Data and model convergence: a case for software defined architectures

Abstract

High Performance Computing, data analytics, and machine learning are often considered three separate and different approaches. Applications, software and now hardware stacks are typically designed to only address one of the areas at a time. This creates a false distinction across the three different areas. In reality, domain scientists need to exercise all the three approaches in an integrated way. For example, large scale simulations generate enormous amount of data, to which Big Data Analytics techniques can be applied. Or, as scientist seek to use data analytics as well as simulation for discovery, machine learning can play an important role in making sense of the disparate source's information. Pacific Northwest National Laboratory is launching a new Laboratory Directed Research and Development (LDRD) Initiative to investigate the integration of the three techniques at all level of the high-performance computing stack, the Data-Model Convergence (DMC) Initiative. The DMC Initiative aims to increase scientist productivity by enabling purpose-built software and hardware and domain-aware ML techniques. In this talk, I will present the objectives of PNNL's DMC Initiative, highlighting the research that will be performed to enable the integration of vastly different programming paradigms and mental models. I will then make the case for how reconfigurable architectures could represent a great opportunity to address the challenges of DMC. In principle, the possibility to dynamically modify the architecture during runtime could provide a way to address the requirement of workloads that have significantly diverse behaviors across phases, without losing too much flexibility or programmer productivity, with respect to highly heterogeneous architectures composed by sea of fixed application specific accelerators. Reconfigurable architectures have been explored since long time ago, and arguably new software breakthroughs are required to make them successful. I will thus present the efforts that the DMC initiative is launching to design a productive toolchain for upcoming novel reconfigurable systems.