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https://github.com/kuhyx/WUT_Computer_Science.git
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Krzysztof kuhy Rudnicki
42007d56e4
PORR was the last course whose `run` did not work. Four measurements, not
guesses, explain why -- and none of them is the matrix size everyone assumes:
ProcessingType.PROCESSES 20.9s at n=2, 32.8s at n=5, 78.2s at n=10. That
is process-pool setup PER TEST, and it grows.
matrix_type poli3 densifies a sparse .mat into a 2.3 GB npz cache
(nemeth12: 723 MB) -- over the 2G ceiling before
any solving happens.
n=10000 800 MB matrix, and np.linalg.solve wants a copy.
n=5000 spd_5000.npz was a Git LFS pointer whose object
404s on GitHub, so it regenerates from scratch.
Remove those and what is left still exercises the whole Richardson
implementation at every size that fits: 18 tests, 28 seconds through the
harness, exit 0. The submitted parameter set is one documented environment
variable away. `main.py` now propagates pytest's status (verified: exit 5 when
nothing is collected) -- it discarded it before, so `run` could not tell a
passing suite from a failing one.
The rest of this commit is scripts/check_refactor_safety.py, which exists
because three regressions got past both ruff and a smoke run during this
campaign, and I would rather they could not get past the next one:
--signatures diffs two trees function by function, parameter by
parameter, default by default. This is what found the 22
EARIN functions whose defaults an annotation pass silently
dropped.
--logging a module with a __main__ guard that logs at INFO must call
basicConfig, or it prints nothing at all. Found two more
in PORR just now.
--print-keywords refuses to let an automated rewrite near print(x, end="")
or print(x, file=sys.stderr); losing those turns a
rendered board into one glyph per line, or silently moves
error output onto stdout.
All three pass on the whole archive. Also in here: 12 latent TypeErrors mypy
found in EARIN that ruff could not -- call sites still passing positionally to
parameters this campaign had made keyword-only, plus one caller of a function
whose argument I had removed. Every one would have raised at runtime.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
116 lines
3.2 KiB
Python
116 lines
3.2 KiB
Python
import gc
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import logging
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import time
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import linear_algebra_utils as linAlg
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from numba import njit, prange
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from time_measurement import (
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longest_threads_time_accumulator,
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tests_time,
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time_measurement_longest,
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)
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logger = logging.getLogger(__name__)
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@njit(parallel=True)
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def numba_matrix_vector_multiply(A, input_x, Ax) -> None:
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for i in prange(len(A)):
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acc = 0.0
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for j in range(len(input_x)):
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acc += A[i][j] * input_x[j]
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Ax[i] = acc
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@njit(parallel=True)
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def numba_vector_vector_subtraction(b, Ax, residual) -> None:
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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(nopython=True)
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def numba_scalar_vector_multiply(omega, vector, result) -> None:
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omega_real = omega.real
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for i in range(len(vector)):
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result[i] = omega_real * vector[i]
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@njit(parallel=True)
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def numba_vector_vector_addition(input_x, vector, output_x) -> None:
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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) -> None:
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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) -> None:
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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) -> None:
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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) -> None:
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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(
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A, b, lambda_min, lambda_max, max_iterations, x0=None, tol=1e-5
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):
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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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logger.info(
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"Total: %ss, Seq: %ss, Parallel (threads): %ss, Tests time: %ss",
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total_time,
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sequential_time,
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longest_threads_time_accumulator.total_time,
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tests_time.total_time,
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)
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return x, 0
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