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https://github.com/kuhyx/WUT_Computer_Science.git
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88 lines
2.9 KiB
Python
88 lines
2.9 KiB
Python
import gc
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import time
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from numba import njit, prange
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from time_measurement import time_measurement_longest, longest_threads_time_accumulator, tests_time
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import linear_algebra_utils as linAlg
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@njit(parallel=True)
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def numba_matrix_vector_multiply(A, input_x, Ax):
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for i in prange(len(A)):
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Ax[i] = sum(A[i][j] * input_x[j] for j in range(len(input_x)))
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@njit(parallel=True)
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def numba_vector_vector_subtraction(b, Ax, residual):
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for i in prange(len(b)):
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residual[i] = b[i] - Ax[i]
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@njit(parallel=True)
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def numba_scalar_vector_multiply(omega, vector, result):
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for i in prange(len(vector)):
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result[i] = omega * vector[i]
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@njit(parallel=True)
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def numba_vector_vector_addition(input_x, vector, output_x):
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for i in prange(len(input_x)):
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output_x[i] = input_x[i] + vector[i]
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# Funkcje z dekoratorem
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@time_measurement_longest(longest_threads_time_accumulator)
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def matrix_vector_multiply(A, input_x, Ax):
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numba_matrix_vector_multiply(A, input_x, Ax)
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@time_measurement_longest(longest_threads_time_accumulator)
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def vector_vector_subtraction(b, Ax, residual):
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numba_vector_vector_subtraction(b, Ax, residual)
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@time_measurement_longest(longest_threads_time_accumulator)
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def scalar_vector_multiply(omega, vector, result):
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numba_scalar_vector_multiply(omega, vector, result)
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@time_measurement_longest(longest_threads_time_accumulator)
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def vector_vector_addition(input_x, vector, output_x):
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numba_vector_vector_addition(input_x, vector, output_x)
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# Metoda Richardson z obsługą wątków
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def RichardsonMethodThreads(A, b, lambda_min, lambda_max, max_iterations, x0=None, tol=1e-5):
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longest_threads_time_accumulator.hard_reset()
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gc.disable()
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start_time = time.perf_counter()
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x0 = x0 if x0 is not None else [0.0] * len(b)
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x = x0[:]
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omega = 2 / (lambda_min + lambda_max)
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n = len(b)
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for iteration in range(max_iterations):
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Ax = [0.0] * n
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matrix_vector_multiply(A, x, Ax)
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longest_threads_time_accumulator.save_lap_and_reset()
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residual = [0.0] * n
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vector_vector_subtraction(b, Ax, residual)
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longest_threads_time_accumulator.save_lap_and_reset()
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change_vector = [0.0] * n
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scalar_vector_multiply(omega, residual, change_vector)
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longest_threads_time_accumulator.save_lap_and_reset()
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_x = [0.0] * n
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vector_vector_addition(x, change_vector, _x)
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longest_threads_time_accumulator.save_lap_and_reset()
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x = _x[:]
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if linAlg.SequentialLinearAlgebraUtils.vector_norm(residual) < tol:
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break
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end_time = time.perf_counter()
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gc.enable()
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total_time = end_time - start_time
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sequential_time = total_time - longest_threads_time_accumulator.total_time
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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")
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return x, 0
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