Team failure

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Team Failure

Team Members

1. John Iannandrea, TBB Heat diffusion
2. Colin Campbell, OpenMP Heat diffusion
3. Mateya Lucic, Cilk Plus Heat diffusion

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Assignment

Our assignment was to implement OMP, TBB, and Cilk Plus versions of a 2d diffusion algorithm.

Serial

This is the serial version of the code we have parallelized

class SerialDiffuser : public IDiffuser {
protected:
	void evolveTimestep(){
		for (int row = 1; row < N - 1; row++) {
			for (int col = 1; col < N - 1; col++) {
				float uxx = (ui[(row + 1) * N + col] - (2 * ui[row * N + col]) + ui[(row - 1) * N + col]) / delta;
				float uyy = (ui[row * N + (col + 1)] - (2 * ui[row * N + col]) + ui[row * N + (col - 1)]) / delta;

				u[row * N + col] = ui[row * N + col] + deltaT * diff * (uxx + uyy);
			}
		}
	}
public:
	SerialDiffuser(int _N, int _T) : IDiffuser(_N, _T) {}
	void init(){
		for (int row = 0; row < N; row++) {
			for (int col = 0; col < N; col++) {
				if ((pow(row * dir - 0.5, 2) + pow(col * dir - 0.5, 2) <= 0.1)
					& (pow(row * dir - 0.5, 2) + pow(col * dir - 0.5, 2) >= 0.05))
					ui[row * N + col] = 1.0;
			}
		}
	}
	void compute(){
		for (int m = 1; m < timeSteps; m++) {
			evolveTimestep();
			std::copy(u, u + N * N, ui);
		}
	}
};

Omp

class OMPDiffuser : public IDiffuser {
protected:
	void evolveTimestep(){
		#pragma omp parallel for
		for (int row = 1; row < N - 1; row++) {
			for (int col = 1; col < N - 1; col++) {
				float uxx = (ui[(row + 1) * N + col] - (2 * ui[row * N + col]) + ui[(row - 1) * N + col]) / delta;
				float uyy = (ui[row * N + (col + 1)] - (2 * ui[row * N + col]) + ui[row * N + (col - 1)]) / delta;

				u[row * N + col] = ui[row * N + col] + deltaT * diff * (uxx + uyy);
			}
		}
	}
public:
	OMPDiffuser(int _N, int _T) : IDiffuser(_N, _T) {}
	void init(){
		#pragma omp parallel for
		for (int row = 0; row < N; row++) {
			for (int col = 0; col < N; col++) {
				if ((pow(row * dir - 0.5, 2) + pow(col * dir - 0.5, 2) <= 0.1)
					& (pow(row * dir - 0.5, 2) + pow(col * dir - 0.5, 2) >= 0.05))
					ui[row * N + col] = 1.0;
			}
		}
	}
	void compute(){
		for (int m = 1; m < timeSteps; m++) {
			evolveTimestep();
			std::copy(u, u + N * N, ui);
		}
	}
};

Cilk

class CilkDiffuser : public IDiffuser {
protected:
	void evolveTimestep(){
		cilk_for(int row = 1; row < N - 1; row++) {
			for (int col = 1; col < N - 1; col++) {
				float uxx = (ui[(row + 1) * N + col] - (2 * ui[row * N + col]) + ui[(row - 1) * N + col]) / delta;
				float uyy = (ui[row * N + (col + 1)] - (2 * ui[row * N + col]) + ui[row * N + (col - 1)]) / delta;

				u[row * N + col] = ui[row * N + col] + deltaT * diff * (uxx + uyy);
			}
		}
	}
public:
	CilkDiffuser(int _N, int _T) : IDiffuser(_N, _T) {}
	void init(){
		cilk_for(int row = 0; row < N; row++) {
			for (int col = 0; col < N; col++) {
				if ((pow(row * dir - 0.5, 2) + pow(col * dir - 0.5, 2) <= 0.1)
					& (pow(row * dir - 0.5, 2) + pow(col * dir - 0.5, 2) >= 0.05))
					ui[row * N + col] = 1.0;
			}
		}
	}
	void compute(){
		cilk_for(int m = 1; m < timeSteps; m++) {
			evolveTimestep();
			u[0:N*N] = ui[0:N*N];
		}
	}
};

TBB

class TBBEvolve {
	float* u;
	float* ui;
	float delta, deltaT;
	const float diff = 0.5;
	int N;
public:
	TBBEvolve(float* _u, float* _ui, float d, float dt, float n) : u(_u), ui(_ui), delta(d), deltaT(dt), N(n) {}
	void operator()(tbb::blocked_range2d<int> r) const{
		for (int row = r.rows().begin(); row < r.rows().end(); row++) {
			#pragma simd
			for (int col = r.cols().begin(); col < r.cols().end(); col++) {
				float uxx = (ui[(row + 1) * N + col] - (2 * ui[row * N + col]) + ui[(row - 1) * N + col]) / delta;
				float uyy = (ui[row * N + (col + 1)] - (2 * ui[row * N + col]) + ui[row * N + (col - 1)]) / delta;

				u[row * N + col] = ui[row * N + col] + deltaT * diff * (uxx + uyy);
			}
		}
	}
};

class TBBDiffuser : public IDiffuser {
protected:
	void evolveTimestep(){

	}
public:
	TBBDiffuser(int _N, int _T) : IDiffuser(_N, _T) {}
	void init(){
		for (int row = 0; row < N; row++) {
			for (int col = 0; col < N; col++) {
				if ((pow(row * dir - 0.5, 2) + pow(col * dir - 0.5, 2) <= 0.1)
					& (pow(row * dir - 0.5, 2) + pow(col * dir - 0.5, 2) >= 0.05))
					ui[row * N + col] = 1.0;
			}
		}
	}
	void compute(){
		for (int m = 1; m < timeSteps; m++) {
			tbb::blocked_range2d<int> r(1, N - 1, 1, N - 1);
			tbb::parallel_for(r, TBBEvolve(u, ui, delta, deltaT, N));
		}
	}
};

Results

What we found was that all the parallelization methods were all very similar. We also tested this with cuda and found cuda to be the fastest.