# Computation of pi in CUDA Measure circumference of the circle by counting pixels on the edge of the circle. Compute value of pi using this circumference.

## Presentation on theme: "Computation of pi in CUDA Measure circumference of the circle by counting pixels on the edge of the circle. Compute value of pi using this circumference."— Presentation transcript:

Computation of pi in CUDA Measure circumference of the circle by counting pixels on the edge of the circle. Compute value of pi using this circumference

Motivation Say I have a digital camera and magnification system which gives me pixel size of 1 unit on object plane The image obtained is binary i.e. ‘1’ inside the circle and ‘0’ elsewhere. What are my limitations in measurement of local radius of curvature ? My hypothesis is that if I can measure circumference or compute ‘pi’. I can measure that radius of curvature.

Algorithm Pixel counting performed in region 0≤ x ≤ R/sqrt(2). In this region, Possible states Not Possible state pi = 4 x d/R Change ‘R’ and see the error in pi ‘d’ is the counter which keep tracks of contour length

Implementation in CUDA The region from 0 ≤ x ≤ R/sqrt(2) is further divided into segments. The contour length of each segment is computed independently by separate threads. The sum of these contour lengths gives us 1/8 th of the circumference of the circle. ‘pi’ value and error value are computed and outputted at the end of the program.

Some important parts of the CUDA code float R = 10.0*sqrt(2.0); int Nt = 8; dist_h = (float*)malloc(fsize); status_h = (int*)malloc(isize); start_h = (int*)malloc(isize); end_h = (int*)malloc(isize); Variables for inputting Radius And # of threads required for computation Variables for tracking contour length, Errors, start and end ‘x’ value for each thread /* Kernel */ __global__ void distance(int *start, int *end, float *dist, int *status, float R) { int yold = floor(0.5+(sqrt(R*R-(start[threadIdx.x]- 1.0)*(start[threadIdx.x]-1.0)))); int d = 0; int flag = 0; for (int k=start[threadIdx.x]; k <= end[threadIdx.x]; k++) { int ynew = floor(0.5+(sqrt(R*R-k*k))); if (ynew == yold) { d = d + 1.0; } else { if (ynew < yold) { d = d + 1.41421356; } else { flag = 1; } } yold = ynew; } dist[threadIdx.x] = d; status[threadIdx.x] = flag;} Kernel function The function computes contour length of a circular segment with radius ‘R’

Some important parts of the CUDA code float sum = 0; for (int i=0; i { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/13/3622965/slides/slide_6.jpg", "name": "Some important parts of the CUDA code float sum = 0; for (int i=0; i

Sources of error Large ‘R’ compared to pixel resolution Computed pi = 2.8567 for R=100xsqrt(2) Small ‘R’ compared to pixel resolution Computed pi = 3.7712 for R=3xsqrt(2) Curvature change is not picked up because of poorer pixel resolution. Causes the circumference to be underestimated Highly curved boundaries are not captured because of poorer pixel resolution. Causes the circumference to be overestimated

Reducing uncertainty in ‘R’ estimation Camera pixels record light intensity. If we can predict the intensity distribution close to edge and if that intensity distribution spreads over 3 or more pixels, we can possibly get subpixel resolution. If R = f(x) is known, we can use that information to reduce the uncertainty in ‘R’

Download ppt "Computation of pi in CUDA Measure circumference of the circle by counting pixels on the edge of the circle. Compute value of pi using this circumference."

Similar presentations