High Performance Computing (HPC) for AEGIS Combat Systems Test Bed (CSTB)
AREA(S): Information Systems
PROGRAM: PEO IWS 1.0, AEGIS Integrated Systems Program Office
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Provide dynamic resource allocation software for High Performance Computing
(HPC) by optimizing computing hardware/software usage in response to
unanticipated simulation events and/or simulations requiring more processing
time in the Combat Systems Test Bed (CSTB).
The CSTB, as an integrated model across the entire AEGIS Combat System, is
computationally intensive to operate and functions in a time-managed
environment. The AEGIS program office has made an investment in modeling and
simulation capabilities to emulate an integrated Combat System. Ultimately this
system requires a grid environment, Monte Carlo analysis capability, innovative
scheduling software, and modular models to ensure necessary model speed and
capacity. The required innovative methods are (1) dynamically allocation of
resources during the simulation and (2) optimization of models in a modular
fashion so that they can take advantage of all hardware available in a grid
The CSTB will be integrating 30-plus models and when used in a simulation will
produce a high-fidelity representation of the entire AEGIS Combat System.
Every model created contains inherent limitations and system resource
requirements, and operates at a designated speed. A High Level Architecture
(HLA) enables the models to integrate together and facilitates the
transportation of interactions amongst them. The current paradigm is to
schedule a model on one server, the next model on another server, and so forth.
The necessary innovative breakthrough is for the scheduling software to be
smart enough to adjust the model in a modular fashion so that the slowest part
of the model is able to run as fast as its quickest part, which would achieve
efficiency in runtime. Furthermore, if a model’s runtime could be sped up with
a GPU (Graphical Processing Unit), the scheduling software should be aware of
this and apply the appropriate resources when possible. Additionally, there is
no current capability for the system to reallocate resources due to an
unplanned event. For example, if a threat did a certain maneuver or a type of
jamming midway through the simulation, there is no way to dynamically allocate
the available computing resources so that this event does not slow down the
entire simulation. This has an exponential impact on time when considering
Monte Carlo runs. For the CSTB to be effective in its mission and deliver
critical analysis, the Navy must run the CSTB using a High Performance
Computing (HPC) paradigm. This HPC environment will use servers in parallel and
will need a method for maximizing the resource capability, availability,
throughput, and capacity within fiscal limitations.
There are commercial off-the-shelf (COTS) solutions available for resource
allocation such as Univa Grid Engine (UGE) and HTCondor. UGE optimizes
throughput and performance of applications, containers, and services by
maximizing shared computing resources. HTCondor is able to develop, implement,
deploy, and evaluate mechanisms and policies that support High Throughput
Computing (HTC) on large collections of distributive computing resources.
Unfortunately, neither of these solutions addresses unplanned events during a
simulation or compensates for additional processing requirements and resource
The Navy seeks scheduling software that allocates and monitors computing
resources, as well as starts the simulations using HPC. Software used in an
HPC-enabled CSTB computing environment will have to comply with the DISA Risk
Management Framework (RMF) methodology for identifying, managing, and
mitigating cybersecurity risk [Ref 3]. The solution will use multiple models
with distribution across multiple servers that utilize the Linux Operating
System, allowing for extraordinary levels of processing speed. The software
will start the simulations by dynamically allocating system resources to
software processes, efficiently utilize the available resources, monitor
resources to ensure effective execution of priorities, and enable reallocation
of resources when required. The innovation needed to achieve these objectives
requires the capability of dynamically adjusting resources throughout a
simulation to shorten the time it takes specific events to execute, e.g., a
maneuvering threat or a scenario that requires jamming. Furthermore, the
scheduling software must be intelligent to operate the models in a modular
fashion allowing for the slowest part of the model to execute as quick as the
fastest part. For example, if a radar model could execute in a shorter time
using a Graphical Processing Unit (GPU), the scheduling software should be
aware of this and take advantage of this computing resource in a modular
fashion. Overall, this innovation would save days of computing time required
for Monte Carlo runs.
The scheduling software will drive affordability through the Navy by reducing
costs in acquisition and manning. The software will maximize shared computing
resources across the server farm, which optimizes performance of the models’
throughput. This distributed structure will reduce costs by selecting resources
that are optimal for each segment of work, subsequently extending the mean time
before failure of each server. Initial estimates for service life enable a cost
reduction of 40% for server purchases. In addition, an estimated 20-40%
reduction in staffing costs is expected for running the model. The current
process to achieve high performance computing is starting each run manually on
individual computers. The scheduling software will enable one individual to
commence and monitor multiple simultaneous runs. Thus, through a reduction in
acquisition of servers and in staffing required to commence and monitor runs,
the scheduling software helps to achieve affordability for the Navy. The AEGIS
CSTB needs to execute runs in a timely manner to answer engineering questions
posed by the technical team. This requires the AEGIS CSTB to run on a server
farm and have the ability to spin up multiple processes in parallel to support
the analysis required to answer the engineering questions asked. HPC allows the
AEGIS CSTB to operate on a server farm to execute parallel processing, and cuts
overall run time for Monte Carlo analysis. This will allow the CSTB to conduct
multiple runs concurrently. The requirement is to reduce the time it takes to
run 100 Monte Carlo sets in series down to the time it would take to run 2-10
sets in series. In this manner, runtime performance will be optimized, allowing
the response time to be decreased by at least a factor of 10.
The system parameters required to attain the specific intended use of the
scheduling software are accepting/starting modeling jobs; allocating jobs to
available resources; monitoring the jobs; ensuring the jobs are executed to
completion; saving the data that is produced on a network-attached storage; and
confirming the validity of the data. User prioritization of jobs will guarantee
that high-priority jobs are finished first.
The CSTB operates in a test environment that consists of desktops and a server
farm. The desktop allows the end-user to access the server farm, where multiple
simulations are executed concurrently. The desktops are used for conducting
analyses on the data that is produced from the simulations.
The Phase II effort will likely require secure access, and NAVSEA will process
the DD254 to support the contractor for personnel and facility certification
for secure access. The Phase I effort will not require access to classified
information. If need be, data of the same level of complexity as secured data
will be provided to support Phase I work.
Work produced in Phase II may become classified. Note: The prospective
contractor(s) must be U.S. Owned and Operated with no Foreign Influence as
defined by DOD 5220.22-M, National Industrial Security Program Operating
Manual, unless acceptable mitigating procedures can and have been be implemented
and approved by the Defense Security Service (DSS). The selected contractor
and/or subcontractor must be able to acquire and maintain a secret level
facility and Personnel Security Clearances, in order to perform on advanced
phases of this contract as set forth by DSS and NAVSEA in order to gain access
to classified information pertaining to the national defense of the United
States and its allies; this will be an inherent requirement. The selected
company will be required to safeguard classified material IAW DoD 5220.22-M
during the advance phases of this contract.
I: Define and develop a concept for scheduling software relative to HPC.
Demonstrate that the concept shows it will feasibly support the test
environments identified in the Description. Determine feasibility by an
assessment of analysis and simulation runtime. Develop a Phase II plan. The
Phase I Option, if exercised, will include the initial design specifications
and capabilities included in the Description to build a prototype solution in
II: Design, develop, and deliver a prototype scheduling software to efficiently
allocate and monitor resources, as well as start simulations across a server
farm. Ensure that the prototype system will be capable of accepting CSTB
Modeling jobs in accordance with the Description requirements.
It is probable that the work under this effort will be classified under Phase
II (see Description section for details).
III DUAL USE APPLICATIONS: Support the Navy in transitioning the technology to
Navy use in order to meet a critical Navy need to decrease the amount of time
it takes to generate data required to answer engineering questions posed by the
technical team. Test the product in the CSTB Laboratory to verify and validate
its functionality. The final product must be approved by the AEGIS CSTB program
This scheduling software can be utilized across the motor vehicle industry and
other large industries that have intensive computational needs. Academia, the
aviation industry, the weather industry, and the energy industry, could benefit
from this technology.
“Introduction to High Performance Computing.” HPC Advisory Council, 18 March
Newton, Randall. “What’s Happening to Cluster Computing?” Digital Engineering,
1 November 2016. http://www.digitaleng.news/de/whats-happening-to-cluster-computing/
DODI 8510.01, Risk Management Framework (RMF) for DoD Information Technology
(IT), 12 March 2014. http://www.esd.whs.mil/Portals/54/Documents/DD/issuances/dodi/851001_2014.pdf
High Performance Computing; HPC; Monte Carlo Analysis; Dynamically Allocating
System Resources; Parallel Processing; Combat Systems Test Bed; CSTB;
Scheduling Software; ACS; AEGIS Combat System
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