating them, in an interactive way, as they are produced, as opposed to the traditional approach consisting of transferring data off-site after the end of the computation, for offline analysis. As such processing runs on the same resources executing the simulation, if it consumes too many resources, there is a risk to "disturb" the simulation.

Consequently, an alternative approach was proposed (in transit processing), as a means to reduce this impact: data are are transferred to some temporary processing resources (with high memory and processing capacities). After this real-time processing, they are moved to persistent storage.

In the Big Data area, the search for real-time, fast analysis was materialized through a different approach: stream-based processing. Such an approach is based on a different abstraction for data, that are seen as a dynamic flow of items to be processed. Stream-based processing and in situ/in transit processing have been developed separately and implemented in different tools in the BDA and HPC areas respectively.

A major challenge from the perspective of the HPC-BDA convergence is their joint use in a unified data processing architecture. This is one of the future research challenges that I plan to address in the near future, by combining ongoing approaches currently active in my team: Damaris and KerA. We started preliminary work within the "Frameworks" work package of the HPC-Big Data IPL. Further exploring this convergence is a core direction of our current efforts to build collaborative European projects.