Work in progress
pip install rem-math
Sum of two 32-bit integer array
from rem_math import rem_math as rm
import numpy as np
array = [i for i in range(100_000_000)]
np_array = np.array([i for i in range(100_000_000)], dtype=np.int32)
sum_two_i32_result = rm.sum_two_ints32(array, array, simd=True)
sum_of_array = rm.sum_arr_int32(array)
sum_of_np_array = rm.sum_nparr_int32(np_array)
print(sum_two_i32_result)--------------------------------------- benchmark 'arr_i32': 1 tests ---------------------------------------
Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
------------------------------------------------------------------------------------------------------------
test_sum_arr_i32 4.2611 4.4810 4.3450 0.0814 4.3310 0.0625 2;1 230.1495 5 100
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---------------------------------------- benchmark 'arr_i32_simd': 1 tests -----------------------------------------
Name (time in us) Min Max Mean StdDev Median IQR Outliers OPS (Kops/s) Rounds Iterations
--------------------------------------------------------------------------------------------------------------------
test_sum_arr_i32_simd 1.1802 1.2003 1.1903 0.0071 1.1903 0.0051 2;0 840.1472 5 100000
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---------------------------------------- benchmark 'numpy': 1 tests ----------------------------------------
Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
------------------------------------------------------------------------------------------------------------
test_numpy_sum 7.6939 7.8477 7.7710 0.0544 7.7710 0.0387 2;0 128.6840 5 13
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Legend:
Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
OPS: Operations Per Second, computed as 1 / Mean
```
-------------------------------------- benchmark 'numpy_arr_sum': 1 tests --------------------------------------
Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
----------------------------------------------------------------------------------------------------------------
test_numpy_arr_sum 70.3064 78.1331 75.1148 4.1352 78.1298 7.3991 2;0 13.3129 5 2
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-------------------------------------- benchmark 'sum_floatsf32': 1 tests --------------------------------------
Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
----------------------------------------------------------------------------------------------------------------
test_sum_floatsf32 54.6912 54.8023 54.7604 0.0562 54.8004 0.0981 1;0 18.2614 5 10
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-------------------------------------- benchmark 'sum_floatsf32_simd': 1 tests --------------------------------------
Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
---------------------------------------------------------------------------------------------------------------------
test_sum_floatsf32_simd 52.0826 52.5085 52.1729 0.1877 52.0914 0.1119 1;1 19.1670 5 3
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--------------------------------------- benchmark 'sum_ints32': 1 tests ---------------------------------------
Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
---------------------------------------------------------------------------------------------------------------
test_sum_ints32 32.8143 40.4775 36.1822 3.0797 34.9533 4.6386 2;0 27.6379 5 10
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-------------------------------------- benchmark 'sum_ints32_simd': 1 tests --------------------------------------
Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
------------------------------------------------------------------------------------------------------------------
test_sum_ints32_simd 32.8149 34.9717 33.8741 0.9905 34.3772 1.7000 3;0 29.5211 5 10
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```
-------------------------------------- benchmark 'numpy_sum': 1 tests --------------------------------------
Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
------------------------------------------------------------------------------------------------------------
test_numpy_sum 7.8130 8.9292 8.4827 0.6113 8.9290 1.1160 2;0 117.8870 5 14
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--------------------------------------- benchmark 'rm_sum': 1 tests ----------------------------------------
Name (time in ms) Min Max Mean StdDev Median IQR Outliers OPS Rounds Iterations
------------------------------------------------------------------------------------------------------------
test_rm_sum 1.7189 1.8860 1.7523 0.0747 1.7189 0.0419 1;1 570.6719 5 100
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Array accumulation time: [15.509 µs 15.532 µs 15.575 µs]
Found 11 outliers among 100 measurements (11.00%)
1 (1.00%) low mild
3 (3.00%) high mild
7 (7.00%) high severe
Array accumulation with SIMD instructions
time: [77.083 ns 77.499 ns 78.264 ns]
Found 9 outliers among 100 measurements (9.00%)
2 (2.00%) high mild
7 (7.00%) high severe
Array accumulation time: [4.9527 ms 4.9757 ms 5.0013 ms]
Found 11 outliers among 100 measurements (11.00%)
1 (1.00%) high mild
10 (10.00%) high severe
Array accumulation with SIMD instructions
time: [4.8752 ms 4.8976 ms 4.9250 ms]
Found 8 outliers among 100 measurements (8.00%)
2 (2.00%) high mild
6 (6.00%) high severe
Benchmarking Array accumulation of two float arrays: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.6s, or reduce sample count to 80.
Array accumulation of two float arrays
time: [53.260 ms 53.448 ms 53.653 ms]
Found 13 outliers among 100 measurements (13.00%)
9 (9.00%) high mild
4 (4.00%) high severe
Benchmarking Array accumulation of two float arrays with SIMD: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.8s, or reduce sample count to 80.
Array accumulation of two float arrays with SIMD
time: [55.241 ms 55.470 ms 55.718 ms]
Found 10 outliers among 100 measurements (10.00%)
8 (8.00%) high mild
2 (2.00%) high severe
Benchmarking Array accumulation of two integer arrays: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.6s, or reduce sample count to 80.
Array accumulation of two integer arrays
time: [53.543 ms 53.783 ms 54.043 ms]
Found 10 outliers among 100 measurements (10.00%)
7 (7.00%) high mild
3 (3.00%) high severe
Benchmarking Array accumulation of two integer arrays with SIMD: Warming up for 3.0000 s
Warning: Unable to complete 100 samples in 5.0s. You may wish to increase target time to 5.8s, or reduce sample count to 80.
Array accumulation of two integer arrays with SIMD
time: [55.783 ms 56.053 ms 56.360 ms]
Found 15 outliers among 100 measurements (15.00%)
7 (7.00%) high mild
8 (8.00%) high severe
- Add GPU-accelerated operations for improved performance.
- Implement own custom type objects for best performance from ecosystem.
- Expand mathematical functionality with additional features and algorithms.
Stay tuned for updates as the library evolves!