Update README.md
Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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David Rotermund
GitHub
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@ -69,7 +69,7 @@ Some example for array signatures are:
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|numba.types.float32[::1,:]| 2d array of float32 with Fortran memory layout|
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|numba.types.float32[::1,:,:]| 3d array of float32 with Fortran memory layout|
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## An example (up to 437x faster)
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## An example (up to 350x faster)
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For measuring the time used by the program I ran everything twice and took the second time. I did this is because the just-in-time compilation takes a moment for the first call of a function.
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@ -501,3 +501,299 @@ if __name__ == "__main__":
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print(f"{end_time-start_time:.5f} sec")
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print(results[0:10])
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```
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### Optimization 5 (0.235sec)
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We don't really need float64. Let's us switch to float32:
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```python
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import time
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import numpy as np
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from numba import njit
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import numba
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@njit(
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numba.types.uint64(numba.types.float32[:], numba.types.uint64, numba.types.float32),
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cache=True,
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)
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def get_spike(
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h: np.ndarray, number_of_neurons: np.uint64, random_number: np.float32
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) -> np.uint64:
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summation: np.float32 = np.float32(0.0)
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output: np.uint64 = np.uint64(number_of_neurons - 1)
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for i in range(0, np.uint64(number_of_neurons - 1)):
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summation += h[i]
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if random_number <= summation:
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output = np.uint64(i)
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return output
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return output
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@njit(
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numba.types.uint64[::1](
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numba.types.uint64,
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numba.types.uint64,
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numba.types.float32[::1],
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numba.types.float32[:, ::1],
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),
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cache=True,
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)
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def main(
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number_of_iterations: np.uint64,
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number_of_neurons: np.uint64,
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random_number_spikes: np.ndarray,
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random_number_h: np.ndarray,
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) -> np.ndarray:
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results = np.zeros((number_of_iterations), dtype=np.uint64)
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for i in range(0, number_of_iterations):
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h = random_number_h[i, :]
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h /= h.sum()
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results[i] = get_spike(h, number_of_neurons, random_number_spikes[i])
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return results
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if __name__ == "__main__":
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number_of_iterations: np.uint64 = np.uint64(10000)
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number_of_neurons: np.uint64 = np.uint64(10000)
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myrng = np.random.default_rng()
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random_number_spikes = myrng.random((number_of_iterations), dtype=np.float32)
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random_number_h = myrng.random(
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(number_of_iterations, number_of_neurons), dtype=np.float32
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)
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start_time = time.perf_counter()
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results = main(
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number_of_iterations=number_of_iterations,
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number_of_neurons=number_of_neurons,
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random_number_spikes=random_number_spikes,
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random_number_h=random_number_h,
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)
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end_time = time.perf_counter()
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check_for_errors = np.sum([results >= number_of_neurons])
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if check_for_errors > 0:
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print("Something went really wrong! Panic!")
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print(f"{end_time-start_time:.5f} sec")
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print(results[0:10])
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```
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### Optimization 6 (0.144sec)
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Let us activate [fastmath](https://numba.pydata.org/numba-doc/latest/reference/jit-compilation.html#jit-decorator-fastmath)
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```python
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@njit(
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numba.types.uint64(numba.types.float32[:], numba.types.uint64, numba.types.float32),
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cache=True,
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fastmath=True,
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)
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[...]
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@njit(
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numba.types.uint64[::1](
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numba.types.uint64,
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numba.types.uint64,
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numba.types.float32[::1],
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numba.types.float32[:, ::1],
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),
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cache=True,
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fastmath=True,
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)
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```
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```python
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import time
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import numpy as np
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from numba import njit
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import numba
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@njit(
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numba.types.uint64(numba.types.float32[:], numba.types.uint64, numba.types.float32),
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cache=True,
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fastmath=True,
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)
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def get_spike(
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h: np.ndarray, number_of_neurons: np.uint64, random_number: np.float32
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) -> np.uint64:
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summation: np.float32 = np.float32(0.0)
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output: np.uint64 = np.uint64(number_of_neurons - 1)
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for i in range(0, np.uint64(number_of_neurons - 1)):
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summation += h[i]
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if random_number <= summation:
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output = np.uint64(i)
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return output
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return output
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@njit(
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numba.types.uint64[::1](
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numba.types.uint64,
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numba.types.uint64,
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numba.types.float32[::1],
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numba.types.float32[:, ::1],
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),
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cache=True,
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fastmath=True,
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)
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def main(
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number_of_iterations: np.uint64,
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number_of_neurons: np.uint64,
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random_number_spikes: np.ndarray,
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random_number_h: np.ndarray,
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) -> np.ndarray:
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results = np.zeros((number_of_iterations), dtype=np.uint64)
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for i in range(0, number_of_iterations):
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h = random_number_h[i, :]
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h /= h.sum()
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results[i] = get_spike(h, number_of_neurons, random_number_spikes[i])
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return results
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if __name__ == "__main__":
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number_of_iterations: np.uint64 = np.uint64(10000)
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number_of_neurons: np.uint64 = np.uint64(10000)
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myrng = np.random.default_rng()
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random_number_spikes = myrng.random((number_of_iterations), dtype=np.float32)
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random_number_h = myrng.random(
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(number_of_iterations, number_of_neurons), dtype=np.float32
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)
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start_time = time.perf_counter()
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results = main(
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number_of_iterations=number_of_iterations,
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number_of_neurons=number_of_neurons,
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random_number_spikes=random_number_spikes,
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random_number_h=random_number_h,
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)
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end_time = time.perf_counter()
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check_for_errors = np.sum([results >= number_of_neurons])
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if check_for_errors > 0:
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print("Something went really wrong! Panic!")
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print(f"{end_time-start_time:.5f} sec")
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print(results[0:10])
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```
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### Optimization 7 (0.022sec)
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We can run the function main in [parallel](https://numba.pydata.org/numba-doc/latest/reference/jit-compilation.html#jit-decorator-parallel). This can be activated by:
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```python
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@njit(
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numba.types.uint64[::1](
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numba.types.uint64,
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numba.types.uint64,
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numba.types.float32[::1],
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numba.types.float32[:, ::1],
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),
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cache=True,
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fastmath=True,
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parallel=True,
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)
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```
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and then we need to replace **range** by **[prange](https://numba.pydata.org/numba-doc/latest/user/parallel.html#numba-parallel)**.
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```python
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import time
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import numpy as np
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from numba import njit, prange
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import numba
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@njit(
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numba.types.uint64(numba.types.float32[:], numba.types.uint64, numba.types.float32),
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cache=True,
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fastmath=True,
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)
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def get_spike(
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h: np.ndarray, number_of_neurons: np.uint64, random_number: np.float32
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) -> np.uint64:
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summation: np.float32 = np.float32(0.0)
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output: np.uint64 = np.uint64(number_of_neurons - 1)
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for i in range(0, np.uint64(number_of_neurons - 1)):
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summation += h[i]
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if random_number <= summation:
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output = np.uint64(i)
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return output
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return output
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@njit(
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numba.types.uint64[::1](
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numba.types.uint64,
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numba.types.uint64,
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numba.types.float32[::1],
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numba.types.float32[:, ::1],
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),
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cache=True,
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fastmath=True,
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parallel=True,
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)
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def main(
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number_of_iterations: np.uint64,
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number_of_neurons: np.uint64,
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random_number_spikes: np.ndarray,
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random_number_h: np.ndarray,
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) -> np.ndarray:
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results = np.zeros((number_of_iterations), dtype=np.uint64)
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for i in prange(0, number_of_iterations):
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h = random_number_h[i, :]
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h /= h.sum()
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results[i] = get_spike(h, number_of_neurons, random_number_spikes[i])
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return results
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if __name__ == "__main__":
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number_of_iterations: np.uint64 = np.uint64(10000)
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number_of_neurons: np.uint64 = np.uint64(10000)
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myrng = np.random.default_rng()
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random_number_spikes = myrng.random((number_of_iterations), dtype=np.float32)
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random_number_h = myrng.random(
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(number_of_iterations, number_of_neurons), dtype=np.float32
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)
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start_time = time.perf_counter()
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results = main(
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number_of_iterations=number_of_iterations,
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number_of_neurons=number_of_neurons,
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random_number_spikes=random_number_spikes,
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random_number_h=random_number_h,
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)
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end_time = time.perf_counter()
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check_for_errors = np.sum([results >= number_of_neurons])
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if check_for_errors > 0:
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print("Something went really wrong! Panic!")
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print(f"{end_time-start_time:.5f} sec")
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print(results[0:10])
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```
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## Failure is an option: Debugging
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