SPX: Collaborative Research: Scalable Heterogeneous Migrating Threads for Post-Moore Computing

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The project will advance the state of the art in computer architecture and programming systems for extreme and heterogeneous parallelism. It is clear that the post-Moore' law era will require major disruptions in computing systems. This project will address computer architecture and programming system challenges for this new era, with a focus on approaches that are expected to be scalable in size, cost effectiveness, and usability by retaining some tenets of the von Neumann computing model (unlike more exploratory approaches like biological or quantum computing). By emphasizing data analytics, the work will also benefit a rapidly growing swatch of modern life (commercial, cyber, national security, social networks). A deeper understanding of how such applications can be made more scalable, and responsive enough to handle increasing real-time requirements, should lead to wider impacts across every-day life with significant potential for technology transition. There is also a direct connection to pedagogy and workforce development, since both hardware and software aspects of this proposal can enable a broad range of students to better understand the wider diversity of computing platforms projected in future technology roadmaps. The SHMT (Scalable Heterogeneous Migrating Thread) model developed in this award will include extensions to the migrating threads and asynchronous task models to support heterogeneity, and extensions to the transaction and actor models to support data coherence. Further, the investigators propose to use data analytic graph problems to evaluate their research, since these applications are both important in practice and are challenging to solve on current systems. Given the expected continued increase in the size, complexity, and dynamic nature of such computations, it is of growing value to understand how to implement them in a manner that can scale to very high levels of concurrency in environments that include high rate streams of both updates and queries. These techniques can also apply to other application classes, such as scientific applications where data is sparse or irregular. The overall objective of this 3-year research project is to advance the foundations of computer architecture and programming systems to address the emerging challenges of scalable parallelism and extreme heterogeneity, with an emphasis on data analytics and solving data coherence, system management, resource allocation, and task scheduling issues. The investigators will leverage their distinct but synergistic expertise in the architecture and programming systems areas by building on, and integrating, their past work on migrating threads and near-memory processing, software support for asynchronous task parallelism for heterogeneous computing, and data analytics. The Center for Research into Novel Computing Hierarchies (CRNCH) at Georgia Tech will provide access to first-of-a-kind alternative systems for use in evaluating the new concepts. Industrial collaborators include Lexis-Nexis Risk Solutions and Kyndi, for whom graph data analytics are central to their business model.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.