Merge pull request #231 from ashvardanian/main-dev

Better Profiling

Author: ashvardanian

scripts/bench.cxx

@@ -819,10 +819,6 @@ int main(int argc, char **argv) {
     dense_<f16_k>("l2sq_f16_sve", simsimd_l2sq_f16_sve, simsimd_l2sq_f16_accurate);
     dense_<f16_k>("l2_f16_sve", simsimd_l2_f16_sve, simsimd_l2_f16_accurate);
 
-    dense_<bf16_k>("cos_bf16_sve", simsimd_cos_bf16_sve, simsimd_cos_bf16_accurate);
-    dense_<bf16_k>("l2sq_bf16_sve", simsimd_l2sq_bf16_sve, simsimd_l2sq_bf16_accurate);
-    dense_<bf16_k>("l2_bf16_sve", simsimd_l2_bf16_sve, simsimd_l2_bf16_accurate);
-
     dense_<f32_k>("dot_f32_sve", simsimd_dot_f32_sve, simsimd_dot_f32_accurate);
     dense_<f32_k>("cos_f32_sve", simsimd_cos_f32_sve, simsimd_cos_f32_accurate);
     dense_<f32_k>("l2sq_f32_sve", simsimd_l2sq_f32_sve, simsimd_l2sq_f32_accurate);

scripts/bench_vectors.py

@@ -72,6 +72,21 @@ class Kernel:
     tensor_type: callable = np.array
 
 
+def serial_cosine(a: List[float], b: List[float]) -> float:
+    dot_product = sum(ai * bi for ai, bi in zip(a, b))
+    norm_a = sum(ai * ai for ai in a) ** 0.5
+    norm_b = sum(bi * bi for bi in b) ** 0.5
+    if norm_a == 0 and norm_b == 0:
+        return 1
+    if dot_product == 0:
+        return 0
+    return dot_product / (norm_a * norm_b)
+
+
+def serial_sqeuclidean(a: List[float], b: List[float]) -> float:
+    return sum((ai - bi) ** 2 for ai, bi in zip(a, b))
+
+
 def yield_kernels(
     metric_families: List[str],
     dtype_names: List[str],
@@ -197,6 +212,25 @@ def for_dtypes(
             simd.vdot,
             lambda A, B: simd.cdist(A, B, metric="vdot"),
         )
+    if "spatial" in metric_families:
+        yield from for_dtypes(
+            "serial.cosine",
+            ["float64", "float32", "float16", "int8"],
+            serial_cosine,
+            wrap_rows_batch_calls(serial_cosine),
+            lambda A, B: spd.cdist(A, B, "cosine"),
+            simd.cosine,
+            lambda A, B: simd.cdist(A, B, metric="cosine"),
+        )
+        yield from for_dtypes(
+            "serial.sqeuclidean",
+            ["float64", "float32", "float16", "int8"],
+            serial_sqeuclidean,
+            wrap_rows_batch_calls(serial_sqeuclidean),
+            lambda A, B: spd.cdist(A, B, "sqeuclidean"),
+            simd.sqeuclidean,
+            lambda A, B: simd.cdist(A, B, metric="sqeuclidean"),
+        )
     if "spatial" in metric_families and include_scipy:
         yield from for_dtypes(
             "scipy.cosine",
@@ -528,11 +562,11 @@ def result_to_row(result: Result) -> List[str]:
     if isinstance(result.baseline_seconds, float):
         ops_per_second = result.distance_calculations / result.baseline_seconds
         gbs_per_second = result.bytes_per_vector * ops_per_second / 1e9
-        baseline_cell = f"{ops_per_second:,.0f} ops/s, {gbs_per_second:,.2f} GB/s"
+        baseline_cell = f"{ops_per_second:,.0f} ops/s, {gbs_per_second:,.3f} GB/s"
     if isinstance(result.simsimd_seconds, float):
         ops_per_second = result.distance_calculations / result.simsimd_seconds
         gbs_per_second = result.bytes_per_vector * ops_per_second / 1e9
-        simsimd_cell = f"{ops_per_second:,.0f} ops/s, {gbs_per_second:,.2f} GB/s"
+        simsimd_cell = f"{ops_per_second:,.0f} ops/s, {gbs_per_second:,.3f} GB/s"
     if isinstance(result.baseline_seconds, float) and isinstance(result.simsimd_seconds, float):
         improvement_cell = f"{result.baseline_seconds / result.simsimd_seconds:,.2f} x"