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title: 
style: nestedList # TOC style (nestedList|nestedOrderedList|inlineFirstLevel)
minLevel: 0 # Include headings from the specified level
maxLevel: 0 # Include headings up to the specified level
includeLinks: true # Make headings clickable
debugInConsole: false # Print debug info in Obsidian console

CPU

  • Physical cores: 20
  • Physical + Logical cores: 40 
    Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU(s):                          40
On-line CPU(s) list:             0-39
Thread(s) per core:              2
Core(s) per socket:              10
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           85
Model name:                      Intel(R) Xeon(R) Silver 4210R CPU @ 2.40GHz
Stepping:                        7
CPU MHz:                         2400.000
CPU max MHz:                     3200.0000
CPU min MHz:                     1000.0000

Bilinear

Bilinear Resize Benchmark Results

Method Resize (640x640x3 to 256x256x3) Speedup over Python Speedup over OpenCV Resize (2160x3840x3 to 480x854) Speedup over Python Speedup over OpenCV
Python 1.7087 s N/A N/A 10.4762 s N/A N/A
OpenCV 0.0003 s 5425.3690 N/A 0.0005 s 21883.2490 N/A
Mojo (For Loops) 0.0077 s 221.8717 0.0410 0.0492 s 217.8379 0.0097
Mojo (SIMD) 0.0010 s 1755.0607 0.323 0.0061 s 1763.9810 0.0789
Mojo (SIMD+ Parallel) 0.0001 s 13858.1098 2.5543 0.0006 s 18995.7696 0.8492

Python

Bilinear resize from (640, 640, 3) to (256, 256):
Error rate: 0.4068756103515625
Opencv: 0.0003149523865431547 s
Python: 1.7087329153902828 s
cv2 resize is 5425.369003057714 times faster than python

Bilinear resize from (2160, 3840, 3) to (480, 854):
Error rate: 0.37517483086130626
Opencv: 0.00047873249957337973 s
Python: 10.476222490519286 s
cv2 resize is 21883.248995744227 times faster than python

Mojo

Error

Mojo numpy Error checking:

Mojo Org Tensor shape:  640x640x3
Mojo Result Tensor shape:  256x256x3
Numpy Org Tensor shape:  (640, 640, 3)
Numpy Result Tensor shape:  (256, 256, 3)
Average error:  0.4065704345703125

Mojo Org Tensor shape:  2160x3840x3
Mojo Result Tensor shape:  480x854x3
Numpy Org Tensor shape:  (2160, 3840, 3)
Numpy Result Tensor shape:  (480, 854, 3
Average error:  0.3744877049180328

Conclusion: Algorithm implementation looks goods, error rates similar to python

Latency

using the best latencies from python:

  • for 640x640x3 -> 256x256x3: 
        Opencv: 0.0003149523865431547 s
    Python: 1.7087329153902828 s
  • for 2160x3840x3 -> 480x854x3: 
        Opencv: 0.00047873249957337973 s
    Python: 10.476222490519286 s
1. Pure For loops:
  • 640x640x3 -> 256x256x3: ```     ———————     Benchmark Report (s)     ———————     Mean: 0.0077014453141175223     Total: 145.21845284299999     Iters: 18856     Warmup Mean: 0.0112494864     Warmup Total: 0.056247432     Warmup Iters: 5     Fastest Mean: 0.0077014453141175223     Slowest Mean: 0.0077014453141175223

    resize:  640x640x3 -> 256x256x3 :  0.0077014453141175223 s     speedup over python:  221.87171961839474     speedup over opencv:  0.040895231180284479     speedup in opencv:  24.452728866883096     worst speedup in opencv:  24.452728866883096

    
- `2160x3840x3 -> 480x854x3`:

---------------------
Benchmark Report (s)
---------------------
Mean: 0.049159172993067587
Total: 113.459371268
Iters: 2308
Warmup Mean: 0.062143362000000001
Warmup Total: 0.31071681000000001
Warmup Iters: 5
Fastest Mean: 0.049159172993067594
Slowest Mean: 0.049159172993067594

resize:  2160x3840x3 -> 480x854x3 :  0.049159172993067587 s
speedup over python:  217.83793874849002
speedup over opencv:  0.0097384164628011635
speedup in opencv:  102.68609930780876
worst speedup in opencv:  102.68609930780877 ```
2. SIMD Vectorization
  • 640x640x3 -> 256x256x3: 
      Mojo Org Tensor shape:  640x640x3
  Mojo Result Tensor shape:  256x256x3
  Numpy Org Tensor shape:  (640, 640, 3)
  Numpy Result Tensor shape:  (256, 256, 3)
  Average error:  0.906005859375
  ---------------------
  Benchmark Report (s)
  ---------------------
  Mean: 0.00097360333796067309
  Total: 120.06963165400001
  Iters: 123325
  Warmup Mean: 0.0011944061999999999
  Warmup Total: 0.005972031
  Warmup Iters: 5
  Fastest Mean: 0.00097360333796067298
  Slowest Mean: 0.00097360333796067298
	
  resize:  640x640x3 -> 256x256x3 :  0.00097360333796067309 s
  speedup over python:  1755.0606584499035
  speedup over opencv:  0.32349148186247961
  speedup in opencv:  3.0912715050256341
  worst speedup in opencv:  3.0912715050256336
  • 2160x3840x3 -> 480x854x3 
      Mojo Org Tensor shape:  2160x3840x3
  Mojo Result Tensor shape:  480x854x3
  Numpy Org Tensor shape:  (2160, 3840, 3)
  Numpy Result Tensor shape:  (480, 854, 3)
  Average error:  0.3744877049180328
  ---------------------
  Benchmark Report (s)
  ---------------------
  Mean: 0.0060707756540178568
  Total: 5.4394149860000001
  Iters: 896
  Warmup Mean: 0.0075168404000000005
  Warmup Total: 0.037584201999999997
  Warmup Iters: 5
  Fastest Mean: 0.0060707756540178577
  Slowest Mean: 0.0060707756540178577
	
  resize:  2160x3840x3 -> 480x854x3 :  0.0060707756540178568 s
  speedup over python:  1763.9810010608544
  speedup over opencv:  0.078858539148376761
  speedup in opencv:  12.680934884153052
  worst speedup in opencv:  12.680934884153054
3. SIMD Vectorization + Parallelization
  • 640x640x3 -> 256x256x3: 
      Mojo Org Tensor shape:  640x640x3
  Mojo Result Tensor shape:  256x256x3
  Numpy Org Tensor shape:  (640, 640, 3)
  Numpy Result Tensor shape:  (256, 256, 3)
  Average error:  0.906005859375
  ---------------------
  Benchmark Report (s)
  ---------------------
  Mean: 0.00012330201849217637
  Total: 5.6342857349999997
  Iters: 45695
  Warmup Mean: 0.00029481879999999997
  Warmup Total: 0.001474094
  Warmup Iters: 5
  Fastest Mean: 0.0001233020184921764
  Slowest Mean: 0.0001233020184921764
	
  resize:  640x640x3 -> 256x256x3 :  0.00012330201849217637 s
  speedup over python:  13858.10983701539
  speedup over opencv:  2.5543165504880907
  speedup in opencv:  0.39149415518171204
  worst speedup in opencv:  0.39149415518171216
  • 2160x3840x3 -> 480x854x3 
      Mojo Org Tensor shape:  2160x3840x3
  Mojo Result Tensor shape:  480x854x3
  Numpy Org Tensor shape:  (2160, 3840, 3)
  Numpy Result Tensor shape:  (480, 854, 3)
  Average error:  0.5209252211813687
  ---------------------
  Benchmark Report (s)
  ---------------------
  Mean: 0.00056374303959517047
  Total: 6.3500015980000004
  Iters: 11264
  Warmup Mean: 0.0017351432
  Warmup Total: 0.008675716
  Warmup Iters: 5
  Fastest Mean: 0.00056374303959517036
  Slowest Mean: 0.00056374303959517036
	
  resize:  2160x3840x3 -> 480x854x3 :  0.00056374303959517047 s
  speedup over python:  18995.769638380512
  speedup over opencv:  0.84920338868780065
  speedup in opencv:  1.1775741987384343
  worst speedup in opencv:  1.1775741987384341

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