8000 ENH: Use AVX for float32 implementation of np.sin & np.cos by r-devulap · Pull Request #13368 · numpy/numpy · GitHub
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ENH: Use AVX for float32 implementation of np.sin & np.cos #13368

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Merged
merged 11 commits into from
Aug 18, 2019
Merged

ENH: Use AVX for float32 implementation of np.sin & np.cos #13368

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bd2c82b
ENH: Use AVX for float32 implementation of np.sin & np.cos
Mar 20, 2019
e50e725
BUG: fixing NAN handling and adding tests for sin/cos
May 1, 2019
d56fbfd
BUG: sin and cos cast float16 to float32
May 7, 2019
bca9628
TEST: adding tests to validate AVX sin/cos implementation
May 20, 2019
9cae09c
BUG: use strides and process strided arrays using AVX
May 22, 2019
cd9f1a8
BUG: AVX2 impl of sin/cos requires an FMA
May 22, 2019
8589d48
TEST: Rounding max tolerable ulp error to an int
May 23, 2019
0f78154
BUG: eliminate unsed variables warning in cpuid
May 23, 2019
44d14ab
MAINT: using an enum to switch between sin/cos
May 29, 2019
6f34f5f
TEST: improving test coverage for sin/cos for input > 117435.992f
May 30, 2019
457b3e6
BUG: rename avx2_scalef_ps to fma_scalef_ps
Aug 3, 2019
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TEST: improving test coverage for sin/cos for input > 117435.992f
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Raghuveer Devulapalli committed Aug 3, 2019
commit 6f34f5faa0c4e50e822c6aee01e074785fc86f33
7 changes: 6 additions & 1 deletion numpy/core/tests/test_umath.py
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Expand Up @@ -710,7 +710,12 @@ def test_log_float32(self):

def test_sincos_float32(self):
np.random.seed(42)
x_f32 = np.float32(np.random.uniform(low=-100.,high=100.,size=1000000))
N = 1000000
M = np.int(N/20)
index = np.random.randint(low=0, high=N, size=M)
x_f32 = np.float32(np.random.uniform(low=-100.,high=100.,size=N))
# test coverage for elements > 117435.992f for which glibc is used
x_f32[index] = np.float32(10E+10*np.random.rand(M))
x_f64 = np.float64(x_f32)
assert_array_max_ulp(np.sin(x_f32), np.float32(np.sin(x_f64)), maxulp=2)
assert_array_max_ulp(np.cos(x_f32), np.float32(np.cos(x_f64)), maxulp=2)
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