remove: file

code/threads_indep_numba_numpy.py

@@ -1,91 +0,0 @@
-import gc
-import time
-import numpy as np
-from numba import njit, prange
-from time_measurement import time_measurement_longest, longest_threads_time_accumulator, tests_time
-import linear_algebra_utils as linAlg
-
-# Funkcje równoległe z Numba
-@njit(parallel=True)
-def numba_matrix_vector_multiply(A, input_x, Ax):
-    n, m = A.shape
-    for i in prange(n):
-        Ax[i] = np.dot(A[i], input_x)
-
-@njit(parallel=True)
-def numba_vector_vector_subtraction(b, Ax, residual):
-    for i in prange(len(b)):
-        residual[i] = b[i] - Ax[i]
-
-@njit(parallel=True)
-def numba_scalar_vector_multiply(omega, vector, result):
-    for i in prange(len(vector)):
-        result[i] = omega * vector[i]
-
-@njit(parallel=True)
-def numba_vector_vector_addition(input_x, vector, output_x):
-    for i in prange(len(input_x)):
-        output_x[i] = input_x[i] + vector[i]
-
-# Funkcje z dekoratorem
-@time_measurement_longest(longest_threads_time_accumulator)
-def matrix_vector_multiply(A, input_x, Ax):
-    numba_matrix_vector_multiply(A, input_x, Ax)
-
-@time_measurement_longest(longest_threads_time_accumulator)
-def vector_vector_subtraction(b, Ax, residual):
-    numba_vector_vector_subtraction(b, Ax, residual)
-
-@time_measurement_longest(longest_threads_time_accumulator)
-def scalar_vector_multiply(omega, vector, result):
-    numba_scalar_vector_multiply(omega, vector, result)
-
-@time_measurement_longest(longest_threads_time_accumulator)
-def vector_vector_addition(input_x, vector, output_x):
-    numba_vector_vector_addition(input_x, vector, output_x)
-
-# Metoda Richardson z obsługą wątków
-def RichardsonMethodThreads(A, b, lambda_min, lambda_max, max_iterations, x0=None, tol=1e-5):
-    longest_threads_time_accumulator.hard_reset()
-
-    gc.disable()
-    start_time = time.perf_counter()
-
-    A = np.array(A)
-    b = np.array(b)
-    x0 = np.array(x0) if x0 is not None else np.zeros_like(b)
-    x = x0.copy()
-
-    omega = 2 / (lambda_min + lambda_max)
-    n = len(b)
-
-    for iteration in range(max_iterations):
-        Ax = np.zeros_like(x)
-        matrix_vector_multiply(A, x, Ax)
-        longest_threads_time_accumulator.save_lap_and_reset()
-
-        residual = np.zeros_like(b)
-        vector_vector_subtraction(b, Ax, residual)
-        longest_threads_time_accumulator.save_lap_and_reset()
-
-        change_vector = np.zeros_like(residual)
-        scalar_vector_multiply(omega, residual, change_vector)
-        longest_threads_time_accumulator.save_lap_and_reset()
-
-        _x = np.zeros_like(x)
-        vector_vector_addition(x, change_vector, _x)
-        longest_threads_time_accumulator.save_lap_and_reset()
-
-        x = _x.copy()
-
-        if linAlg.SequentialLinearAlgebraUtils.vector_norm(residual) < tol:
-            break
-
-    end_time = time.perf_counter()
-    gc.enable()
-    total_time = end_time - start_time
-    sequential_time = total_time - longest_threads_time_accumulator.total_time
-
-    print(f"Total: {total_time:.3e}s, Seq: {sequential_time:.3e}s, Parallel (threads): {longest_threads_time_accumulator.total_time:.3e}s, Tests time: {tests_time.total_time:.3e}s")
-
-    return x, 0