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Author: lixilinx

misc/benchmark_mm_trtrs_inv.py

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+"""Benchmarking matrix multiplication, back substitution and inverse
+"""
+import torch
+import time
+import random
+import statistics
+
+n = 1024
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+results = []#check whether NaN shows up
+times_mm, times_trtrs, times_inv = [], [], []
+with torch.no_grad():
+    while min([len(times_mm), len(times_trtrs), len(times_inv)])<3:       
+        A = torch.randn(n, n, device=device) + 10*torch.eye(n, device=device)
+        b = torch.randn(n, n, device=device)        
+        j = random.randint(0, 2)#call mm, trtrs, inverse in random order
+        if j==0:            
+            t0 = time.time()
+            x = A.mm(b)
+            results.append(x[0,0])
+            times_mm.append(time.time() - t0)
+        elif j==1:                 
+            t0 = time.time()
+            x = torch.trtrs(b, A)[0]#just take triangular part of A
+            results.append(x[0,0])
+            times_trtrs.append(time.time() - t0)
+        else:
+            t0 = time.time()
+            x = torch.inverse(A)
+            results.append(x[0,0])
+            times_inv.append(time.time() - t0)
+    
+print('Median Time in ms:')        
+print('Multiplication {}; BackSubstitution {}; Inversion {}'.format(
+        1000*statistics.median(times_mm), 
+        1000*statistics.median(times_trtrs), 
+        1000*statistics.median(times_inv)))