Benchmarks

pancakesbootAI and Robotics

Nov 24, 2013 (3 years and 8 months ago)

91 views

BENCHMARK SUITE

RADAR SIGNAL & DATA PROCESSING

CERES EPC WORKSHOP 2008
-
10
-
01

THE BENCHMARK SUITE

The purpose is to evaluate processing architectures with regard to
radar signal & data processing requirements



The suite comprises


Signal processing kernels


“front
-
end” processing


data
-
independent, stream
-
oriented


Information and knowledge processing kernels


“back
-
end” processing


data
-
dependent, thread oriented


Application examples


some of the kernels are used


illustrates complications in data access/movement


It is to a large extent based on the HPEC Challenge benchmark suite

THE HPEC CHALLENGE BENCHMARK
SUITE

Created under the DARPA PCA program, introduced 2005

Nine kernel benchmarks:

Signal processing


Time
-
domain and frequency
-
domain FIR filters


QR factorization


Singular value decomposition


Constant false
-
alarm rate detection

Information and knowledge processing


Pattern matching


Graph optimization via genetic algorithm


Real
-
time database operation

Communication kernel


Corner turn (memory rearrangement) of a data matrix

Metrics

Latency, throughput, efficiency

MORE KERNELS

Complement to the HPEC Challenge suite


Fast Fourier Transform


The free FFTW package from MIT


C subroutine library for computing the DFT in one or more
dimensions


Benchmark source code and methodology are available


Interpolation kernels


Cubic interpolation


Bi
-
cubic interpolation


Source code is available

APPLICATIONS

signal processing
kernels

processing chain 1

processing chain 2

different processing directions in chain

channel

range

pulse

data access/movement complications

when combining kernels

APPLICATIONS


A simplified Doppler signal processing chain


problem
: processing along different directions in data set


benchmark
: Doppler filtering, pulse compression, CFAR detection


Space
-
Time Adaptive Processing (STAP)


problem
: weight calculations based on a sliding volume in a 3D data
set


benchmark
: QR decompositions of matrices formed from data in a
sliding volume

Synthetic Aperture Radar (SAR) processing


problem
: 2D interpolations along tilted paths in memory


benchmark
: elementwise addition of data from two matrices
accessed along tilted lines

THE PROVIDED SOURCE CODE


Single processor code


For comparisons/reference

Excecutable ”spec”

Basis for parallel code, if applicable


Click to edit Master title style

Click to edit Master text styles


Second level


Third level


Fourth level


Fifth level