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GPU610/Cosmosis

10,514 bytes added, 13:38, 13 April 2013

→‎Assignment 3: code

We initially intended on using the fast-math library provided by CUDA. At first our results were marginally faster than our previous code. Though after some optimizations we discovered that our code actually performed better than the fast-math library. With fast-math, it took 0.451889 seconds to process 1000 bodies for 512 samples, conversely without fast-math we got 0.409581 seconds, which is a considerable improvement.

==== Optimized Code ====

void __global__ SimCalc(BodyArray a)

{

int_fast32_t idx = blockIdx.x * blockDim.x + threadIdx.x;

if (idx < a.size) {

const _T G = 6.67384f * pow(10.0f, -11.0f);

//precompute positions at index

const _T px = a.array[idx].Position.x;

const _T py = a.array[idx].Position.y;

//mass at the index

const _T M_idx = G*a.array[idx].Mass;

a.array[idx].Force = vec2_t();

for (int_fast32_t j(0); j != a.size; ++j) {

if (idx != j) {

_T dx = a.array[j].Position.x - px;

_T dy = a.array[j].Position.y - py;

_T r = sqrt(dx*dx + dy*dy);

_T F = (M_idx*a.array[j].Mass)/(r*r);

a.array[idx].Force.x += F * (dx / r);

a.array[idx].Force.y += F * (dy / r);

}

void __global__ SimTick(BodyArray a, _T dt)

{

int idx = blockIdx.x * blockDim.x + threadIdx.x;

if (idx < a.size)

{

_T mass = a.array[idx].Mass;

a.array[idx].Velocity.x += dt * (a.array[idx].Force.x / mass);

a.array[idx].Velocity.y += dt * (a.array[idx].Force.y / mass);

a.array[idx].Position.x += dt * a.array[idx].Velocity.x;

a.array[idx].Position.y += dt * a.array[idx].Velocity.y;

}

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