TabulaROSA: Tabular Operating System Architecture for Massively Parallel Heterogeneous Compute Engines
Name
1807.05308.pdf
Description
Submitted version
Size
1.23 MB
Format
Adobe PDF
Checksum (MD5)
a975710dad40c79c97fbd97d19b31d51
Author(s)
Kepner, Jeremy
Brightwell, Ron
Edelman, Alan
Gadepally, Vijay N.
Hayden, Jananthan
Jones, Michael
Madden, Samuel R.
Michaleas, Peter W.
Okhravi, Hamed
Pedretti, Kevin
Date Issued
November 2018
Journal
High Performance Extreme Computing Conference (HPEC), 2018 IEEE
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kepner, Jeremy, Ron Brightwell, Alan Edelman et al. in Proceedings of the 2018 IEEE High Performance Extreme Computing Conference (HPEC), 25-27 Sept. 2018, Waltham, MA, USA, © 2018 IEEE.
Version
Original manuscript
Abstract
The rise in computing hardware choices is driving a reevaluation of operating systems. The traditional role of an operating system controlling the execution of its own hardware is evolving toward a model whereby the controlling processor is distinct from the compute engines that are performing most of the computations. In this context, an operating system can be viewed as software that brokers and tracks the resources of the compute engines and is akin to a database management system. To explore the idea of using a database in an operating system role, this work defines key operating system functions in terms of rigorous mathematical semantics (associative array algebra) that are directly translatable into database operations. These operations possess a number of mathematical properties that are ideal for parallel operating systems by guaranteeing correctness over a wide range of parallel operations. The resulting operating system equations provide a mathematical specification for a Tabular Operating System Architecture (TabulaROSA) that can be implemented on any platform. Simulations of forking in TabularROSA are performed using an associative array implementation and compared to Linux on a 32,000+ core supercomputer. Using over 262,000 forkers managing over 68,000,000,000 processes, the simulations show that TabulaROSA has the potential to perform operating system functions on a massively parallel scale. The TabulaROSA simulations show 20x higher performance as compared to Linux while managing 2000x more processes in fully searchable tables.
MIT Department
Lincoln Laboratory
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/HPEC.2018.8547577
