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==== Calculation of Pi ====
For this assessment, we used code found at [https://helloacm.com/cc-coding-exercise-finding-approximation-of-pi-using-monto-carlo-algorithm/ helloacm.com]
<syntaxhighlight lang="cpp">
int main() {
srand(time(NULL));
cout.precision(10);
std::chrono::steady_clock::time_point ts, te;
const double N[] = {1e1,1e3, 1e4, 1e5, 1e6, 1e7, 1e8};
for (int j = 0; j < (sizeof(N) / sizeof(N[0])); j ++) {
ts = std::chrono::steady_clock::now();
int circle = 0;
for (int i = 0; i < N[j]; i ++) {
double x = static_cast<double>(rand()) / static_cast<double>(RAND_MAX);
double y = static_cast<double>(rand()) / static_cast<double>(RAND_MAX);
if (x * x + y * y <= 1.0) circle ++;
}
te = std::chrono::steady_clock::now();
cout << N[j] << (char)9 << (char)9 << (double)circle / N[j] * 4 ;
reportTime("", te - ts);
}
return 0;
}
</syntaxhighlight>
In this version, the value of PI is calculated using the Monte Carlo method.
This method states:
100000000 3.1419176 - took - 10035 millisecs
''The first column represents the "stride" or the number of digits of pi points we are calculating to.generating
With this method, we can see that the accuracy of the calculation is slightly off. This is due to the randomization of points within the circle. Running the program again will give us slightly different results.
'''Parallelizing
=== Assignment 2 ===
}
</syntaxhighlight>
<br>
[http://docs.nvidia.com/cuda/curand/index.html cuRAND] documentation.
<br>
However, the parallelized results seem to stay accurate throughout the iterations. <br>
It seems as though the calculation time doesn't change much and stays consistent. <br>
[[File:Cudamalloc.PNG|800px]] <br>[[File:Prof.PNG]] <br> Profiling the code shows that '''memcpycudaMalloc''' takes up most of the time spent. Even when <br>
there are 10 iterations, the time remains at 300 milliseconds. <br>
As the iteration passes 25 million, we have a bit of memory leak which results in inaccurate results. <br><br>
In order to optimize the code, we must find a way reduce the time memcpy cudaMalloc takes.<br>
=== Assignment 3 ===
----
After realizing the cudaMemcpy and cudaMalloc takes quite a bit of time, we focused our efforts on optimizing it.
It was difficult to find a solution because the initial copy takes a bit of time to set up.<br>
We tried using cudaMallocHost to see if we can allocate memory instead of using malloc. <br>
cudaMallocHost will allocate pinned memory which is stored in RAM and can be accessed by the GPU's DMA directly.
We changed one part of our code
<syntaxhighlight lang="cpp">
<br/>
The final results show that although cudaMallocHost should imporve the speed of memory transfer, if didn'''Test runs: t make much <br/>of a difference here. In conclusion, we can see that the GPU performance is significantly faster than the CPU'''Run 1:<br/>n = 10[[File:10-Kernal-Blas.png]]<br/><br/>n = 1000[[File:1000-Kernal-Blas.png]]<br/><br/>n = 10000[[File:10000-Kernal-Blas.png]]<br/><br/>n = 100000[[File:100000-Kernal-Blas.png]]<br/><br/>n = 1000000[[File:1000000-Kernal-Blass performance.png]]<br/>Here is where an error occurs and onward where we suspect that a memory leak causes the problem resulting in an error in pi calculation