diff options
Diffstat (limited to 'buch/papers/multiplikation/code/MM.py')
| -rw-r--r-- | buch/papers/multiplikation/code/MM.py | 46 |
1 files changed, 25 insertions, 21 deletions
diff --git a/buch/papers/multiplikation/code/MM.py b/buch/papers/multiplikation/code/MM.py index 47bd6ab..7220ae1 100644 --- a/buch/papers/multiplikation/code/MM.py +++ b/buch/papers/multiplikation/code/MM.py @@ -226,28 +226,28 @@ def plot_c_res(ave, num): MM_t = MM[:,0] MM_n = MM[:,1] - MM_t = np.mean(MM_t.reshape(-1,ave),axis=1) - MM_n = np.mean(MM_n.reshape(-1,ave),axis=1) + # MM_t = np.mean(MM_t.reshape(-1,ave),axis=1) + # MM_n = np.mean(MM_n.reshape(-1,ave),axis=1) MM_dc_t = MM_dc[:,0] MM_dc_n = MM_dc[:,1] - MM_dc_t = np.mean(MM_dc_t.reshape(-1,ave),axis=1) - MM_dc_n = np.mean(MM_dc_n.reshape(-1,ave),axis=1) + # MM_dc_t = np.mean(MM_dc_t.reshape(-1,ave),axis=1) + # MM_dc_n = np.mean(MM_dc_n.reshape(-1,ave),axis=1) strassen_t = strassen[:,0] strassen_n = strassen[:,1] - strassen_t = np.mean(strassen_t.reshape(-1,ave),axis=1) - strassen_n = np.mean(strassen_n.reshape(-1,ave),axis=1) + # strassen_t = np.mean(strassen_t.reshape(-1,ave),axis=1) + # strassen_n = np.mean(strassen_n.reshape(-1,ave),axis=1) winograd_t = winograd[:,0] winograd_n = winograd[:,1] - winograd_t = np.mean(winograd_t.reshape(-1,ave),axis=1) - winograd_n = np.mean(winograd_n.reshape(-1,ave),axis=1) + # winograd_t = np.mean(winograd_t.reshape(-1,ave),axis=1) + # winograd_n = np.mean(winograd_n.reshape(-1,ave),axis=1) blas_t = blas[:,0] blas_n = blas[:,1] - blas_t = np.mean(blas_t.reshape(-1,ave),axis=1) - blas_n = np.mean(blas_n.reshape(-1,ave),axis=1) + # blas_t = np.mean(blas_t.reshape(-1,ave),axis=1) + # blas_n = np.mean(blas_n.reshape(-1,ave),axis=1) def func(x, a,b): return b*x**a @@ -261,14 +261,16 @@ def plot_c_res(ave, num): plt.rc('axes', labelsize=23) plt.rc('xtick', labelsize=23) plt.rc('ytick', labelsize=23) - plt.plot(MM_n, MM_t, label='3 For Loops', lw=5) - plt.plot(winograd_n, winograd_t, label='Winograd MM', lw=5) - plt.plot(blas_n, blas_t, label='Blas', lw=5) - plt.plot(strassen_n, strassen_t, label='Strassen', lw=5) - plt.plot(MM_dc_n, MM_dc_t, label='Divide and Conquer', lw=5) + plt.loglog(MM_n, MM_t, label='3 For Loops', lw=5) + plt.loglog(winograd_n, winograd_t, label='Winograd MM', lw=5) + plt.loglog(blas_n, blas_t, label='Blas', lw=5) + plt.loglog(strassen_n, strassen_t, label='Strassen', lw=5) + plt.loglog(MM_dc_n, MM_dc_t, label='Divide and Conquer', lw=5) plt.xlabel("n") + # plt.yscale('log', base=10) + # plt.xscale('log', base=2) plt.ylabel("time (s)") - plt.grid(True) + plt.grid(True, which="both", ls="-") plt.tight_layout() plt.legend(fontsize=19) plt.savefig('c_meas_' + str(num)+ '.pdf') @@ -278,15 +280,17 @@ def plot_c_res(ave, num): # plt.plot(blas_n, func(blas_n, *popt2), 'r-', label='fit MM: a=%5.5f, b=%5.10f' % tuple(popt2)) plt.legend() - + # return [MM_n,winograd_n,blas_n,strassen_n,MM_dc_n] + return [MM_t,winograd_t,blas_t,strassen_t,MM_dc_t] + # test%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% if __name__ == '__main__': - # plot_c_res(1, 4096) + # A = plot_c_res(1, 4096) - # arr = plot(1024) - n = np.logspace(1,12,12,base=2,dtype=(np.int)) + arr = plot(1024) + # n = np.logspace(1,12,12,base=2,dtype=(np.int)) # n = np.arange(1,50,2) # A = np.random.randint(-10, 6, (5,3)) # B = np.random.randint(-10, 6, (3,5)) @@ -297,7 +301,7 @@ if __name__ == '__main__': # print(C_test) # print(np.equal(C, C_test)) - t_np = test_perfomance(n) + # t_np = test_perfomance(n) # C = strassen(A, B) # C_test = A@B |