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CHIRP SIGNAL ANALYSIS

Cesar A Aceros Moreno

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DCFT Definition

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CHIRP Signal

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OUTPUT OF THE DCFT

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Dataflow of the DCFT

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Structure of the datafiles• datain.txt

1281.000000 x[0]0.000000 x[1]0.555570 x[2]

….

0.831470 x[124]-0.195090 x[125]0.980785 x[126]-0.707107 x[127]

• dataout.txt

128-0.000000 real.x[0,0]-0.000000 imag.x[1,0]0.000000 real.x[1,0]0.000000 imag.x[1,0]

….

-0.000000 real.x[127,0]1.414215 imag.x[127,0]-1.414213 real.x[0,1]0.000000 imag.x[0,1]

….

-0.000000 real.x[127,1]0.000000 imag.x[127,1]1.913880 real.x[0,2]0.580570 imag.x[0,2]

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DCFT IMPLEMENTATIONS

DCFT

PCIMPLEMENTATION

MatlabC

CLUSTERIMPLEMENTATION

PLANET LABSerial

Parallel

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DCFT Serialmain(){ char temp[1]; float *data, *dataw; long int nn=0; int i=0,l=0; FILE *infile,*outfile; if ((infile = fopen("datain.txt", "r")) == NULL) fprintf(stderr,"Cannot open file"); fscanf(infile,"%d",&nn); printf("Valor size: %d\n",nn); data = malloc(sizeof(float)*nn*2); dataw = malloc(sizeof(float)*nn*2); for (i=1;i<2*nn+1;i++){ fscanf(infile,"%f",&data[i]); } fclose(infile);

outfile = fopen("dataout.txt", "w");fprintf(outfile,"%ld\n",nn); for (l=0;l<nn;l++){ printf("Layer %ld \n",l); for (i=1;i<2*nn+1;i++){ dataw[i]=data[i]; } haddamard(dataw,nn,l); four1(dataw,nn,1); for (i=1;i<2*nn+1;i++){ dataw[i]=dataw[i]/sqrt(nn); fprintf(outfile,"%f\n", dataw[i] ); }

} fclose(outfile);

}

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Experiment Description: createdata

• For the experiment the sample size was limited to be within the set {6000, 7000, 8000}

• The sample size was varied from one experimental run to the next.

• The values of K0 and L0 where chosen randomly on each run.

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Experiment Description: dcft

• Input:– Reads the file datain generated by createdata.

• Output:– Calculates the DCFT of the input data and

prints the results to a file named dataout.

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Experiment Description

• Of the two programs dcft is the only one of interest.

• Utilized Paradyn to instrument and analyze the performance of dcft.

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Analyzing Performance with Paradyn

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Analyzing Performance with Paradyn: Process Specification

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Analyzing Performance with Paradyn: Process Specification

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Analyzing Performance with Paradyn: The Where Axis

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Analyzing Performance with Paradyn: Metric Specification

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Analyzing Performance with Paradyn: Metric Descriptions

• cpu – Each bin represents the percentage of cpu time spent during the corresponding time interval. Aggregation is total cpu time over an interval.

• cpu_inclusive – Same as cpu but includes called procedures in the process time calculation.

• exec_time - Each bin represents the elapsed wall clock time per unit during the corresponding time interval. Aggregation is the sum over the interval.

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Analyzing Performance with Paradyn: Metric Descriptions

• io_bytes – This metric represents the number of bytes for Input/Output operations. Currently, only “read” and “write” are supported as input/output operations for UNIX, MPI, and PVM.

• io_ops – Number of Input/Output operations. IO operations are the same as for io_bytes.

• io_wait – Time spent during Input/Output operations. IO operations are the same as for io_bytes.

• io_wait_inclusive – Same as io_wait but includes called procedures in the process time calculation.

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Analyzing Performance with Paradyn: Bottleneck Analysis

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Analyzing Performance with Paradyn: Results

Sampling Size = 7000, K0 = 15, L0 =15

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Analyzing Performance with Paradyn: Results

Sampling Size = 7000, K0 = 15, L0 =15

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Analyzing Performance with Paradyn: Results

Sampling Size = 8000, K0 = 10, L0 =10

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Analyzing Performance with Paradyn: Results

Sampling Size = 8000, K0 = 10, L0 =10

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Analyzing Performance with Paradyn: Results

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FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions, of arbitrary input size, and of both real and complex data (as well as of even/odd data, i.e. the discrete cosine/sine transforms or DCT/DST). We believe that FFTW, which is free software, should become the FFT library of choice for most applications.

#include <fftw_mpi.h>

#include <fftw.h>

plan = fftw_mpi_create_plan(MPI_COMM_WORLD, NX , FFTW_FORWARD, FFTW_IN_PLACE);

fftw_mpi(plan, 1, datainC, dataout);

fftw_mpi_destroy_plan(plan);

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Results of Execution Time.

CLUSTER IMPLEMENTATION OF THE DCFT

y = 5E-06x2 - 0.0016x + 0.374

050

100150200250

300350

0 2000 4000 6000 8000 10000

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PLANET LAB

• CHIRP IMPLEMENTATION

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