diff options
| -rw-r--r-- | benchmarks/benchmarks/bench_linalg.py | 71 |
1 files changed, 59 insertions, 12 deletions
diff --git a/benchmarks/benchmarks/bench_linalg.py b/benchmarks/benchmarks/bench_linalg.py index dc2849d58..a72cccb5f 100644 --- a/benchmarks/benchmarks/bench_linalg.py +++ b/benchmarks/benchmarks/bench_linalg.py @@ -108,26 +108,73 @@ class Lstsq(Benchmark): class Einsum(Benchmark): param_names = ['dtype'] - params = [[np.float64]] + params = [[np.float32, np.float64]] def setup(self, dtype): - self.a = np.arange(2900, dtype=dtype) - self.b = np.arange(3000, dtype=dtype) - self.c = np.arange(24000, dtype=dtype).reshape(20, 30, 40) - self.c1 = np.arange(1200, dtype=dtype).reshape(30, 40) - self.d = np.arange(10000, dtype=dtype).reshape(10,100,10) - - #outer(a,b): trigger sum_of_products_contig_stride0_outcontig_two + self.one_dim_small = np.arange(600, dtype=dtype) + self.one_dim = np.arange(3000, dtype=dtype) + self.one_dim_big = np.arange(480000, dtype=dtype) + self.two_dim_small = np.arange(1200, dtype=dtype).reshape(30, 40) + self.two_dim = np.arange(240000, dtype=dtype).reshape(400, 600) + self.three_dim_small = np.arange(10000, dtype=dtype).reshape(10,100,10) + self.three_dim = np.arange(24000, dtype=dtype).reshape(20, 30, 40) + # non_contigous arrays + self.non_contigous_dim1_small = np.arange(1, 80, 2, dtype=dtype) + self.non_contigous_dim1 = np.arange(1, 4000, 2, dtype=dtype) + self.non_contigous_dim2 = np.arange(1, 2400, 2, dtype=dtype).reshape(30, 40) + self.non_contigous_dim3 = np.arange(1, 48000, 2, dtype=dtype).reshape(20, 30, 40) + + # outer(a,b): trigger sum_of_products_contig_stride0_outcontig_two def time_einsum_outer(self, dtype): - np.einsum("i,j", self.a, self.b, optimize=True) + np.einsum("i,j", self.one_dim, self.one_dim, optimize=True) # multiply(a, b):trigger sum_of_products_contig_two def time_einsum_multiply(self, dtype): - np.einsum("..., ...", self.c1, self.c , optimize=True) + np.einsum("..., ...", self.two_dim_small, self.three_dim , optimize=True) # sum and multiply:trigger sum_of_products_contig_stride0_outstride0_two def time_einsum_sum_mul(self, dtype): - np.einsum(",i...->", 300, self.d, optimize=True) + np.einsum(",i...->", 300, self.three_dim_small, optimize=True) # sum and multiply:trigger sum_of_products_stride0_contig_outstride0_two def time_einsum_sum_mul2(self, dtype): - np.einsum("i...,->", self.d, 300, optimize=True)
\ No newline at end of file + np.einsum("i...,->", self.three_dim_small, 300, optimize=True) + + # scalar mul: trigger sum_of_products_stride0_contig_outcontig_two + def time_einsum_mul(self, dtype): + np.einsum("i,->i", self.one_dim_big, 300, optimize=True) + + # trigger contig_contig_outstride0_two + def time_einsum_contig_contig(self, dtype): + np.einsum("ji,i->", self.two_dim, self.one_dim_small, optimize=True) + + # trigger sum_of_products_contig_outstride0_one + def time_einsum_contig_outstride0(self, dtype): + np.einsum("i->", self.one_dim_big, optimize=True) + + # outer(a,b): non_contigous arrays + def time_einsum_noncon_outer(self, dtype): + np.einsum("i,j", self.non_contigous_dim1, self.non_contigous_dim1, optimize=True) + + # multiply(a, b):non_contigous arrays + def time_einsum_noncon_multiply(self, dtype): + np.einsum("..., ...", self.non_contigous_dim2, self.non_contigous_dim3 , optimize=True) + + # sum and multiply:non_contigous arrays + def time_einsum_noncon_sum_mul(self, dtype): + np.einsum(",i...->", 300, self.non_contigous_dim3, optimize=True) + + # sum and multiply:non_contigous arrays + def time_einsum_noncon_sum_mul2(self, dtype): + np.einsum("i...,->", self.non_contigous_dim3, 300, optimize=True) + + # scalar mul: non_contigous arrays + def time_einsum_noncon_mul(self, dtype): + np.einsum("i,->i", self.non_contigous_dim1, 300, optimize=True) + + # contig_contig_outstride0_two: non_contigous arrays + def time_einsum_noncon_contig_contig(self, dtype): + np.einsum("ji,i->", self.non_contigous_dim2, self.non_contigous_dim1_small, optimize=True) + + # sum_of_products_contig_outstride0_oneļ¼non_contigous arrays + def time_einsum_noncon_contig_outstride0(self, dtype): + np.einsum("i->", self.non_contigous_dim1, optimize=True) |