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-rwxr-xr-xbuch/papers/multiplikation/code/MMbin26848 -> 26848 bytes
-rwxr-xr-xbuch/papers/multiplikation/code/MM.c2
-rw-r--r--buch/papers/multiplikation/code/MM.py109
3 files changed, 61 insertions, 50 deletions
diff --git a/buch/papers/multiplikation/code/MM b/buch/papers/multiplikation/code/MM
index f07985f..d52dda4 100755
--- a/buch/papers/multiplikation/code/MM
+++ b/buch/papers/multiplikation/code/MM
Binary files differ
diff --git a/buch/papers/multiplikation/code/MM.c b/buch/papers/multiplikation/code/MM.c
index 04c4dab..a897d4f 100755
--- a/buch/papers/multiplikation/code/MM.c
+++ b/buch/papers/multiplikation/code/MM.c
@@ -31,7 +31,7 @@ int main() {
run_algo(strassen, "strassen",0);
run_algo(MM, "MM", 0);
- // run_algo(winograd, "winograd", 0);
+ run_algo(winograd, "winograd", 0);
run_algo_cblas(0);
return 0;
diff --git a/buch/papers/multiplikation/code/MM.py b/buch/papers/multiplikation/code/MM.py
index 626b82d..7220ae1 100644
--- a/buch/papers/multiplikation/code/MM.py
+++ b/buch/papers/multiplikation/code/MM.py
@@ -132,6 +132,10 @@ def winograd2(A, B):
return C
def test_perfomance(n):
+
+ import mkl
+ mkl.set_num_threads(1)
+
t_mm = []
t_mm_dc = []
t_mm_strassen = []
@@ -144,21 +148,21 @@ def test_perfomance(n):
# A = np.random.randint(-100, 100,(i, i))
# B = np.random.randint(-100, 100,(i, i))
- start = time.time()
- C3 = strassen(A, B)
- t_mm_strassen.append(time.time() - start)
+ # start = time.time()
+ # C3 = strassen(A, B)
+ # t_mm_strassen.append(time.time() - start)
- start = time.time()
- C1 = MM(A, B)
- t_mm.append(time.time() - start)
+ # start = time.time()
+ # C1 = MM(A, B)
+ # t_mm.append(time.time() - start)
- start = time.time()
- C2 = MM_dc(A, B)
- t_mm_dc.append(time.time() - start)
+ # start = time.time()
+ # C2 = MM_dc(A, B)
+ # t_mm_dc.append(time.time() - start)
- start = time.time()
- C4 = winograd2(A, B)
- t_wino.append(time.time() - start)
+ # start = time.time()
+ # C4 = winograd2(A, B)
+ # t_wino.append(time.time() - start)
start = time.time()
C = A@B
@@ -169,22 +173,23 @@ def test_perfomance(n):
plt.rc('axes', labelsize=23)
plt.rc('xtick', labelsize=23)
plt.rc('ytick', labelsize=23)
- plt.plot(n, t_mm, label='Standard', lw=5)
- plt.plot(n, t_mm_dc, label='Divide and conquer', lw=5)
- plt.plot(n, t_mm_strassen, label='Strassen', lw=5)
- plt.plot(n, t_wino, label='Winograd', lw=5)
+ # plt.plot(n, t_mm, label='Standard', lw=5)
+ # plt.plot(n, t_mm_dc, label='Divide and conquer', lw=5)
+ # plt.plot(n, t_mm_strassen, label='Strassen', lw=5)
+ # plt.plot(n, t_wino, label='Winograd', lw=5)
plt.plot(n, t_np, label='NumPy A@B', lw=5)
+ # plt.xscale('log', base=2)
plt.legend()
plt.xlabel("n")
plt.ylabel("time (s)")
- plt.grid(True)
+ plt.grid(True, which="both", ls="-")
plt.tight_layout()
# plt.yscale('log')
plt.legend(fontsize=19)
- plt.savefig('meas_' + str(max(n))+ '.pdf')
- arr = np.array([n, t_mm, t_mm_dc, t_mm_strassen, t_wino, t_np])
- np.savetxt('meas_' + str(max(n))+ '.txt',arr)
- return arr
+ # plt.savefig('meas_' + str(max(n))+ '.pdf')
+ # arr = np.array([n, t_mm, t_mm_dc, t_mm_strassen, t_wino, t_np])
+ # np.savetxt('meas_' + str(max(n))+ '.txt',arr)
+ return t_np
def plot(num):
@@ -198,10 +203,11 @@ def plot(num):
plt.plot(n, t_mm, label='3 For Loops', lw=5)
plt.plot(n, t_mm_dc, label='Divide and Conquer', lw=5)
plt.plot(n, t_mm_strassen, label='Strassen', lw=5)
- # plt.plot(n, t_wino, label='Winograd', lw=5)
+ plt.plot(n, t_wino, label='Winograd', lw=5)
plt.plot(n, t_np, label='NumPy A@B', lw=5)
plt.legend()
plt.xlabel("n")
+ # plt.yscale('log', base=10)
plt.ylabel("time (s)")
plt.grid(True)
plt.tight_layout()
@@ -211,36 +217,37 @@ def plot(num):
return arr
def plot_c_res(ave, num):
+
MM = np.loadtxt("meas/MM.txt", delimiter=',')
- # winograd = np.loadtxt("meas/winograd.txt", delimiter=',')
+ winograd = np.loadtxt("meas/winograd.txt", delimiter=',')
blas = np.loadtxt("meas/blas.txt", delimiter=',')
MM_dc = np.loadtxt("meas/MM_dc.txt", delimiter=',')
strassen = np.loadtxt("meas/strassen.txt", delimiter=',')
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 = 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)
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
@@ -254,14 +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')
@@ -271,23 +280,25 @@ 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)
- # plot(8)
- # n = np.logspace(1,10,10,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, 10, (5,3))
- B = np.random.randint(-10, 10, (3,5))
+ # A = np.random.randint(-10, 6, (5,3))
+ # B = np.random.randint(-10, 6, (3,5))
- C = winograd2(A, B)
- C_test = A@B
- print(C)
- print(C_test)
+ # C = winograd2(A, B)
+ # C_test = A@B
+ # print(C)
+ # print(C_test)
# print(np.equal(C, C_test))
# t_np = test_perfomance(n)
generated by cgit v1.2.1 (git 2.18.0) at 2024-11-23 11:46:37 +0100