Loci: Logic Programming for Parallel Computational Field Simulations
The Loci Framework
The Loci framework was originally developed in the late 1990s with
support from the NSF ERC for Computational Field Simulations to
simplify the development of complex numerical models that can take
advantage of massively parallel high end computing systems. The
framework provides a rulebased programming model whereby an
application is described in terms of a collection of simple
computational kernels. A key feature of the Loci framework are that
these computational kernels are documented such that the dataflow
between kernels can be extracted using relational annotations that are
provided by the rules. In this system the given data is provided as a
set of facts (relations). The application is then given in the form
of a collection of transformation rules. Finally, the user makes a
query (e.g. what is the heat flux at this surface?) From this the
scheduler provided by the Loci framework provide a composition of the
provided transformations that will answer the query. The effect of the
programming model is to place numerical programming in a sphere
similar to one of information management using relation databases
where problems are solved using powerful relational queries.
The advantage of the Loci framework is that it views the
datastructures of a scientific computation abstractly as a
relation. Computational kernels then read and write data using
relational queries. Relations provide a mechanism for abstracting the
datastructure from the computations. In consideration of performance
most datastructures in Loci are implemented using distributed arrays,
but the model does not preclude alternative implementations such as
hashed containers. This separation of concerns gives the Loci
programming model an ability to morph an application onto a wide
variety of architectural configurations.
To facilitate flexible scheduling of computations, the majority of
computational transformations described by Loci rules have
singleassignment semantics and are referentially transparent. Loci
takes advantage of these features in order to optimize the scheduling
of computations in order to factor in nonuniform cost structures
presented by modern high end computing systems. For example, Loci
provides a work replication optimization that replicates computations
on remote processors in order to reduce interprocessor bandwidth
requirements. These optimization have been shown to improve
application scalability on clusters that use lowcost and subsequently
lowbandwidth interconnect. Optimizations that change the order of
computations in order to enhance locality have also been implemented
and shown to improve application performance.
Why is the system called Loci?
The name of this system, Loci, is the plural of locus. The meaning of
locus that is implied here is the set of points that satisfy some
given set of rules. For example, a circle is the the locus of points
that are equidistant from a given center point. The Loci system
derives the control structure by finding the set of "entities" that
satisfy the rules given to the system. Thus, in a discrete sense, the
program control structure in the Loci system is derived by computing a
set of locus. From this the process the Loci system borrows its name.
Source Code
Latest Stable Loci Release: Loci4.0.tgz
Also available from SourceForge.net
Representative Papers
 "A RuleBased Specification System for
Computational Fluid Dynamics", PhD Dissertation,
Mississippi State University, December 1999
 "Loci: A Deductive Framework for GraphBased Algorithms",
ISCOPE'99 Copyright
SpringerVerlag pp 142153

E. Luke and T. George, "Loci: A RuleBased Framework for Parallel Multidisciplinary Simulation Synthesis," Journal of Functional Programming, Special Issue on Functional Approaches to HighPerformance Parallel
Programming, Volume 15, Issue 03, pp. 477502, Cambridge University Press

Y. Zhang and E. Luke, "Dynamic Memory Management in the Loci Framework," Parallel and Distributed Computing Practices , Volume 7, Number 3, September, 2006

P. Adhikari, E. Luke, and E. Allen, "Verification of a Loop Scheduling Protocol using Finite State Verification," ISCA 22nd International Conference on parallel and Distributed Computing and Communication Systems (PDCCS2009), September 2426, Louisville, Kentucky, 2009

Y. Zhang and E. Luke, "The Design and Implementation of Dynamic Irregular Parallel Computations using the KeyValueReference Model," ISCA 22nd International Conference on parallel and Distributed Computing and Communication Systems (PDCCS2009), September 2426, Louisville, Kentucky, 2009

K. Soni, N. Cain, and E. Luke, "Work Replication: A Communication Optimization in Loci," ISCA 21st International Conference on Parallel and Distributed Computing and Communication Systems (PDCCS2008), September 2426, 2008, New Orleans, LA

Y. Zhang and E. Luke, "Concurrent Composition Using Loci," Computing in Science and Engineering, Volume 11, Issue 3 (May 2009), pp. 2735

Y. Zhang and E. Luke, "Using the Loci Framework for Automated Program and Component Generation," Workshop on Automated Program Generation for Computational Science, ICCS May 31Jun 2, 2010, Amsterdam, The Netherlands, Procedia Computer Science, Volume 1, Issue 1, May 2010, pp. 18551861
 E. Luke, "Loci: Automated Synthesis for Terascale Computing Systems," 27th Army Science Conference, Orlando, Fl, November 29th  December 2nd, 2010, CP08

S. Medders, E. Allen and E. Luke, "Using Rule Structure to Evaluate the Completeness of Rule Based System Testing," Journal of Software Engineering and Knowledge Engineering, Volume 20, Issue 7, pp. 975986, 2010
An Example Application: The Open Source FlowPsi CFD Solver
The flowPsi<\bf> solver is an open source implementation of a fluid flow
solver using computational fluid dynamics model to solve the Navier Stokes
equations for compressible ideal gas flows. It can be found on source forge
at flowPsi .
An Example Application: Chem a FiniteRate Chemistry Solver
The Loci/CHEM code, originally developed as a technology demonstrator
for the Loci framework, has become a well developed and mature
simulation code for complex multiphysics simulations. The solver that
forms the central core of the code is based on highresolution Godonov
methods implemented for multicomponent flows using an implicit time
integration scheme that makes the code effective for high Reynolds
number flows at high speeds. The core algorithims have been extended
to accurately model flows at low speeds through the use of PDE
preconditioning techniques.
Since the original development in 1999, the Loci/CHEM code has been
significantly extended under support from NASA Marshall Space Flight
Center in order to simulate cryogenics, high pressure combustion,
multiphase flows, coupled nongray radiation effects. More recently
support from the Air Force, Department of Homeland Security and US
Army are extending the capabilities of this mature production code
into areas of hypersonic flows and blast simulations. Although the
first version of the code was released in 1999, by 2006 the Loci/CHEM
code was among the top 30 in CPU hours requested on the DoD MSRC
centers. Today it is used widely by NASA and industry predominately
in the simulation of aerospace related problems. Notably it has been
used extensively as the reference model in external aerodynamics
simulations for the ARES I. More information about the CHEM code
can be found
here .