Optimising radix-2 FFT C code - c

Question

I'm a beginner in C programming.
I am current trying to work on a project requiring 1024-point FFT implementation using radix-2, Decimation-in-frequency.
I attach the FFT function C code below.
How can i increase the performance by modifying the C code.
#include "i_cmplx.h" /* definition of the complex type */
#include "twiddle1024.h" /* quantised and scaled Twiddle factors */
#define LL 1024 /* Maximum length of FFT */
/* fft radix-2 funtion using Decimation In Frequency */
//#pragma CODE_SECTION(fft, "mycode"); // Makes the program run from internal memory
void fft(COMPLEX *Y, int N) /* FFT(input sample array, # of points) */
{
int temp1R, temp1I, temp2R,temp2I; /* 32 bits temporary storage for */
/* intermediate results */
short tempR, tempI, c, s; /* 16 bits temporary storages */
/* variables */
int TwFStep, /* Step between twiddle factors */
TwFIndex, /* Index of twiddle factors */
BLStep, /* Step for incrementing butterfly index */
BLdiff, /* Difference between upper and lower butterfly legs */
upperIdx,
lowerIdx, /* upper and lower indexes of buterfly leg */
i, j, k; /* loop control variables */
BLdiff=N;
TwFStep=1;
for(k=N;k>1;k=(k>>1)) /* Do Log(base 2)(N) Stages */
{
BLStep=BLdiff;
BLdiff=BLdiff>>1;
TwFIndex=0;
for(j=0;j<BLdiff;j++)/* Nbr of twiddle factors to use=BLDiff */
{
c=w[TwFIndex].real;
s=w[TwFIndex].imag;
TwFIndex=TwFIndex+TwFStep;
/* Now do N/BLStep butterflies */
for(upperIdx=j;upperIdx<N;upperIdx+=BLStep)
{
/* Calculations inside this loop avoid overflow by shifting left once
the result of every adittion/substration and by shifting left 15
places the result of every multiplication. Double precision temporary
results (32-bit) are used in order to avoid losing information because
of overflow. Final DFT result is scaled by N (number of points), i.e.,
2^(Nbr of stages) =2^(log(base 2) N) = N */
lowerIdx=upperIdx+BLdiff;
temp1R = (Y[upperIdx].real - Y[lowerIdx].real)>>1;
temp2R = (Y[upperIdx].real + Y[lowerIdx].real)>>1;
Y[upperIdx].real = (short) temp2R;
temp1I = (Y[upperIdx].imag - Y[lowerIdx].imag)>>1;
temp2I = (Y[upperIdx].imag + Y[lowerIdx].imag)>>1;
Y[upperIdx].imag = (short) temp2I;
temp2R = (c*temp1R - s*temp1I)>>15;
Y[lowerIdx].real = (short) temp2R;
temp2I = (c*temp1I + s*temp1R)>>15;
Y[lowerIdx].imag = (short) temp2I;
}
}
TwFStep = TwFStep<<1; /* update separation of twiddle factors)*/
}
/* bit reversal for resequencing data */
j=0;
for (i=1;i<(N-1);i++)
{
k=N/2;
while (k<=j)
{
j = j-k;
k=k/2;
}
j=j+k;
if (i<j)
{
tempR=Y[j].real;
tempI=Y[j].imag;
Y[j].real=Y[i].real;
Y[j].imag=Y[i].imag;
Y[i].real=tempR;
Y[i].imag=tempI;
}
}
return;
}