almost ready

Author: aseembits93

code_to_optimize/sample_jit_code.py code_to_optimize/sample_code.pycode_to_optimize/sample_jit_code.py renamed to code_to_optimize/sample_code.py

@@ -10,48 +10,28 @@
 def tridiagonal_solve(a: np.ndarray, b: np.ndarray, c: np.ndarray, d: np.ndarray) -> np.ndarray:
     n = len(b)
 
-    c_prime = np.empty(n - 1, dtype=np.float64)
-    d_prime = np.empty(n, dtype=np.float64)
-    x = np.empty(n, dtype=np.float64)
-
-    # Alias arrays to local variables to avoid repeated attribute lookups
-    a_arr = a
-    b_arr = b
-    c_arr = c
-    d_arr = d
-    cp = c_prime
-    dp = d_prime
-    x_arr = x
-
-    # First element
-    prev_cprime = c_arr[0] / b_arr[0]
-    cp[0] = prev_cprime
-    prev_dprime = d_arr[0] / b_arr[0]
-    dp[0] = prev_dprime
-
-    # Forward sweep (compute c_prime and d_prime)
+    # Create working copies to avoid modifying input
+    c_prime = np.zeros(n - 1, dtype=np.float64)
+    d_prime = np.zeros(n, dtype=np.float64)
+    x = np.zeros(n, dtype=np.float64)
+
+    # Forward sweep - sequential dependency: c_prime[i] depends on c_prime[i-1]
+    c_prime[0] = c[0] / b[0]
+    d_prime[0] = d[0] / b[0]
 
     for i in range(1, n - 1):
-        ai_1 = a_arr[i - 1]
-        denom = b_arr[i] - ai_1 * prev_cprime
-        curr_cprime = c_arr[i] / denom
-        curr_dprime = (d_arr[i] - ai_1 * prev_dprime) / denom
-        cp[i] = curr_cprime
-        dp[i] = curr_dprime
-        prev_cprime = curr_cprime
-        prev_dprime = curr_dprime
-
-    # Last d_prime entry
-    denom = b_arr[n - 1] - a_arr[n - 2] * prev_cprime
-    dp[n - 1] = (d_arr[n - 1] - a_arr[n - 2] * prev_dprime) / denom
-
-    # Back substitution using a scalar for the "next x" value
-    prev_x = dp[n - 1]
-    x_arr[n - 1] = prev_x
+        denom = b[i] - a[i - 1] * c_prime[i - 1]
+        c_prime[i] = c[i] / denom
+        d_prime[i] = (d[i] - a[i - 1] * d_prime[i - 1]) / denom
+
+    # Last row of forward sweep
+    denom = b[n - 1] - a[n - 2] * c_prime[n - 2]
+    d_prime[n - 1] = (d[n - 1] - a[n - 2] * d_prime[n - 2]) / denom
+
+    # Back substitution - sequential dependency: x[i] depends on x[i+1]
+    x[n - 1] = d_prime[n - 1]
     for i in range(n - 2, -1, -1):
-        xi = dp[i] - cp[i] * prev_x
-        x_arr[i] = xi
-        prev_x = xi
+        x[i] = d_prime[i] - c_prime[i] * x[i + 1]
 
     return x
 

code_to_optimize/tests/pytest/test_jax_jit_code.py

@@ -10,7 +10,7 @@
 jax = pytest.importorskip("jax")
 import jax.numpy as jnp
 
-from code_to_optimize.sample_jit_code import (
+from code_to_optimize.sample_code import (
     leapfrog_integration_jax,
     longest_increasing_subsequence_length_jax,
     tridiagonal_solve_jax,

code_to_optimize/tests/pytest/test_numba_jit_code.py

@@ -1,7 +1,7 @@
 import numpy as np
 import pytest
 
-from code_to_optimize.sample_jit_code import (
+from code_to_optimize.sample_code import (
     leapfrog_integration,
     longest_increasing_subsequence_length,
     tridiagonal_solve,

code_to_optimize/tests/pytest/test_tensorflow_jit_code.py

@@ -9,7 +9,7 @@
 
 tf = pytest.importorskip("tensorflow")
 
-from code_to_optimize.sample_jit_code import (
+from code_to_optimize.sample_code import (
     leapfrog_integration_tf,
     longest_increasing_subsequence_length_tf,
     tridiagonal_solve_tf,

code_to_optimize/tests/pytest/test_torch_jit_code.py

@@ -9,7 +9,7 @@
 
 torch = pytest.importorskip("torch")
 
-from code_to_optimize.sample_jit_code import (
+from code_to_optimize.sample_code import (
     leapfrog_integration_torch,
     longest_increasing_subsequence_length_torch,
     tridiagonal_solve_torch,