-`resolution`: number of grid points along each dimension of the unit square; the gridspacing is $h = 1.0/(\text{resolution}-1)$
-`resolution`: number of grid points along each dimension of the unit square; the gridspacing is $`h = 1.0/(\text{resolution}-1)`$
-`iterations`: number of Jacobi iterations to perform
Further and more specifically, your program should
- use $\bar{u}_h=\mathbf{0}$ as initial approximation to $u$, and (after finishing all iterations)
- print the Euclidean $\parallel \cdot \parallel_2$ and Maximum $\parallel \cdot \parallel_{\infty}$ norm of the residual $\parallel A_h\bar{u}_h-b_h \parallel$ and of the total error $\parallel \bar{u}_h-u_p \parallel$ to the console,
- use $`\bar{u}_h=\mathbf{0}`$ as initial approximation to $`u`$, and (after finishing all iterations)
- print the Euclidean $`\parallel \cdot \parallel_2`$ and Maximum $`\parallel \cdot \parallel_{\infty}`$ norm of the residual $`\parallel A_h\bar{u}_h-b_h \parallel`$ and of the total error $`\parallel \bar{u}_h-u_p \parallel`$ to the console,
- print the average runtime per iteration to the console, and
- produce the same results as a serial run.
Finally, benchmark the parallel performance of your program `jacobiMPI` using 2 nodes of the IUE-Cluster for 4 different `resolution`s=$\{125,250,1000,4000\}$ using between 1 and 80 MPI-processes (`NUMMPIPROC`).
Finally, benchmark the parallel performance of your program `jacobiMPI` using 2 nodes of the IUE-Cluster for 4 different `resolution`s=$`\{125,250,1000,4000\}`$ using between 1 and 80 MPI-processes (`NUMMPIPROC`).
More specifically, you should
- create a plot of the parallel speedup and a plot of the parallel efficiency for each `resolution`, and
