Title: Simulation framework for performance prediction in the engineering of RC systems and applications
Full Citation
Permanent Link: http://ufdc.ufl.edu/UF00094696/00001
 Material Information
Title: Simulation framework for performance prediction in the engineering of RC systems and applications
Physical Description: Book
Language: English
Creator: Grobelny, Eric
Reardon, Casey
Jacobs, Adam
George, Alan D.
Publisher: Grobelny et al.
Place of Publication: Gainesville, Fla.
Copyright Date: 2007
 Record Information
Bibliographic ID: UF00094696
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.


This item has the following downloads:

ERSA2007_F1 ( PDF )

Full Text

Simulation Framework for Performance Prediction in the

Engineering of RC Systems and Applications

Eric Grobelny, Casey Reardon, Adam Jacobs, Alan D. George
NSF Center for High-Performance Reconfigurable Computing (CHREC)
HCS Research Lab, ECE Department, University of Florida
Telephone: (352) 392-5225 Fax: (352) 392-8671

Abstract-Reconfigurable computing (RC) is rapidly emerging
as a promising technology for the future of high-performance
computing, enabling systems with the computational density and
power of custom-logic hardware and the versatility of software-
driven hardware in an optimal mix. Novel methods for rapid virtual
prototyping, performance prediction, and evaluation are of critical
importance in the engineering of complex reconfigurable systems
and applications. This approach can yield insightful tradeoff
analyses while saving valuable time and resources for researchers
and engineers alike. The research described herein provides a
methodology for mapping arbitrary applications to targeted re-
configurable systems in a simulation environment. By splitting the
process into two domains, the application and simulation domains,
characterization of each element can occur idl, p, ndl, nlr and
in parallel, leading to fast and accurate performance prediction
results. This paper presents the design of a novel new framework
for simulative performance prediction, along with a single-node
case study performed with a 'rnth, ti, -aperture radar application
to provide validation results and a component tradeoff analysis,
illustrating the effectiveness of our approach at the node level.

Reconfigurable computing (RC) is becoming recognized
as an increasingly important and viable paradigm for high-
performance computing in times where the size and power
consumption of clusters and traditional supercomputers have
grown to alarming levels. With RC, the performance potential
of underlying hardware resources in a system can be fully
realized in a highly adaptive manner. The underlying device
technology enabling this new paradigm of computing is field-
programmable hardware, such as the field-programmable gate
array or FPGA. These programmable logic devices feature
many thousands of logic cells as building blocks that can be
quickly configured and interconnected to form application-
specific custom logic. RC extends the fields of large-scale and
embedded high-performance computing by incorporating and
dynamically reconfiguring these devices at run-time to acceler-
ate operations that would otherwise be performed in software.
Hybrid systems of microprocessors and FPGAs can leverage
system-level concepts from conventional high-performance
computing while accommodating hardware reconfigurability.
While these hybrid systems offer the potential for large
performance improvements over traditional systems, the in-
troduction of reconfigurable devices can dramatically increase
the design complexity of such systems. In addition to tra-
ditional design space parameters such as processor speed,

memory subsystem performance, and network interconnect,
RC systems must also consider FPGA resources, IO subsys-
tem performance, and reconfiguration capabilities. The large
design space can make targeting applications to a specific RC
system difficult and daunting. Simulation provides a means
of predicting the performance and bottlenecks of applications
running on numerous system configurations, for the purpose of
studying design tradeoffs. The resulting analyses can provide
useful data before investing ,is.iiiIk.iiI amounts of time and
resources on the development of a particular solution.
In this paper, we present a framework for simulating ap-
plications on reconfigurable computing systems that balances
both speed and fidelity. By providing this balance, our frame-
work can provide a broad range of timely and meaningful
prediction analyses for a given reconfigurable application or
platform. With the appropriate models and calibration data,
numerous existing and future systems and applications can
be efficiently simulated and analyzed. The remainder of this
paper is organized as follows. Section II presents related
modeling and simulation research, for both reconfigurable
and high-performance computing. Section III provides an
overview of our simulation approach and methodology for
performance prediction of reconfigurable computing systems.
In Section IV, the results of our node-level architecture case
study are presented to validate and help illustrate the capabil-
ities of our simulation framework. Finally, the conclusions of
this paper are summarized in Section V.


The modeling and performance prediction of RC devices
can be broken into two primary categories: device-level and
system-level. Device-level modeling is normally handled us-
ing electronic design toolkits such as ActiveHDL and Model-
Sim provided by vendors such as Aldec and Mentor Graphics,
respectively. These tools use the HDL languages employed to
design the corresponding functional cores and only target the
performance of the specific configurable device or family of
devices rather than the entire computational subsystem exer-
cised when using the core with a real application. Although
accurate, the tools are device-specific and have the potential to
take hours or even days to execute and do not address critical
performance issues in other components of a subsystem or
system, such as the I/O bus to which the device attaches.

A number of research projects attempt to predict the
performance of RC devices through the use of analytical
models. In [1], analytical models were developed to analyze
the performance of heterogeneous workstations outfitted with
RC devices. The research specifically addresses performance
issues dealing with load balancing using synchronous iterative
applications at both the node and device level. The models
showed reasonable accuracy, however igniiik.iii effort is
required for each application under study. In [2], RC models
are developed to predict the performance of vision algo-
rithms. The models incorporate the traditional configurable
computing system with a configurable device being used as
an offload engine by a host processor with implementation
details abstracted away. The models in their project address
performance prediction in a general sense with numerous
architectural and core variations possible. Although a compre-
hensive model is presented, model validation is not provided
and, again, the models are fairly complex and difficult to create
for mapping applications to specific architectures.

This paper presents a novel new simulation framework for
performance prediction of reconfigurable computing systems
that balances speed and accuracy. The goal of the project is to
develop a framework that supports the analyses of numerous
variations of RC architectures at both the node and system
levels and application mappings to them. These architectures
include clusters of RC nodes, supercomputers with RC de-
vices, embedded systems, and other current and emerging
configurations. As a result of the wide design space possible
for current and future RC systems, we assume a generic
system model as shown in Fig. 1. The generic system consists
of one or more nodes interconnected by some high-speed
interconnect. Each RC node includes a host processor that
typically handles general computing tasks while offloading
specialized tasks to the corresponding RC device. The archi-
tecture is general enough, however, to support future systems
that incorporate RC devices that act completely independent of
the host processor. The RC devices can be attached to various
local interconnect technologies within the node, including a
peripheral bus or system bus or switching fabric. By support-
ing the generic architecture described in Fig. 1, this simulation
framework allows for comprehensive analyses of arbitrary
serial and parallel RC applications targeted for the wide
range of reconfigurable systems. This node architecture can
be tailored for prototyping of a variety of system platforms.
For example, the Local Interconnect(s) cloud shown in Fig. 1
in some cases may represent one level of interconnect (e.g. a
direct HyperTransport connection between CPUs, FPGAs, and
main memory) and in other cases a hierarchy of interconnects
(e.g. CPUs and main memory residing on the front-side
interconnect such as HyperTransport, with FPGAs attached
via a bridge to an I/O interconnect such as PCI-Express).
In order to tackle the problem of optimally mapping arbi-
trary applications to specific target architectures, the modeling
framework is split into two separate domains the application

U Memory (D

Local Network C)
Int s Interface
nter - -----

RC Device

Node Architecture

Fig. 1. Generic RC System Architecture

domain and the simulation domain. This split allows users
to characterize applications independently of the candidate
system architectures while supporting concurrent model devel-
opment that is independent of the potential applications. This
independence offers a high level of data and model reusability
and modularity which in turn facilitates rapid analyses of
numerous virtually prototyped systems and applications. The
overall structure of our simulation framework, and the key
steps within each domain, are illustrated in Fig. 2. The
remainder of this section provides details and examples on the
procedures used in the application and simulation domains.

Fig. 2. RC framework diagram

A. Application domain
The purpose of the application domain is to collect char-
acterization data on a selected application that captures its
inherent behavior, with the intent of creating some form of
stimulus data for the simulation models. The steps that make
up the application domain are shown by the ovals in Fig. 2.
Hardware core characterization defines the behavior of the
kernel or function to be performed within the RC device.
The computation time for an RC core can be obtained from
two sources. The first source uses experimentally measured
delays from a hardware core implementation, while the second
uses delays supplied from simulations of the hardware design
from vendor-supplied tools. Both methods provide reasonably

accurate results assuming deterministic behavior of the RC
devices. Computation time is not the only parameter required
to characterize a hardware core. Other key parameters include
core size, input data size, and output data size. The core size
parameter is important in order to manage how many cores
can fit onto a single RC device. This management capability
enables us to consider performance gains when scaling up
device size by squeezing more cores onto the fabric at once,
thus executing on more data in parallel. It also supports
the modeling of partial reconfiguration by allowing cores to
be exchanged within the RC device during the simulation.
However, an in-depth study of this technique was not included
in this paper. The final parameters, the input and output data
sizes, allow accurate modeling of transactions with the RC
device. These parameters are critical in order to accurately
capture the communication delays incurred when passing data
between various node components and the RC device.
Another vital step in the application domain that can be
conducted in parallel with characterizing the hardware core
is the application characterization stage. This step involves
identifying and gathering a sequence of key events that defines
the performance of the target application. The events currently
supported within this framework include the computation
conducted by the host processor, the computation executed in
the RC device, and the communication between RC nodes. A
specific sequence of these three events can be used to capture
the behavior of any parallel or serial RC application. However,
gathering these sequences of events is a key challenge when
dealing with RC applications due to the large number of
programming interfaces implemented to transfer data to and
from RC devices. The framework addresses this challenge by
using a scripting language that allows the user to manually
represent the behavior of an RC application. When using
this approach, classic instrumentation tools can be employed
to gather the data relevant to defining host computation and
inter-node communication. Presently, the incorporation of RC
events into scripts must be performed manually, since no
common standard exists that defines interactions with RC
devices. In the future, the adoption of a standard RC program-
ming interface, such as that being explored by the USURP
project [3] or the U-APPREQ work group in the OpenFPGA
consortium [4] would allow the automatic characterization of
these events. However, until a common standard is finalized,
manual script generation provides the most flexible alternative
to cover the wide range of RC application and RC device
combinations. When a common standard is adopted, automatic
generation of application scripts within our scripting syntax
can be implemented. The proceeding paragraph presents spe-
cific details on the scripting language developed for stimulat-
ing RC simulation models.
The final step in the application domain deals with gen-
erating scripts that represent the behaviors of the target
applications. The information collected during the application
and core characterization steps define both the structure and
the values needed to construct scripts that accurately exercise
the computational subsystems as if the actual application was

#Sample RC Script
#Setup RC device
RC INITFABRIC 1 10000 2000
#Configure RC device with FFT core
RC CORECONFIG 1 FFT 500 150 650 2500 1024 1024 50 25
#Host processor compute block 1.12 seconds
COMP 1.12E6
#Define loop with 100 iterations
#Compute at host processor for 450 microseconds
COMP 450
#Send 8192 bytes of data to the FFT core, blocking
#End loop

Fig. 3. Sample RC Script

executing on the target system. The framework incorporates a
custom scripting syntax to facilitate script construction. Fig. 3
illustrates a sample RC script. The script begins by initializing
the RC device with key parameters such as device size and
maximum clock frequency such that the necessary models can
correctly manage the device. The next line configures a single
FFT core on the device while providing core details obtained
during the hardware core characterization step. In this case,
the FFT core is defined to occupy 2500 slices of the 10000-
slice device, operate at 150 MHz, and execute on 1024-byte
data chunks in 650 clock cycles. After the fabric and the
FFT core have been configured, the script starts executing
the computation section of the application beginning with
1.12 seconds of computation at the host processor. The script
then proceeds by defining 100 iterations of host computation
followed by the execution of an FFT. Once the 100 iterations
have completed, the script is complete and the simulation
will terminate. It must be noted that the current scripting
syntax was designed to support most of the functionalities
used in current RC applications and is easily expandable to
support other RC events that may arise as the technologies
and programming interfaces mature.

B. Simulation domain
Now that the application domain has been described, we can
transition to the simulation domain. The purpose of the simu-
lation domain in our framework is to provide an environment
for developing and simulating virtual prototypes of candidate
systems. The steps that make up the simulation domain are
illustrated by the rectangles in Fig. 2. The first step is the
model development stage. In this stage, models of key system
components are constructed. The simulation environment used
for building component and system models is Mission-Level

Designer (MLD) from MLDesign Technologies [5]. MLD is a
graphical discrete-event simulation tool that supports modular,
hierarchical designs of arbitrary systems allowing for quick
development times of models with varying degrees of fidelity.
Since the goal of our framework is performance prediction
of RC systems, component models do not incorporate the
actual mechanisms to manipulate data. In fact, the actual
data is abstracted away by only considering how much data
rather than what data. As a result, the models focus on the
performance timing of the interactions between components
exercised by the application. Focusing on system performance
facilitates quick model design times (due to the reduced
detail associated with each component model) and improved
simulation speed (due to less processing needed to execute
each component model).
An overview of the key component models in the current
RC model library is described below. The first component, the
RCScriptParser, converts script commands into appropriate
data structures used by the models. The RCMiddleware model
manages transactions between the host processor and the
RC device, and also includes performance-critical overhead
incurred by the device drivers. The RCCore model was devel-
oped as a generic black-box model, such that any hardware
core could be represented. The black-box model uses the core
size, input data size, output data size, and computational delay
as characterized in the application domain to abstract away
the data manipulation inside the core. This abstraction allows
for faster simulations of systems while producing reasonably
accurate results. Meanwhile, the parameters of the core that
dictate performance can be scaled in order to predict the
core's execution time on future generations of the RC device.
The SimpleBus model captures the communication delays of
data transfers between hardware components sharing the bus
through the use of simple bandwidth and latency calculations
while also considering contention. From these models, higher-
level complex components such as the host processor or RC
device can be created.
Once the component models have been developed, the
next step in the simulation domain is the calibration of
those models. In the model calibration stage, the parameters
of a component model are selected such that the model's
performance matches that of the corresponding real-world
technology. In order to calibrate the component models, exper-
imental measurements must be obtained from benchmarks that
exercise the target component. Once the experimental data has
been gathered, the model parameters are tuned to match the
measured data points. Various metrics such as average error
or mean squared error can be used to match model results
against the experimental data.
The current framework focuses on the components exer-
cised when transferring data between the host processor and
the RC device due to the common bottleneck that arises at this
data path in many RC applications. As such, the corresponding
component models, specifically the local interconnect (e.g. a
bus) and RC device drivers, require an in-depth calibration
process in order to accurately capture the performance of the

data transfers. However, in this instance, it is very difficult
to benchmark either the bus or the drivers in isolation due
to the complexity of the system and the proprietary nature
of the device drivers, respectively. To resolve this problem,
the performance of transactions between the host and RC
device are measured as a whole, such that the measurements
correspond to a interconnect technology (e.g. PCI-X, PCI-
Express, HyperTransport) and the optimal set of parameters
for the drivers is obtained using a parameter solver developed
by the authors. The parameter solver defines bounds on the
RC driver's latency and bandwidth from the experimental data,
then iterates over each range to find the parameter combination
that optimizes the chosen error metric, within a definable gran-
ularity. Furthermore, a penalty factor is included to represent
the declining throughput performance sometimes experienced
for extremely large data transfers, possibly caused by memory
buffer overruns within the target device or operating system.
The implementation details of the solver are outside the scope
of this paper, but calibration results using the parameter solver
are presented in Section IV.
The final stage of the simulation domain is the system
analysis. In this stage, the RC application script is processed
by the system models producing performance results for each
candidate system architecture. The performance results can be
used to identify bottlenecks in the virtually prototyped systems
and conduct what-if scenarios and tradeoff analyses with
respect to various design options such as algorithm decompo-
sitions and mappings and individual component performance.
In the next section, the steps outlined above are performed
with an application and two target systems to demonstrate the
capabilities of this approach.

In this section, the simulation framework described in the
previous section is demonstrated and validated, using a node-
level case study. The goal of this case study is to demonstrate
the steps involved in the process of simulating an arbitrary
reconfigurable system and application within this framework.
The results presented during this case study are intended to
serve as a validation of the framework, while later illustrating
its capabilities and features.
A set of experiments were conducted using two variations
of a single-node system. Table I summarizes the two systems
used in these experiments. For the validation of each system,
experiments were conducted using a i li' k aperture radar
application (SAR). SAR is a high-resolution, broad-imaging
application used for reconnaissance, surveillance, targeting,
navigation, and other operations requiring highly detailed,
terrain-structural information. The imaging process iterates
over four stages: range compression, azimuth transform, range
cell correction, and azimuth compression [6]. The SAR ap-
plication under study uses data with 6,144 ranges and 4096
azimuths per iteration, or patch. Multiple patches can be
combined to resolve larger images.
The 1D-FFT is the main computational component of the
range compression, azimuth transform, and azimuth com-


System Host Processor System Memory RC Device I/O Bus
Delta P4 Xeon 3.2 GHz 2 GB PC2700 DDR-SDRAM Nallatech H101-PCIXM board (Xilinx V4LX100) 133 MHz PCI-X
Kappa P4 Xeon 2.4 GHz 1 GB PC2100 DDR-SDRAM Nallatech BenNUEY board (Xilinx V2Pro-50) 66 MHz PCI

pression stages. In order to support all of these stages, the
hardware implementation of the FFT uses a pipelined radix-2
structure that is capable of performing power-of-2 FFTs (or
inverse FFT) up to 8,192 elements in length. Each element
processed by the FFT is a 16-bit fixed-point complex number
(32 bits total). Transforms performed along the range dimen-
sion use 8,192-point FFTs, while those performed along the
azimuth dimension use 4,096-point FFTs. SAR was chosen
for this case study because it includes a well-defined computa-
tional kernel (i.e. the FFT) that is sometimes a prime candidate
for implementation inside the FPGA. The remainder of this
section illustrates each step of the framework for validating
and predicting the performance of SAR.

A. SAR characterization
The first steps taken to validate the framework were to
characterize the application and hardware core. The applica-
tion characterization was originally performed using a pure
software version of SAR, with instrumentation code added to
capture timing data for the various sections of the application.
From the timing data, as well as an understanding of the
algorithm, we identified the FFT as a repeatedly used function
that consumes a Min ilk.,iim portion of the total execution time.
Thus, corresponding code was added to the SAR code in
order to produce an RC version of the algorithm and an
FFT hardware core was obtained and characterized through
experimental measurements.
Once the characterization data was obtained, a script of the
SAR algorithm was manually written to incorporate measured
host processor computation as well as the necessary events
that offload the FFT tasks to the RC device. For each system,
two versions of the SAR script were generated, the SW-Based
Timing script and the RC-Based Timing script. Both of these
scripts represent an implementation of the SAR algorithm that
uses an FPGA to perform the FFT operations. The overall
structure of each script version is the same, since they contain
the same sequence of computation blocks in the host processor
and FFT operations using the FPGA. Where these two script
versions differ is in the length of time spent within each host
processor computation block. For the SW-Based Timing script
the timing data for each of the host processor compute blocks
is measured from a pure software version of SAR. The RC-
Based Timing script derives host processor computation block
timing data from the RC version of SAR that incorporates
code to offload FFT operations to the system's FPGA device.
A comparison between both script versions was performed
to study possible variations in performance predictions due
to differences in host computation times between the two
versions of the SAR application.

B. Model development and calibration
In parallel with the characterization processes, system
models for the single-node testbeds were built. The primary
components of the RC node model include a host CPU, RC
device, and bus model. The processor model incorporates
the RCScriptParser and RCMiddleware models described in
Section III. The RC device model uses the RCCore model
which was configured to capture the behavior of the FFT
used in the SAR application. Finally, the SimpleBus model
was used as the interconnect between the host CPU and the
RC device.
Once completed, the system model parameters were then
calibrated to match the performance characteristics of the
target systems. For this case study, we focused our calibration
efforts on the interconnect between the host processor and RC
device. Figs. 4a and 4b show the experimental results versus
simulation results in terms of throughput values for device
reads and writes, respectively, on the Delta system, while
Figs. 5a and 5b show the same on the Kappa system. The
No Chokepoint curve represents the resulting calibration data
points from the parameter solver when it does not incorporate
the detection of transfer penalties for large transfers, while
the Chokepoint data does consider these penalties. A No
Chokepoint curve was not included in the Delta write results
(Fig. 4b), since the existence of a hardware overflow was
obvious and severe, thus the Chokepoint curve was the only
logical fit to the experimental data. Similarly, a Chokepoint
curve was not included in the Kappa write results (Fig. 5b),
since a hardware threshold was not observed in the experi-
mental data. The simulation results for the data sets in these
four charts yielded a mean percent error that ranged between
2.1% and 5.1% versus their experimental counterparts. Those
average values are very encouraging, especially considering
that they are mildly inflated by single erratic experimental data
points that yield double-digit percent errors when compared to
the calibrated model results. Additionally, the data from the
Kappa varies widely from the Delta system, illustrating the
importance of a flexible calibration approach that can handle
such disparate behaviors. The accurate and flexible calibration
approach enables accurate modeling of various systems and
applications as described the following paragraphs.

C. System validation with SAR
After calibrating the models, the manually generated SAR
scripts are fed into the system models for system analysis.
The results from the simulation of the SAR script can be
compared to the execution times of SAR on the two exper-
imental testbeds with the intent of validating the simulation
framework. The experimental run-times were gathered using

- Experimental -A-Chokepoint -* No Chokepoint



20f -

1 10 100 1,000 10,000 1O0000 1,000,000 10,D000,000
Transfer Size (Bytes)

Experimental A Chokepoint

S10 Tr 10 1a000 13S000 100CB 1t00 00) 1000
Transfer Size (Bytes)

(a) Reads (b) Writes
Fig. 4. Throughput for device reads (a) and writes (b) on the Delta system

-l- Experimental -A-Chokepoint No Chokepoint

-- ,- ,

1 10 100 1,000 10,000 100000 1,000,00 10,OD 000,000
Transfer Size (Bytes)

-I- Experimental -- No Chokepoint


1 10 100 1000 10,000 100000 1,000,000 10,000,000
Transfer Size (Bytes)

(a) Reads (b) Writes
Fig. 5. Throughput for device reads (a) and writes (b) on the Kappa system

the version of SAR with code to offload the FFTs to the
RC device. For each system, the two different versions of
the SAR script, the SW-Based Timing script and RC-Based
Timing script, were used as inputs to the system models, and
the performance prediction results were recorded.
Table II summarizes the results of the SAR validation
experiments. On the Kappa system, our simulation-based
predictions of execution time were within 6.8% and 4.6% of
the actual experimental application results for the two script
versions, respectively. On the Delta system, the accuracies of
the simulations improved to 2.6% and 2.1% for the two scripts.
These results show that our system models have accurately
predicted the performance of the entire SAR application for
each system, thus providing a basis that demonstrates our
prediction results will be accurate and insightful when explor-
ing various system modifications. Meanwhile, the runtimes
for each simulation ranged between 12 and 14 seconds, thus
providing a fast and accurate simulative analysis.
It should also be noted that for each system, the RC-Based
Timing script results provided a smaller margin of error versus
the SW-Based Timing script. Thus, in both cases using the RC
version of the application for script generation led to more
accurate predictions. This observation is due to the differences
in timing of the host processor computation blocks. Since the


System Exp. SW-Based Diff. RC-Based Diff.
Timing Timing
Delta 42.63s 41.53s 2.58% 41.75s 2.06%
Kappa 77.11s 82.32s 6.76% 80.68s 4.63%

length of these computation blocks differed when the FPGA
was introduced into the application, only the RC-Based Timing
script captures those differences. Thus, the RC-Based Timing
script can account for various nuances that take place within
the system when a reconfigurable device is utilized. One
possible reason for the observed differences in performance
is cache-fetching patterns, which can change the number of
cache hits in a host processor computation block.

D. SAR simulative study
Now that we have validated the accuracy of our system
models for SAR, we can now predict and analyze the effects
of system modifications on the application's performance. The
simulations conducted in this section observe the impact on
overall system performance due to varying the characteristics
of the local interconnect and middleware models for the
Delta system. Four parameter sets are examined for the local



interconnect with each set representing a specific generation
of the PCI interconnect technology, including 64-bit, 133
MHz PCI-X, xl PCIe, x8 PCIe, and x16 PCIe. For each PCI
technology, four parameter sets for the middleware capabilities
are considered. The Base MW set uses the baseline values
obtained during the calibration of the experimental Delta
system. The 1/2L set uses latency values that are one-half
that of the baseline values. The 2xBW set employs throughput
values that are twice that of the baseline values, while the
Combo set combines the halved latency values from the 1/2L
set and the doubled throughput values in the 2xBW set.


Base MW


xl PCIe

x8 PCIe

x16 PCIe

The results from the I/O study are presented in Table III. As
expected, the improved interconnect technologies yield faster
SAR execution times than the original PCI-X configuration.
However, the improvements were modest, as scaling from
PCI-X in the baseline case to a 16-lane PCIe interface only
led to a 2.24 second decrease in run-time. Meanwhile, halving
the middleware latency had little effect on the overall system
performance, although simply doubling the throughput of the
device middleware (2xBW) led to a 3.45 second drop in run-
time from the baseline. Thus, it appears that the middleware,
namely the throughput of the middleware, is the primary
bottleneck of those evaluated with this system for SAR.
Despite the high number of transfers that occur during each
application run, the time gained during each transfer from the
latency improvement was far outweighed by the gains from
the bandwidth increases for the large data transfers.
In this study, major increases in the capabilities of the
local interconnect led to modest gains in the results due to
other bottlenecks insight which illustrates the effectiveness
of simulation in predicting performance. Of course, with other
system architectures, such as multiple-FPGA nodes, use of
a more powerful technology such as PCI-Express may be
expected to achieve a pronounced improvement. This study
serves as a simple yet valuable example of the potential pro-
vided by this simulation framework. Numerous other tradeoff
analyses can easily be performed within this framework, such
as scaling the size and performance of the RC device and
scaling the number of nodes in the system. These analyses
can provide important insight into design decisions regarding
current and future RC system architectures.

Reconfigurable devices such as FPGAs are becoming an
increasingly important option for accelerating applications
in high-performance computing, from satellites to super-
computers. Unfortunately, RC devices cannot achieve good

speedup with all applications and their mappings to a par-
ticular platform, and developing reconfigurable applications
and platforms can be a costly and time-consuming process.
Meanwhile, simulation can be a relatively quick and cost-
effective means to evaluate an application's performance on
platforms that incorporate reconfigurable computing devices.
In this paper, a framework for fast and accurate simulations
of applications on reconfigurable systems was introduced.
First, the framework and methodology of our modeling
approach for reconfigurable systems was presented. This
framework divides its process into two domains, the
application and simulation domain, which provides a
methodology for mapping arbitrary applications to a variety
of RC systems facilitating rapid in-depth performance
projections and analyses. Finally, an application case study
using the SAR application was performed. Validation
results showed our system models could predict the overall
performance of the application within a modest range of error.
After the application validation tests, a simulation study was
performed to demonstrate the capabilities of the framework
to identify bottlenecks when running the SAR application
on various system configurations. Future work in this area
will include expanding the model library to support parallel
and large-scale reconfigurable systems and applications, and
developing characterization and calibration techniques for
additional resources such as the memory hierarchy.

This work was supported in part by the I/UCRC Program
of the National Science Foundation under Grant No.
EEC-0642422. The authors gratefully acknowledge vendor
equipment and/or tools provided by Xilinx, MLDesign
Technologies, and Nallatech that helped make this work

[1] M. Smith and G. Patterson, "Parallel Application Performance on Shared
High Performance Reconfigurable Computing Resources," Performance
Evaluation, Vol. 60, No. 1-4, May 2005, pp. 107-125
[2] K. Bondalapati, and V. Prasanna, "Reconfigurable Computing i..'i,
Proc. IEEE, Vol. 90, No. 7, July 2002, pp. 1201-1217.
[3] B. Holland, J. Greco, I. Troxel, G. Barfield, V. Aggarwal, and A. George,
"Compile- and Run-time Services for Distributed Heterogeneous Recon-
figurable Computing" Proc. International Conference on Engineering of
Reconfigurable Systems and Algorithms (ERSA), Las Vegas, NV, June
26-29, 2006.
[4] OpenFPGA, http://www.openfpga.org.
[5] G. Schorcht, I. Troxel, K. Farhangian, P. Unger, D. Zinn, C. Mick,
A. George, and H. Salzwedel, "System-Level Simulation Modeling
with MLDesigner," Proc. 11th IEEE/ACM International Symposium on
Computer and Telecommunication Systems (MASCOTS), Orlando, FL,
Oct. 12-15, 2003, pp. 207-212.
[6] P. Meisl, M. Ito, and I. Cumming, "Parallel Synthetic Aperture Radar
Processing on Workstation Networks," Proc. Of 10th International Par-
allel Processing Symp. (IPPS), Washington, DC, Apr. 15-19, 1996.

University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - - mvs