randompast · October 19, 2020 05:08 · Oct 19, 2020
diff --git a/test_cusignal_fork.py b/test_cusignal_fork.py
@@ -0,0 +1,174 @@
+import time
+import cusignal as cs
+import numpy as np
+import cupy as cp
+from numba import cuda, njit, jit
+
+@njit
+def nbconv_1d3o(x,k): #numba_conv 1 dimension 3rd order
+    y = np.zeros(x.size-k.shape[0]+1)
+    for n in range(0, y.size):
+        d = n+k.shape[0]-1
+        for i in range(k.shape[0]):
+            for j in range(k.shape[1]):
+                for l in range(k.shape[2]):
+                # print(n, x[d-i] * x[d-j] * k[i,j], x[d-i],  x[d-j], k[i,j])
+                    y[n] += x[d-i] * x[d-j] * x[d-l] * k[i,j,l]
+    return y
+
+@njit
+def nbconv_1d2o(x,k): #numba_conv 1 dimension 2nd order
+    y = np.zeros(x.size-k.shape[0]+1)
+    for n in range(0, y.size):
+        d = n+k.shape[0]-1
+        for i in range(k.shape[0]):
+            for j in range(k.shape[1]):
+                # print(n, x[d-i] * x[d-j] * k[i,j], x[d-i],  x[d-j], k[i,j])
+                y[n] += x[d-i] * x[d-j] * k[i,j]
+    return y
+
+@njit
+def nbconv_1d1o(x,k): #numba_conv 1 dimension 1st order
+    y = np.zeros(x.size-k.shape[0]+1)
+    for n in range(0, y.size):
+        d = n+k.shape[0]-1
+        for i in range(k.shape[0]):
+            y[n] += x[d-i] * k[i]
+    return y
+
+@cuda.jit
+def nbconv_1d3o_device(x,k,y): #numba_conv
+    n = cuda.grid(1)
+    if (0 <= n) and (n < y.size):
+        d = n+k.shape[0]-1
+        for i in range(k.shape[0]):
+            for j in range(k.shape[1]):
+                for l in range(k.shape[2]):
+                    y[n] += x[d-i] * x[d-j] * x[d-l] * k[i,j,l]
+
+@cuda.jit
+def nbconv_1d2o_device(x,k,y): #numba_conv
+    n = cuda.grid(1)
+    if (0 <= n) and (n < y.size):
+        d = n+k.shape[0]-1
+        for i in range(k.shape[0]):
+            for j in range(k.shape[1]):
+                y[n] += x[d-i] * x[d-j] * k[i,j]
+
+@cuda.jit
+def nbconv_1d1o_device(x,k,y): #numba_conv
+    n = cuda.grid(1)
+    if (0 <= n) and (n < y.size):
+        d = n+k.shape[0]-1
+        for i in range(k.shape[0]):
+            y[n] += x[d-i] * k[i]
+
+def test_nbc_f(f, x, k):
+    l = x.size - k.shape[0] + 1
+    y = cp.zeros(l)
+    th = 128
+    b = y.size//th+1
+    f[b,th](x, k, y)
+    return y
+
+def test_1d_1o():
+    a = np.arange(5)
+    b = np.arange(2)+1
+
+    c = cs.convolve(a,b, "valid")
+    print("cs-1o", c.size, c)
+
+    c = np.convolve(a,b, "valid")
+    print("np-1o", c.size, c)
+
+    c = nbconv_1d1o(a,b)
+    print("nj-1o", c.size, c.astype(dtype=int))
+
+    c = test_nbc_f(nbconv_1d1o_device, a, b)
+    print("cj-1o", c.size, c.astype(dtype=int))
+
+
+def test_1d_2o():
+    a = np.arange(5)
+    b = np.arange(4).reshape(2,2)+1
+
+    c = cs.convolve1d2o(a,b) #valid
+    print("cs-2o", c.size, c)
+
+    c = nbconv_1d2o(a,b)
+    print("nj-2o", c.size, c.astype(dtype=int))
+
+    c = test_nbc_f(nbconv_1d2o_device, a, b)
+    print("cj-2o", c.size, c.astype(dtype=int))
+
+
+def test_1d_3o():
+    a = np.arange(5)
+    b = np.arange(8).reshape(2,2,2)+1
+
+    c = cs.convolve1d3o(a,b) #valid
+    print("cs-3o", c.size, c)
+
+    c = nbconv_1d3o(a,b)
+    print("nj-3o", c.size, c.astype(dtype=int))
+
+    c = test_nbc_f(nbconv_1d3o_device, a, b)
+    print("cj-3o", c.size, c.astype(dtype=int))
+
+def test_1d_1o2o3o():
+    test_1d_1o()
+    test_1d_2o()
+    test_1d_3o()
+
+def time_n(n,f,a,b):
+    start = time.time()
+    for i in range(n):
+        c = f(a,b)
+    elapsed = time.time() - start
+    return elapsed
+
+def time_n_cuda(n,f,a,b):
+    start = time.time()
+    for i in range(n):
+        c = test_nbc_f(f,a,b)
+    elapsed = time.time() - start
+    return elapsed
+
+def benchmark_1d2o(m,n,d):
+    a = np.random.uniform(-1,1,(n))
+    b = np.random.uniform(-1,1,(d,d))
+
+    t = time_n(m,cs.convolve1d2o,a,b)
+    print("cs-1d2o", t)
+
+    t = time_n(m,nbconv_1d2o,a,b)
+    print("nj-1d2o", t)
+
+    t = time_n_cuda(m,nbconv_1d2o_device,a,b)
+    print("cj-1d2o", t)
+
+
+def benchmark_1d3o(m,n,d):
+    a = np.random.uniform(-1,1,(n))
+    b = np.random.uniform(-1,1,(d,d,d))
+
+    t = time_n(m,cs.convolve1d3o,a,b)
+    print("cs-1d3o", t)
+
+    t = time_n(m,nbconv_1d3o,a,b)
+    print("nj-1d3o", t)
+
+    t = time_n_cuda(m,nbconv_1d3o_device,a,b)
+    print("cj-1d3o", t)
+
+if __name__ == "__main__":
+    m, n, d = 50, 200, 50
+    print("first run")
+    benchmark_1d2o(m,n,d)
+    benchmark_1d3o(m,n,d)
+
+    print("second run")
+    benchmark_1d2o(m,n,d)
+    benchmark_1d3o(m,n,d)
+
+    test_1d_1o2o3o()
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