Location: (College Park, MD)
Personal Research Web Page: http://www.umiacs.umd.edu/users/vishkin/XMT/index.shtml
Keywords: Parallel algorithms, compilers, architecture, application, education of parallelism, bioinformatics, machine learning, security, OS, and SW architectures.
Posted on: Friday, May 6th, 2011
Broad Research Area: AI / Machine Learning / Robotics / Vision, Computer Science Education / Educational Technology, Graphics / Visualization, Hardware / Architecture, Information Assurance / Security / Privacy / Cryptography, Networks / Operating Systems, Numerical/Scientific Computing / HPC / Data-Intensive Scalable Computing, Programming Languages / Compilers, Scientific/Medical Informatics, Software Engineering, Theory / Algorithms
It is now widely recognized that current commercial many-core systems are simply not good enough: most programmers can’t handle them. Therefore, alternatives must be developed. Anticipating this problem over a decade ago, the Explicit Multi-Threading (XMT) framework has been under development at the University of Maryland. XMT is a general-purpose many-core computing platform with the vision of a 1000-core chip that is easy to program but does not compromise on performance.
XMT is built to support the PRAM theory of parallel algorithm, which is second in its wealth only to the serial algorithms. Since four decades of parallel computing research provided no real alternative to the PRAM, the XMT project sought to draft specifications for the general-purpose many-core desktop of the future, by first inventing hardware and software support for the abstractions developed by PRAM algorithmics — a task deemed impossible by architecture researchers prior to the accomplishments of the XMT project.
A 2010 status report of XMT appears in U. Vishkin, Using simple abstraction for reinventing computing for parallelism, CACM, January 2011. Order of magnitude speedups, dramatic advantages on teachability from middle school to graduate courses have been demonstrated. And favorable student ranking for achieving speedups relative to standard platforms have been demonstrated.
So far, XMT has spanned applications, parallel algorithms, compilers, HW/SW and education of parallelism. Research opportunities building on this promising foundation include now also CS education, bioinformatics, machine learning and other applications, security, OS, and SW architectures.
There is so much more to the potential of many-core parallel computing than the horizons of commercial hardware offer!
Please email to my ‘last name’ at umd.edu your resume, a research statement and 3 references.