A Multicore Path to Connectomics-on-Demand
Abstract
The current design trend in large scale machine learning is to use distributed clusters of CPUs and GPUs with MapReduce-style programming. Some have been led to believe that this type of horizontal scaling can reduce or even eliminate the need for traditional algorithm development, careful parallelization, and performance engineering. This paper is a case study showing the contrary: that the benefits of algorithms, parallelization, and performance engineering, can sometimes be so vast that it is possible to solve "cluster-scale" problems on a single commodity multicore machine.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 26, 2017
- Source ID
- 10.1145/3155284.3018766
Entities
People
- Aleksandar Zlateski
- Alexander Matveev
- David Budden
- Gergely Odor
- Hayk Saribekyan
- Nir Shavit
- Tim Kaler
- Wiktor Jakubiuk
- Yaron Meirovitch
Organizations
- Intelligence Advanced Research Projects Activity
- Massachusetts Institute of Technology
- National Science Foundation