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BENCHMARK SUITE RADAR SIGNAL & DATA PROCESSING CERES EPC WORKSHOP 2008-10-01.

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Presentation on theme: "BENCHMARK SUITE RADAR SIGNAL & DATA PROCESSING CERES EPC WORKSHOP 2008-10-01."— Presentation transcript:

1 BENCHMARK SUITE RADAR SIGNAL & DATA PROCESSING CERES EPC WORKSHOP 2008-10-01

2 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

3 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

4 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

5 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

6 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

7 THE PROVIDED SOURCE CODE Single processor code For comparisons/reference Excecutable ”spec” Basis for parallel code, if applicable

8 Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level


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