Engaging Supercomputing Research Projects

Current Projects

Lincoln Laboratory Big Data Class (2013 - Present)

Lincoln Laboratory - Jeremy Kepner, Vijay Gadepally

The Lincoln Laboratory/MIT Beaver Works collaboration hosted a workshop focused on next-generation big data environments that Lincoln Laboratory researchers and their collaborators have created. The class introduced a team of more than 30 students to a set of technologies that employ state-of-the-art database technologies being developed in the Boston area. These technologies include a new generation of massively scalable database systems, SciDB and Accumulo, and an innovative data analysis framework D4M.  Lincoln Laboratory senior scientist Jeremy Kepner, the originator of the D4M technology, developed the class, The class made extensive use of an installation of the Lincoln Laboratory's cluster computing environment LLgrid running on hardware at MGHPCC for student exercises and at scale demonstrations. The high-speed links between MIT campus and Massachusetts Green High Performance Computing Center (MGHPCC) helped ensure that the class technology worked flawlessly throughout the intensive four-day event.  Plans are being developed to offer the course again later in the spring.

The Intel Science and Technology Center for Big Data (2013 - Present)

Lincoln Laboratory - Jeremy Kepner
The Intel Science and Technology Center for Big Data is one of a series of research collaborations that Intel is establishing with U.S. universities to identify and prototype revolutionary technology opportunities. The centers are designed to encourage closer collaboration among academic thought leaders in essential technology areas.  The Intel Science and Technology Center for Big Data is based at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT and is primarily affiliated with the Intel Parallel Computing Lab.

Julia (2013 - Present)

Lincoln Laboratory - Jeremy Kepner
MIT - Alan Edelman

Julia is a high-level, high performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. The library, largely written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing. In addition, the Julia developer community is contributing a number of external packages through Julia’s built-in package manager at a rapid pace. IJulia, a collaboration between the IPython and Julia communities, provides a powerful browser-based graphical notebook interface to Julia. Julia programs are organized around multiple dispatch, by defining functions and overloading them for different combinations of argument types, which can also be user-defined. For a more in-depth discussion of the rationale and advantages of Julia over other systems, read the introduction in the online manual.

High Performance Extreme Computing (HPEC) (2013 - Present)

Lincoln Laboratory - Jeremy Kepner

HPEC is the largest computing conference in New England and is the premier conference in the world on the convergence of High Performance and Extreme Computing. We are passionate about performance. Our community is interested in computing hardware, software, systems, and applications where performance matters. We welcome experts and people who are new to the field.

MITgcm (2013 - Present)

Lincoln Laboratory - Jeremy Kepner
EAPS - Chris Hill

The MITgcm (MIT General Circulation Model) is a numerical model designed for study of the atmosphere, ocean, and climate. Its non-hydrostatic formulation enables it to simulate fluid phenomena over a wide range of scales; its adjoint capability enables it to be applied to parameter and state estimation problems. By employing fluid isomorphisms, one hydrodynamical kernel can be used to simulate flow in both the atmosphere and ocean.  See more at: http://mitgcm.org/#sthash.0DCHtKkY.dpuf

Low-Power Embedded Analytics (2013 - Present)

Lincoln Laboratory - Huy Nguyen, Vijay Gadepally
CSAIL - Prof. Arvind

The goal of this research is to optimize database operations and analytics with accelerators embedded close to data storage, thereby, reducing the net volume of data communication throughout the system. Also, the effort is looking to develop novel storage subsystem concepts for nonvolatile memory and accelerator kernels for in-line analytic processing.