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Remove fast-math flag #17

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Remove fast-math flag #17

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5cf8abc
Remove fast-math flag
EmilyBourne Jan 7, 2023
34077d3
Trigger Benchmark of pyccel
EmilyBourne Jan 7, 2023
e784117
Update performance comparison
github-actions[bot] Jan 7, 2023
d6be3c0
Update README and version
github-actions[bot] Jan 7, 2023
04fb9e7
Remove test results
EmilyBourne Jan 12, 2023
4a227a0
Merge branch 'main' into no_fast_math
yguclu Jan 17, 2023
e7b568b
Merge branch 'main' into no_fast_math
yguclu Jan 31, 2023
6b61376
Remove useless whitespace change
yguclu Jan 31, 2023
1bb6e4e
Remove useless whitespace change
yguclu Jan 31, 2023
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4 changes: 2 additions & 2 deletions .github/actions/generate_pyccel_config/action.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -13,11 +13,11 @@ runs:
run: |
import json
config = json.load(open('pyccel_fortran.json'))
config['general_flags'].extend(['-O3', '-march=native', '-mtune=native', '-mavx', '-ffast-math'])
config['general_flags'].extend(['-O3', '-march=native', '-mtune=native', '-mavx'])
print(json.dumps(config, indent=4),
file=open('pyccel_fortran.json','w'))
config = json.load(open('pyccel_c.json'))
config['general_flags'].extend(['-O3', '-march=native', '-mtune=native', '-mavx', '-ffast-math'])
config['general_flags'].extend(['-O3', '-march=native', '-mtune=native', '-mavx'])
print(json.dumps(config, indent=4),
file=open('pyccel_c.json','w'))
shell: python
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2 changes: 1 addition & 1 deletion benchmarks/pythran.config
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Expand Up @@ -4,7 +4,7 @@ undefs=
include_dirs=
libs=
library_dirs=
cflags=-std=c++11 -fno-math-errno -fvisibility=hidden -fno-wrapv -Wno-unused-function -Wno-int-in-bool-context -Wno-unknown-warning-option -O3 -march=native -mtune=native -mavx -ffast-math
cflags=-std=c++11 -fno-math-errno -fvisibility=hidden -fno-wrapv -Wno-unused-function -Wno-int-in-bool-context -Wno-unknown-warning-option -O3 -march=native -mtune=native -mavx
ldflags=-fvisibility=hidden -Wl,-strip-all
blas=blas
CC=
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2 changes: 1 addition & 1 deletion benchmarks/tests/numba_ackermann_mod.py
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Expand Up @@ -7,7 +7,7 @@
"""
from numba import njit

@njit(fastmath=True)
@njit
def ackermann(m : int, n : int) -> int:
""" Total computable function that is not primitive recursive.
This function is useful for testing recursion
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5 changes: 2 additions & 3 deletions benchmarks/tests/numba_bellman_ford_mod.py
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Expand Up @@ -10,7 +10,7 @@
import numpy as np


@njit(fastmath=True)
@njit
def bellman_ford ( v_num: int, e_num: int, source: int, e: 'int[:,:]', e_weight: 'real[:]',
v_weight: 'real[:]', predecessor: 'int[:]' ):
""" Calculate the shortest paths from a source vertex to all other
Expand Down Expand Up @@ -49,8 +49,7 @@ def bellman_ford ( v_num: int, e_num: int, source: int, e: 'int[:,:]', e_weight:
return 0


@njit(fastmath=True)

@njit
def bellman_ford_test():
""" Test bellman ford's algorithm
"""
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10 changes: 5 additions & 5 deletions benchmarks/tests/numba_dijkstra.py
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Expand Up @@ -9,7 +9,7 @@
import numpy as np

# ================================================================
@njit(fastmath=True)
@njit
def find_nearest ( nv: int, mind: 'int[:]', connected: 'bool[:]' ):
""" Find the nearest node
"""
Expand All @@ -26,7 +26,7 @@ def find_nearest ( nv: int, mind: 'int[:]', connected: 'bool[:]' ):
return d, v

# ================================================================
@njit(fastmath=True)
@njit
def update_mind ( nv: int, mv: int, connected: 'bool[:]', ohd: 'int[:,:]', mind: 'int[:]' ):
""" Update the minimum distance
"""
Expand All @@ -39,7 +39,7 @@ def update_mind ( nv: int, mv: int, connected: 'bool[:]', ohd: 'int[:,:]', mind:
mind[i] = min ( mind[i], mind[mv] + ohd[mv,i] )

# ================================================================
@njit(fastmath=True)
@njit
def dijkstra_distance ( nv: int, ohd: 'int[:,:]', mind: 'int[:]' ):
""" Find the shortest paths between nodes in a graph
"""
Expand Down Expand Up @@ -76,7 +76,7 @@ def dijkstra_distance ( nv: int, ohd: 'int[:,:]', mind: 'int[:]' ):
update_mind ( nv, mv, connected, ohd, mind )

# ================================================================
@njit(fastmath=True)
@njit
def init ( nv: int, ohd: 'int[:,:]' ):
""" Create a graph
"""
Expand All @@ -92,7 +92,7 @@ def init ( nv: int, ohd: 'int[:,:]' ):
ohd[0,333] = 33

# ================================================================
@njit(fastmath=True)
@njit
def dijkstra_distance_test ( ):
""" Test Dijkstra's algorithm
"""
Expand Down
8 changes: 4 additions & 4 deletions benchmarks/tests/numba_euler_mod.py
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Expand Up @@ -11,7 +11,7 @@
import numpy as np

# ================================================================
@njit(fastmath=True)
@njit
def euler(dydt: '()(real, const real[:], real[:])',
tspan: 'real[:]', y0: 'real[:]', n: int,
t: 'real[:]', y: 'real[:,:]'):
Expand All @@ -29,7 +29,7 @@ def euler(dydt: '()(real, const real[:], real[:])',
y[i+1,:] = y[i,:] + dt * y[i+1,:]

# ================================================================
@njit(fastmath=True)
@njit
def humps_fun(x: float):
"""
Humps function
Expand All @@ -42,7 +42,7 @@ def humps_fun(x: float):
return y

# ================================================================
@njit(fastmath=True)
@njit
def humps_deriv(x: 'real', y: 'real[:]', out: 'real[:]'):
"""
Derivative of the humps function
Expand All @@ -51,7 +51,7 @@ def humps_deriv(x: 'real', y: 'real[:]', out: 'real[:]'):
out[0] = - 2.0 * ( x - 0.3 ) / ( ( x - 0.3 )**2 + 0.01 )**2 - 2.0 * ( x - 0.9 ) / ( ( x - 0.9 )**2 + 0.04 )**2

# ================================================================
@njit(fastmath=True)
@njit
def euler_humps_test(t0: float, t1: float, n: int):
"""
Compute an approximate solution y_h(t) ~= y(t) of the initial
Expand Down
2 changes: 1 addition & 1 deletion benchmarks/tests/numba_laplace_2d_mod.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
from numba import njit
import numpy as np

@njit(fastmath=True)
@njit
def laplace_2d(nx: int, ny: int, rtol: float, maxiter: int):
"""
Solve the 2D Laplace equation for phi(x, y) on the rectangular
Expand Down
2 changes: 1 addition & 1 deletion benchmarks/tests/numba_linearconv_1d_mod.py
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Expand Up @@ -11,7 +11,7 @@
from numba import njit
import numpy as np

@njit(fastmath=True)
@njit
def linearconv_1d(nx: int, dt: float, nt: int):
"""
Compute an approximation of the solution u(t, x) to the 1D
Expand Down
12 changes: 6 additions & 6 deletions benchmarks/tests/numba_md_mod.py
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Expand Up @@ -14,7 +14,7 @@
from numpy import sin

# ================================================================
@njit(fastmath=True)
@njit
def compute_kinetic_energy(vel: 'double[:,:]', mass: float):
""" Compute the kinetic energy associated with the current configuration.
"""
Expand All @@ -28,7 +28,7 @@ def compute_kinetic_energy(vel: 'double[:,:]', mass: float):
return 0.5 * mass * kinetic

# ================================================================
@njit(fastmath=True)
@njit
def compute(mass: float, pos: 'double[:,:]', vel: 'double[:,:]',
force: 'double[:,:]'):
"""
Expand Down Expand Up @@ -71,7 +71,7 @@ def compute(mass: float, pos: 'double[:,:]', vel: 'double[:,:]',
return potential

# ================================================================
@njit(fastmath=True)
@njit
def update(dt: float, mass: float, force: 'double[:,:]',
pos: 'double[:,:]', vel: 'double[:,:]', acc: 'double[:,:]'):

Expand All @@ -93,7 +93,7 @@ def update(dt: float, mass: float, force: 'double[:,:]',
acc[:] = force * rmass

# ================================================================
@njit(fastmath=True)
@njit
def r8mat_uniform_ab(r: 'double[:, :]', a: float, b: float, seed: int):
""" Fill r with random numbers with a uniform distribution
"""
Expand Down Expand Up @@ -121,7 +121,7 @@ def r8mat_uniform_ab(r: 'double[:, :]', a: float, b: float, seed: int):
return seed

# ================================================================
@njit(fastmath=True)
@njit
def initialize(pos: 'double[:,:]'):
""" Initialise the positions of the particles
"""
Expand All @@ -130,7 +130,7 @@ def initialize(pos: 'double[:,:]'):
seed = r8mat_uniform_ab(pos, 0.0, 10.0, seed)

# ================================================================
@njit(fastmath=True)
@njit
def md(d_num: int, p_num: int, step_num: int, dt: float):
"""
Run a molecular dynamics simulation. This consists of an N-body
Expand Down
8 changes: 4 additions & 4 deletions benchmarks/tests/numba_midpoint_explicit_mod.py
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Expand Up @@ -11,7 +11,7 @@
import numpy as np

# ================================================================
@njit(fastmath=True)
@njit
def midpoint_explicit(dydt: '()(real, const real[:], real[:])',
tspan: 'real[:]', y0: 'real[:]', n: int,
t: 'real[:]', y: 'real[:,:]'):
Expand All @@ -38,7 +38,7 @@ def midpoint_explicit(dydt: '()(real, const real[:], real[:])',
y[i+1,:] = y[i,:] + dt * y[i+1,:]

# ================================================================
@njit(fastmath=True)
@njit
def humps_fun(x: float):
"""
Humps function
Expand All @@ -51,7 +51,7 @@ def humps_fun(x: float):
return y

# ================================================================
@njit(fastmath=True)
@njit
def humps_deriv(x: 'real', y: 'real[:]', out: 'real[:]'):
"""
Derivative of the humps function
Expand All @@ -60,7 +60,7 @@ def humps_deriv(x: 'real', y: 'real[:]', out: 'real[:]'):
out[0] = - 2.0 * ( x - 0.3 ) / ( ( x - 0.3 )**2 + 0.01 )**2 - 2.0 * ( x - 0.9 ) / ( ( x - 0.9 )**2 + 0.04 )**2

# ================================================================
@njit(fastmath=True)
@njit
def midpoint_explicit_humps_test(t0: float, t1: float, n: int):
"""
Compute an approximate solution y_h(t) ~= y(t) of the initial
Expand Down
8 changes: 4 additions & 4 deletions benchmarks/tests/numba_midpoint_fixed_mod.py
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Expand Up @@ -11,7 +11,7 @@
import numpy as np

# ================================================================
@njit(fastmath=True)
@njit
def midpoint_fixed(dydt: '()(real, const real[:], real[:])',
tspan: 'real[:]', y0: 'real[:]', n: int,
t: 'real[:]', y: 'real[:,:]'):
Expand Down Expand Up @@ -45,7 +45,7 @@ def midpoint_fixed(dydt: '()(real, const real[:], real[:])',
+ (1.0 - 1.0 / theta) * y[i,:]

# ================================================================
@njit(fastmath=True)
@njit
def humps_fun(x: float):
"""
Humps function
Expand All @@ -58,7 +58,7 @@ def humps_fun(x: float):
return y

# ================================================================
@njit(fastmath=True)
@njit
def humps_deriv(x: 'real', y: 'real[:]', out: 'real[:]'):
"""
Derivative of the humps function
Expand All @@ -67,7 +67,7 @@ def humps_deriv(x: 'real', y: 'real[:]', out: 'real[:]'):
out[0] = - 2.0 * ( x - 0.3 ) / ( ( x - 0.3 )**2 + 0.01 )**2 - 2.0 * ( x - 0.9 ) / ( ( x - 0.9 )**2 + 0.04 )**2

# ================================================================
@njit(fastmath=True)
@njit
def midpoint_fixed_humps_test(t0: float, t1: float, n: int):
"""
Compute an approximate solution y_h(t) ~= y(t) of the initial
Expand Down
2 changes: 1 addition & 1 deletion benchmarks/tests/numba_nonlinearconv_1d_mod.py
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Expand Up @@ -12,7 +12,7 @@
import numpy as np


@njit(fastmath=True)
@njit
def nonlinearconv_1d(nx: int, dt: float, nt: int):
"""
Compute an approximation of the solution u(t, x) to the 1D
Expand Down
2 changes: 1 addition & 1 deletion benchmarks/tests/numba_poisson_2d_mod.py
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Expand Up @@ -12,7 +12,7 @@
import numpy as np


@njit(fastmath=True)
@njit
def poisson_2d(nx: int, ny: int, nt: int):
"""
Solve the 2D poisson equation for phi(x, y) on the rectangular
Expand Down
8 changes: 4 additions & 4 deletions benchmarks/tests/numba_rk4_mod.py
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Expand Up @@ -11,7 +11,7 @@
import numpy as np

# ================================================================
@njit(fastmath=True)
@njit
def rk4(dydt: '()(real, const real[:], real[:])',
tspan: 'real[:]', y0: 'real[:]', n: int,
t: 'real[:]', y: 'real[:,:]'):
Expand Down Expand Up @@ -43,7 +43,7 @@ def rk4(dydt: '()(real, const real[:], real[:])',
y[i+1,:] = y[i,:] + dt * (f1[:] + 2.0 * f2[:] + 2.0 * f3[:] + f4[:]) / 6.0

# ================================================================
@njit(fastmath=True)
@njit
def humps_fun(x: float):
"""
Humps function
Expand All @@ -56,7 +56,7 @@ def humps_fun(x: float):
return y

# ================================================================
@njit(fastmath=True)
@njit
def humps_deriv(x: 'real', y: 'real[:]', out: 'real[:]'):
"""
Derivative of the humps function
Expand All @@ -65,7 +65,7 @@ def humps_deriv(x: 'real', y: 'real[:]', out: 'real[:]'):
out[0] = - 2.0 * ( x - 0.3 ) / ( ( x - 0.3 )**2 + 0.01 )**2 - 2.0 * ( x - 0.9 ) / ( ( x - 0.9 )**2 + 0.04 )**2

# ================================================================
@njit(fastmath=True)
@njit
def rk4_humps_test(t0: float, t1: float, n: int):
"""
Compute an approximate solution y_h(t) ~= y(t) of the initial
Expand Down