Monte Carlo Methods Area ratios can be inefficient Consider alternative for integrals sum=0 for(i = 0; i < N; i++) xr = random value between x1 and x2 sum += f(xr) area = sum/N Parallel Monte Carlo Very simple Static Print out the answer Everybody computes N values & a sum Ga...
One opposite is a non-parallelizable problem, that is, a problem for which no speedup may be achieved by utilizing more than one processor. Several suggestions were already posted, but I'd propose yet another name: a perfectly sequential problem. Examples: I/O-bound problems, "calculate f1000...
Computing an Integral Use random values of x to compute f(x) and sum values of f(x): where xi are randomly generated values of x between x1 and x2. Monte Carlo method is very useful if the function cannot be integrated numerically (maybe having a large number of variables) Example: Se...
Training Gaussian process-based models typically involves an O(N3) computational bottleneck due to inverting the covariance matrix. Popular methods for overcoming this matrix inversion problem cannot adequately model all types of latent functions, and are often not parallelizable. However, judicious choice...
Sequential routine computing value of one point returning number of iterations structure complex { float real; float imag; }; int cal_pixel(complex c) { int count, max; complex z; float temp, lengthsq; max = 256; z.real = 0; z.imag = 0; count = 0; /* number of iterations */ ...