python-perfs: proofreading
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0EFC1AC1006886F8
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@ -1,9 +1,13 @@
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.cache/
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.hypothesis/
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*.so
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*.c
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*.html
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*.bin
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*.build/
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build/
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*.pyi
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examples/collatz_length_annotated.c
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examples/collatz_length_cython.c
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examples/collatz_length_cython_to_c.c
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examples/phi5cython.c
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*.lprof
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// Compile with:
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// cc -c -fPIC my_collatz_length.c -o libcollatz.so
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int collatz_length(long n)
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{
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if (n == 1)
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return 0;
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if (n % 2 == 0)
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return 1 + collatz_length(n / 2);
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else
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return 1 + collatz_length(n * 3 + 1);
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}
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from decimal import Decimal, localcontext
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from itertools import count
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from functools import cache
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@cache
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def fib(n):
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if n in (0, 1):
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return 1
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x = n // 2
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return fib(x - 1) * fib(n - x - 1) + fib(x) * fib(n - x)
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@cache
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def approx_phi(n):
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return Decimal(fib(n + 1)) / Decimal(fib(n))
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def approx_phi_up_to(n_digits):
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with localcontext() as ctx:
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ctx.prec = n_digits + 1
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for n in count():
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step1 = approx_phi(2 ** n)
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step2 = approx_phi(2 ** (n+1))
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if step1 == step2:
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return step1
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import sys
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if __name__ == "__main__":
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print(approx_phi_up_to(int(sys.argv[1])))
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from decimal import Decimal, localcontext
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from itertools import count
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from functools import cache
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@cache
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def fib(n: int) -> int:
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if n in (0, 1):
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return 1
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x = n // 2
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return fib(x - 1) * fib(n - x - 1) + fib(x) * fib(n - x)
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@cache
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def approx_phi(n: int) -> Decimal:
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return Decimal(fib(n + 1)) / Decimal(fib(n))
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def approx_phi_up_to(n_digits: int) -> Decimal:
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with localcontext() as ctx:
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ctx.prec = n_digits + 1
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for n in count():
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step1 = approx_phi(2 ** n)
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step2 = approx_phi(2 ** (n+1))
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if step1 == step2:
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return step1
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assert 0
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import sys
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if __name__ == "__main__":
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print(approx_phi_up_to(int(sys.argv[1])))
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from decimal import Decimal, localcontext
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from itertools import count
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from functools import cache
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from numba import jit
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@cache
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@jit
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def fib(n):
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if n in (0, 1):
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return 1
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x = n // 2
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return fib(x - 1) * fib(n - x - 1) + fib(x) * fib(n - x)
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@cache
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@jit
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def approx_phi(n):
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return Decimal(fib(n + 1)) / Decimal(fib(n))
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def approx_phi_up_to(n_digits):
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with localcontext() as ctx:
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ctx.prec = n_digits + 1
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for n in count():
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step1 = approx_phi(2 ** n)
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step2 = approx_phi(2 ** (n+1))
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if step1 == step2:
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return step1
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import sys
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if __name__ == "__main__":
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print(approx_phi_up_to(int(sys.argv[1])))
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#include <stdlib.h>
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#include <stdbool.h>
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#include <string.h>
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#include <inttypes.h>
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#include <stdio.h>
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#include <math.h>
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#include <png.h>
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void show_terrain(int width, int **terrain)
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{
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for (int x = 0; x < width; x++)
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printf("-");
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printf("\n");
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for (int x = 0; x < width; x++) {
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for (int y = 0; y < width; y++) {
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if (terrain[x][y] == 0)
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printf(" ");
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else if (terrain[x][y] == 1)
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printf("·");
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else if (terrain[x][y] == 2)
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printf("●");
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else if (terrain[x][y] == 3)
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printf("⬤");
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else
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printf("+");
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}
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printf("\n");
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}
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for (int x = 0; x < width; x++)
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printf("-");
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printf("\n");
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}
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int save_terrain(int width, int **terrain, char *filename)
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{
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png_image image;
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png_byte row_pointers[width][width];
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memset(&image, 0, (sizeof image));
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image.version = PNG_IMAGE_VERSION;
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image.format = PNG_FORMAT_FLAG_COLORMAP|PNG_FORMAT_FLAG_COLOR;
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image.width = width;
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image.height = width;
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image.colormap_entries = 4;
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for (int x = 0; x < width; x++)
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for (int y = 0; y < width; y++)
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row_pointers[x][y] = terrain[x][y];
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if (png_image_write_to_file(&image, filename, 0,
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row_pointers, 0,
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"\xFF\xFF\xFF\xe9\xff\x70\x70\xd6\xff\xff\x70\xa6")){
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return 0;
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}
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fprintf(stderr, "pngtopng: error: %s\n", image.message);
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return 1;
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}
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#ifdef DEBUG
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#define BOUND(a, low, high) (a < low ? low : (a > high ? high : a))
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#define STORE_HEIGHT(x) by_height[BOUND(x, 0, 19)] += 1
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#endif
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void apply_gravity(int width, int **terrain)
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{
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bool did_something;
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int div;
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#ifdef DEBUG
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int loops = 1;
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uint64_t by_height[20] = {0};
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#endif
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while (1) {
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did_something = false;
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#ifdef DEBUG
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loops += 1;
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#endif
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for (int x = 0; x < width; x++) {
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for (int y = 0; y < width; y++) {
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if (terrain[x][y] >= 4) {
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#ifdef DEBUG
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STORE_HEIGHT(terrain[x][y]);
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#endif
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did_something = true;
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div = terrain[x][y] / 4;
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terrain[x][y] = terrain[x][y] % 4;
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terrain[x - 1][y] += div;
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terrain[x + 1][y] += div;
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terrain[x][y + 1] += div;
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terrain[x][y - 1] += div;
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}
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}
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}
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if (!did_something) {
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#ifdef DEBUG
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fprintf(stderr, "looped %i times:\n", loops);
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for (int i = 0; i < 9; i++) {
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fprintf(stderr, "- %"PRIu64" times to break a pile of %i grains\n", by_height[i], i);
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}
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fprintf(stderr, "- %"PRIu64" times to break a pile of more than 13 grains\n", by_height[9]);
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#endif
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return;
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}
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}
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}
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int main(int argc, char **argv)
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{
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int width;
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int height;
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if (argc < 2) {
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fprintf(stderr, "Please provide height.\n");
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return EXIT_FAILURE;
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}
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if (sscanf(argv[1], "%i", &height) == EOF) {
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fprintf(stderr, "Can't read height as an integer.\n");
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return EXIT_FAILURE;
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}
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width = pow(height, 0.48);
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printf("Spawning a terrain of %ix%i\n", width, width);
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int **terrain = calloc(width, sizeof(int*));
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int *data = calloc(width * width, sizeof(int));
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for (int i = 0; i < width; i++) {
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terrain[i] = data + i * width;
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}
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terrain[width / 2][width / 2] = height;
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apply_gravity(width, terrain);
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if (argc > 2) {
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if (strcmp(argv[2], "/dev/null") != 0) {
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printf("Saving terrain to file...\n");
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if (save_terrain(width, terrain, argv[2]) == 0)
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printf("Terrain saved\n");
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else
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printf("Can't save terrain\n");
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}
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}
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else {
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show_terrain(width, terrain);
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}
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return EXIT_SUCCESS;
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}
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if not did_someting:
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return
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@numba.njit(numba.void(numba.int64[:,:]))
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def apply_gravity(terrain):
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"""
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$ python -m pyperf timeit --fast -s 'from examples.sandpile_numpy import main' 'main(10_000, False)'
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...........
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Mean +- std dev: 100 ms +- 5 ms
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"""
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shape = np.shape(terrain)
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while True:
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did_someting = False
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for x, y in np.ndindex(shape):
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if terrain[x, y] >= 4:
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terrain[x,y] -= 4
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terrain[x - 1][y] += 1
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terrain[x + 1][y] += 1
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terrain[x][y + 1] += 1
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terrain[x][y - 1] += 1
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did_someting = True
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# @numba.njit(numba.void(numba.int64[:,:]))
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# def apply_gravity(terrain):
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# """
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# $ python -m pyperf timeit --fast -s 'from examples.sandpile_numpy import main' 'main(10_000, False)'
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# ...........
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# Mean +- std dev: 100 ms +- 5 ms
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# """
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# shape = np.shape(terrain)
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# while True:
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# did_someting = False
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# for x, y in np.ndindex(shape):
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# if terrain[x, y] >= 4:
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# terrain[x,y] -= 4
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# terrain[x - 1][y] += 1
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# terrain[x + 1][y] += 1
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# terrain[x][y + 1] += 1
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# terrain[x][y - 1] += 1
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# did_someting = True
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#
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# if not did_someting:
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# return
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if not did_someting:
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return
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@numba.njit(numba.void(numba.int64[:,:]))
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def apply_gravity(terrain):
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"""Can handle 10k sand grains in 1.5s."""
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shape = np.shape(terrain)
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while True:
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did_someting = False
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for x, y in np.ndindex(shape):
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if terrain[x, y] >= 4000:
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div, terrain[x, y] = divmod(terrain[x][y], 4)
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terrain[x - 1][y] += div
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terrain[x + 1][y] += div
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terrain[x][y + 1] += div
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terrain[x][y - 1] += div
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did_someting = True
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elif terrain[x, y] >= 4:
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terrain[x,y] -= 4
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terrain[x - 1][y] += 1
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terrain[x + 1][y] += 1
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terrain[x][y + 1] += 1
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terrain[x][y - 1] += 1
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did_someting = True
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if not did_someting:
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return
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# @numba.njit(numba.void(numba.int64[:,:]))
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# def apply_gravity(terrain):
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# """Can handle 10k sand grains in 1.5s."""
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# shape = np.shape(terrain)
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# while True:
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# did_someting = False
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# for x, y in np.ndindex(shape):
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# if terrain[x, y] >= 4000:
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# div, terrain[x, y] = divmod(terrain[x][y], 4)
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# terrain[x - 1][y] += div
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# terrain[x + 1][y] += div
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# terrain[x][y + 1] += div
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# terrain[x][y - 1] += div
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# did_someting = True
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# elif terrain[x, y] >= 4:
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# terrain[x,y] -= 4
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# terrain[x - 1][y] += 1
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# terrain[x + 1][y] += 1
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# terrain[x][y + 1] += 1
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# terrain[x][y - 1] += 1
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# did_someting = True
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#
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# if not did_someting:
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# return
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def main(height, show=True):
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width = int(height**0.5) + 1
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terrain = np.zeros((width, width), dtype=np.int64)
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terrain[width // 2, width // 2] = height
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begin = perf_counter()
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apply_gravity(terrain)
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if show:
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@ -145,51 +145,40 @@ C'est l'heure du live coding !
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# À vous !
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Simulons un tas de sable.
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Simulez un tas de sable, moi je calcule le nombre l'or.
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Ne vous souciez pas des perfs, on s'en occupera.
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Vous avez 10mn.
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notes:
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Leur laisser ~15mn.
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voir sandpile.py
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# Les outils
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## Les outils en ligne de commande
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`time`, un outil POSIX, mais aussi une fonction native de bash :
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```shell
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#!cache time -p python examples/sandpile.py 16
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```
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Mais `time` ne teste qu'une fois.
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notes:
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real 0m0.719s # C'est le temps « sur le mur »
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user 0m0.521s # Temps CPU passé « dans Python »
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sys 0m0.195s # Temps CPU passé dans le kernel
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Je mesure mes perfs, puis ils mesurent leurs perfs.
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## Hyperfine
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`hyperfine` teste plusieurs fois :
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## `pyperf command`
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```shell
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#!cache hyperfine "python examples/sandpile.py 1000"
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```
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#!cache pyperf command python examples/phi1.py 3
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#!cache pyperf command python examples/phi1.py 6
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#!cache pyperf command python examples/phi1.py 9
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```
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## Petite parenthèse
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Mais attention, démarrer un processus Python n'est pas gratuit :
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```shell
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#!cache hyperfine "python -c pass"
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#!cache pyperf command python -c pass
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```
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notes:
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@ -202,13 +191,13 @@ N'essayez pas de retenir les chiffres, retenez les faits.
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Et puis il peut dépendre de la version de Python, des options de compilation, ... :
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```shell
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$ hyperfine "~/.local/bin/python3.10 -c pass" # Avec pydebug
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Benchmark 1: ~/.local/bin/python3.10 -c pass
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Time (mean ± σ): 37.6 ms ± 0.6 ms
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$ pyperf command ~/.local/bin/python3.10 -c pass
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.....................
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command: Mean +- std dev: 37.6 ms +- 0.6 ms
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$ hyperfine "/usr/bin/python3.10 -c pass" # Sans pydebug
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Benchmark 1: /usr/bin/python3.10 -c pass
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Time (mean ± σ): 19.1 ms ± 0.8 ms
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$ pyperf command /usr/bin/python3.10 -c pass
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.....................
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command: Mean +- std dev: 14.4 ms +- 0.4 ms
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```
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notes:
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@ -216,46 +205,14 @@ notes:
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Leur parler de `--enable-optimizations` (PGO).
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## timeit
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## `pyperf timeit`
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Timeit c'est dans la stdlib de Python, ça s'utilise en ligne de commande ou depuis Python.
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Il existe aussi `timeit` dans la stdlib, mais je préfère `pyperf timeit` :
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```shell
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#!cache python -m timeit -s 'import sandpile' 'sandpile.main(1000, show=False)'
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#!cache pyperf timeit --setup 'from examples.phi1 import approx_phi_up_to' 'approx_phi_up_to(3)'
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```
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Mais il ne vous donne que le meilleur temps, ce qui n'est pas toujours représentatif.
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## pyperf
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Il n'est pas dans la stdlib, mais il replace avantageusement `timeit` :
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```shell
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#!cache python -m pyperf timeit --setup 'import sandpile' 'sandpile.main(1000, show=False)'
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```
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notes:
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Avec hyperfine on teste combien de temps ça prend à Python **de
|
||||
démarrer** puis d'exécuter `pass`, ici on teste combien de temps ça
|
||||
prend d'exécuter `pass`.
|
||||
|
||||
|
||||
## pyperf
|
||||
|
||||
Digression, `pyperf` peut aussi remplacer `hyperfine` :
|
||||
|
||||
```shell
|
||||
#!cache python -m pyperf command python examples/sandpile.py 1000
|
||||
```
|
||||
|
||||
notes:
|
||||
|
||||
Avec hyperfine on teste combien de temps ça prend à Python **de
|
||||
démarrer** puis d'exécuter `pass`, ici on teste combien de temps ça
|
||||
prend d'exécuter `pass`.
|
||||
|
||||
|
||||
## Les outils — À vous !
|
||||
|
||||
|
|
@ -263,12 +220,14 @@ Effectuez quelques mesures sur votre implémentation.
|
|||
|
||||
Tentez d'en déterminer la complexité en fonction du nombre de grains.
|
||||
|
||||
Explorez les limites de vos implémentations.
|
||||
|
||||
|
||||
# Profilage
|
||||
|
||||
`time`, `timeit`, `hyperfine`, `pyperf` c'est bien pour mesurer, comparer.
|
||||
`pyperf` c'est bien pour mesurer, comparer.
|
||||
|
||||
`cProfile` peut nous aider à trouver la fonction coupable.
|
||||
Le profilage peut nous aider à trouver la fonction coupable.
|
||||
|
||||
|
||||
## cProfile, exemple
|
||||
|
|
@ -278,22 +237,6 @@ Tentez d'en déterminer la complexité en fonction du nombre de grains.
|
|||
```
|
||||
|
||||
|
||||
## cProfile, exemple
|
||||
|
||||
Testons :
|
||||
|
||||
```python
|
||||
#!sed -n '/import sys/,$p' examples/phi1.py
|
||||
```
|
||||
|
||||
```text
|
||||
#!cache time -p python examples/phi1.py 10
|
||||
```
|
||||
|
||||
|
||||
C'est déjà lent, et pour `20` c'est interminable...
|
||||
|
||||
|
||||
## cProfile, exemple
|
||||
|
||||
Sortons cProfile :
|
||||
|
|
@ -412,7 +355,7 @@ $ python -m snakeviz phi5.prof
|
|||
```
|
||||
|
||||
#!if [ ! -f .cache/phi5.prof ]; then python -m cProfile -o .cache/phi5.prof examples/phi5.py 2000 >/dev/null 2>&1; fi
|
||||
#!if [ ! -f output/phi5-snakeviz.png ]; then python -m snakeviz -s .cache/phi5.prof & TOKILL=$!; sleep 1; cutycapt --min-width=1024 --delay=500 --url=http://127.0.0.1:8080/snakeviz/%2Ftmp%2Fphi5.prof --out=output/phi5-snakeviz.png ; kill $TOKILL; fi
|
||||
#!if [ ! -f output/phi5-snakeviz.png ]; then python -m snakeviz -s .cache/phi5.prof & TOKILL=$!; sleep 1; cutycapt --min-width=1024 --delay=500 --url=file://$(pwd)/.cache/phi5.prof --out=output/phi5-snakeviz.png ; kill $TOKILL; fi
|
||||
|
||||
|
||||
## Snakeviz
|
||||
|
|
@ -436,15 +379,23 @@ $ scalene phi5.py 100000
|
|||
|
||||
|
||||
|
||||
## line_profiler
|
||||
|
||||
```shell
|
||||
$ python -m pip install line_profiler
|
||||
#!cache python -m kernprof --view --prof-mod examples/phi5.py --line-by-line examples/phi5.py 100000
|
||||
```
|
||||
|
||||
|
||||
## Aussi
|
||||
|
||||
- https://github.com/gaogaotiantian/viztracer
|
||||
- https://github.com/joerick/pyinstrument
|
||||
- https://github.com/benfred/py-spy
|
||||
- https://github.com/pyutils/line_profiler
|
||||
- https://github.com/sumerc/yappi
|
||||
- https://github.com/vmprof/vmprof-python
|
||||
- https://github.com/bloomberg/memray
|
||||
- https://github.com/pythonprofilers/memory_profiler
|
||||
|
||||
|
||||
## Profilage — À vous !
|
||||
|
|
@ -459,74 +410,38 @@ Cython est un dialecte de Python transpilable en C.
|
|||
|
||||
## Cython démo
|
||||
|
||||
Sans modifier le code :
|
||||
|
||||
```python
|
||||
#!cat examples/collatz_length.py
|
||||
$ pip install cython
|
||||
$ cythonize --inplace examples/phi5cython.py
|
||||
#!cythonize --inplace examples/phi5cython.py
|
||||
```
|
||||
|
||||
## Cython démo
|
||||
|
||||
```
|
||||
#!cache pyperf timeit --setup 'from examples.phi5 import approx_phi_up_to' 'approx_phi_up_to(100_000)'
|
||||
|
||||
#!cache pyperf timeit --setup 'from examples.phi5cython import approx_phi_up_to' 'approx_phi_up_to(100_000)'
|
||||
```
|
||||
|
||||
|
||||
## Cython démo
|
||||
|
||||
```shell
|
||||
$ python -m pyperf timeit \
|
||||
> -s 'from collatz_length import collatz_length'
|
||||
> 'collatz_length(837799)'
|
||||
#!cache python -m pyperf timeit --fast --setup 'from examples.collatz_length import collatz_length' 'collatz_length(837799)'
|
||||
```
|
||||
En annotant le fichier on permet à cython d'utiliser des types natifs.
|
||||
|
||||
```shell
|
||||
$ cythonize --inplace collatz_length_cython.py
|
||||
#!if ! [ -f examples/collatz_length_cython.*.so ] ; then cythonize --inplace examples/collatz_length_cython.py; fi
|
||||
```
|
||||
|
||||
```shell
|
||||
$ python -m pyperf timeit \
|
||||
> -s 'from collatz_length import collatz_length'
|
||||
> 'collatz_length(837799)'
|
||||
#!cache python -m pyperf timeit --fast -s 'from examples.collatz_length import collatz_length' 'collatz_length(837799)' # faster
|
||||
#!# Beware, the cythonized use `-s` while the non cythonized uses `--setup` just to have two cache buckets :D
|
||||
```
|
||||
Et ainsi réduire les aller-retour coûteux entre le C et Python.
|
||||
|
||||
|
||||
## Cython annotate
|
||||
|
||||
```shell
|
||||
$ cython -a collatz_length.py
|
||||
#!if ! [ -f examples/collatz_length.html ] ; then cython -a examples/collatz_length.py; fi
|
||||
#!if ! [ -f output/collatz_length.png ] ; then cutycapt --min-width=1024 --delay=500 --url=file://$(pwd)/examples/collatz_length.html --out=output/collatz_length.png; fi
|
||||
#!cache cython --annotate examples/phi5cython.py
|
||||
#!if ! [ -f output/phi5cython.png ] ; then cutycapt --min-width=1024 --delay=500 --url=file://$(pwd)/examples/phi5cython.html --out=output/phi5cython.png; fi
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
## Cython annotated
|
||||
|
||||
```python
|
||||
#!cat examples/collatz_length_annotated.py
|
||||
```
|
||||
|
||||
```shell
|
||||
$ cythonize --inplace collatz_length_annotated.py
|
||||
```
|
||||
|
||||
#!if ! [ -f examples/collatz_length_annotated.*.so ] ; then cythonize --inplace examples/collatz_length_annotated.py; fi
|
||||
|
||||
```shell
|
||||
$ python -m pyperf timeit \
|
||||
> -s 'from collatz_length_annotated import collatz_length'
|
||||
> 'collatz_length(837799)'
|
||||
#!cache python -m pyperf timeit --fast -s 'from examples.collatz_length_annotated import collatz_length' 'collatz_length(837799)'
|
||||
```
|
||||
|
||||
|
||||
## Cython annotate again
|
||||
|
||||
```shell
|
||||
$ cython -a examples/collatz_length_annotated.py
|
||||
#!if ! [ -f examples/collatz_length_annotated.html ] ; then cython -a examples/collatz_length_annotated.py; fi
|
||||
#!if ! [ -f output/collatz_length_annotated.png ] ; then cutycapt --min-width=1024 --delay=500 --url=file://$(pwd)/examples/collatz_length_annotated.html --out=output/collatz_length_annotated.png; fi
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Cython — À vous !
|
||||
|
|
@ -537,16 +452,16 @@ $ cython -a examples/collatz_length_annotated.py
|
|||
Numba est un `JIT` : « Just In Time compiler ».
|
||||
|
||||
```python
|
||||
#!
|
||||
#!cat examples/collatz_length_numba.py
|
||||
```
|
||||
|
||||
## Numba démo
|
||||
|
||||
```shell
|
||||
$ python -m pyperf timeit \
|
||||
> -s 'from examples.collatz_length_numba import collatz_length' \
|
||||
> 'collatz_length(837799)'
|
||||
#!cache python -m pyperf timeit --fast -s 'from examples.collatz_length_numba import collatz_length' 'collatz_length(837799)'
|
||||
#!cache pyperf timeit --setup 'from examples.collatz_length import collatz_length' 'collatz_length(837799)'
|
||||
|
||||
#!cache pyperf timeit --setup 'from examples.collatz_length_numba import collatz_length' 'collatz_length(837799)'
|
||||
```
|
||||
|
||||
## numba — À vous !
|
||||
|
|
@ -563,15 +478,18 @@ mypyc est un compilateur qui s'appuie sur les annotationes de type mypy :
|
|||
## mypyc demo
|
||||
|
||||
```shell
|
||||
$ mypyc examples/collatz_length_mypy.py
|
||||
#!if ! [ -f collatz_length_mypy.*.so ] ; then mypyc examples/collatz_length_mypy.py; fi
|
||||
$ pip install mypy
|
||||
#!cd examples; mypyc collatz_length_mypy.py
|
||||
$ mypyc collatz_length_mypy.py
|
||||
```
|
||||
|
||||
|
||||
## mypyc demo
|
||||
|
||||
```shell
|
||||
$ python -m pyperf timeit \
|
||||
> -s 'from collatz_length_mypy import collatz_length' \
|
||||
> 'collatz_length(837799)'
|
||||
#!cache python -m pyperf timeit --fast -s 'from collatz_length_mypy import collatz_length' 'collatz_length(837799)'
|
||||
#!cache pyperf timeit --setup 'from examples.collatz_length import collatz_length' 'collatz_length(837799)'
|
||||
|
||||
#!cache pyperf timeit --setup 'from examples.collatz_length_mypy import collatz_length' 'collatz_length(837799)' 2>/dev/null
|
||||
```
|
||||
|
||||
## mypyc — À vous !
|
||||
|
|
@ -588,17 +506,21 @@ pythran est un compilateur pour du code scientifique :
|
|||
## Pythran demo
|
||||
|
||||
```shell
|
||||
$ pip install pythran
|
||||
$ pythran examples/collatz_length_pythran.py
|
||||
#!if ! [ -f collatz_length_pythran.*.so ]; then pythran examples/collatz_length_pythran.py; fi
|
||||
#!if ! [ -f examples/collatz_length_pythran.*.so ]; then cd examples; pythran collatz_length_pythran.py; fi
|
||||
```
|
||||
|
||||
|
||||
## Pythran demo
|
||||
|
||||
```shell
|
||||
$ python -m pyperf timeit \
|
||||
> -s 'from collatz_length_pythran import collatz_length' \
|
||||
> 'collatz_length(837799)'
|
||||
#!cache python -m pyperf timeit --fast -s 'from collatz_length_pythran import collatz_length' 'collatz_length(837799)'
|
||||
#!cache pyperf timeit --setup 'from examples.collatz_length import collatz_length' 'collatz_length(837799)'
|
||||
|
||||
#!cache pyperf timeit --setup 'from examples.collatz_length_pythran import collatz_length' 'collatz_length(837799)'
|
||||
```
|
||||
|
||||
|
||||
## pythran — À vous !
|
||||
|
||||
|
||||
|
|
@ -607,15 +529,15 @@ $ python -m pyperf timeit \
|
|||
Aussi un compilateur, aussi utilisable pour distribuer une application.
|
||||
|
||||
```shell
|
||||
$ pip install nuitka
|
||||
$ python -m nuitka --module collatz_length_nuitka.py
|
||||
#!if ! [ -f collatz_length_nuitka.*.so ]; then python -m nuitka --module examples/collatz_length_nuitka.py >/dev/null; fi
|
||||
#!if ! [ -f examples/collatz_length_nuitka.*.so ]; then (cd examples/; python -m nuitka --module collatz_length_nuitka.py >/dev/null); fi
|
||||
```
|
||||
|
||||
```shell
|
||||
$ python -m pyperf timeit \
|
||||
> -s 'from collatz_length_nuitka import collatz_length' \
|
||||
> 'collatz_length(837799)'
|
||||
#!cache python -m pyperf timeit --fast -s 'from collatz_length_nuitka import collatz_length' 'collatz_length(837799)'
|
||||
#!cache pyperf timeit --setup 'from examples.collatz_length import collatz_length' 'collatz_length(837799)'
|
||||
|
||||
#!cache --force -- pyperf timeit --setup 'from examples.collatz_length_nuitka import collatz_length' 'collatz_length(837799)'
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -627,14 +549,13 @@ https://github.com/pfalcon/awesome-python-compilers
|
|||
# Hand crafted C
|
||||
|
||||
```c
|
||||
#!sed -n '/int collatz_length/,/^$/p' examples/my_collatz_length.c
|
||||
#!sed -n '/int collatz_length/,$p' examples/my_collatz_length.c
|
||||
```
|
||||
|
||||
Mais comment l'utiliser ?
|
||||
|
||||
## Hand crafted C
|
||||
|
||||
### Avec Cython
|
||||
## Avec Cython
|
||||
|
||||
```cpython
|
||||
#!cat examples/collatz_length_cython_to_c.pyx
|
||||
|
|
@ -645,9 +566,15 @@ $ cythonize -i examples/collatz_length_cython_to_c.pyx
|
|||
#!if ! [ -f examples/collatz_length_cython_to_c.*.so ] ; then cythonize -i examples/collatz_length_cython_to_c.pyx; fi
|
||||
```
|
||||
|
||||
## Avec Cython
|
||||
|
||||
```shell
|
||||
$ python -m pyperf timeit \
|
||||
> -s 'from collatz_length_cython_to_c import collatz_length' \
|
||||
> 'collatz_length(837799)'
|
||||
#!cache python -m pyperf timeit --fast -s 'from examples.collatz_length_cython_to_c import collatz_length' 'collatz_length(837799)'
|
||||
#!cache pyperf timeit --setup 'from examples.collatz_length import collatz_length' 'collatz_length(837799)'
|
||||
|
||||
#!cache pyperf timeit --setup 'from examples.collatz_length_cython_to_c import collatz_length' 'collatz_length(837799)'
|
||||
```
|
||||
|
||||
|
||||
## TODO
|
||||
|
||||
https://docs.python.org/3.12/howto/perf_profiling.html#perf-profiling
|
||||
|
|
|
|||
|
|
@ -6,3 +6,4 @@ numba
|
|||
mypy
|
||||
pythran
|
||||
nuitka
|
||||
line_profiler
|
||||
|
|
|
|||
Loading…
Reference in New Issue