Merge pull request #13 from kuhyx/simpler_threads

Simpler threads

README.md

@@ -0,0 +1,23 @@
+cd code 
+
+pyenv init
+
+COPY AND PASTE PYENV CONFIGURATION TO YOUR SHELL CONFIG
+
+source ~/.zshrc (or your shell)
+
+pyenv install 3.11
+
+pyenv global 3.11
+
+Ensure that python DID change its version
+
+python --version
+
+python -m venv ./venv 
+
+source ./venv/bin/activate
+
+pip install -r requirements.txt
+
+python main.py

code/linear_algebra_utils.py

@@ -2,6 +2,7 @@ import cmath
 import math
 import itertools
 import operator
+import multiprocessing
 from multiprocessing import Pool
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor
@@ -10,7 +11,7 @@ from time_measurement import time_measurement, time_accumulator
 import numpy as np
 import dask.array as da
 
-class LinearAlgebraUtils(ABC):
+class LinearAlgebraUtils:
     @staticmethod
     @abstractmethod
     def dot_product(v1, v2):
@@ -62,7 +63,7 @@ class LinearAlgebraUtils(ABC):
         pass
 
 
-class SequentialLinearAlgebraUtils(ABC):
+class SequentialLinearAlgebraUtils:
     @staticmethod
     def dot_product(v1, v2):
         return sum(x*y for x, y in zip(v1, v2))
@@ -106,11 +107,10 @@ class SequentialLinearAlgebraUtils(ABC):
         return [[A[i][j] - B[i][j] for j in range(len(A[0]))] for i in range(len(A))]
 
 
-class ThreadsLinearAlgebraUtils(ABC):
-    NUM_THREADS = 4
+class ThreadsLinearAlgebraUtils:
+    NUM_THREADS = multiprocessing.cpu_count()
 
     @staticmethod
-    @time_measurement(time_accumulator)
     def get_chunk_size(data):
         num_elements = len(data)
         num_threads = min(ThreadsLinearAlgebraUtils.NUM_THREADS, num_elements)
@@ -118,9 +118,7 @@ class ThreadsLinearAlgebraUtils(ABC):
         remainder = num_elements % num_threads
         return chunk_size, num_threads, remainder
 
-
     @staticmethod
-    @time_measurement(time_accumulator)
     def divide_vectors_to_chunks(v1, v2):
         chunk_size, num_threads, remainder = ThreadsLinearAlgebraUtils.get_chunk_size(v1)
 
@@ -134,7 +132,6 @@ class ThreadsLinearAlgebraUtils(ABC):
         return chunks
     
     @staticmethod
-    @time_measurement(time_accumulator)
     def divide_vector_or_matrix_to_chunks(v):
         chunk_size, num_threads, remainder = ThreadsLinearAlgebraUtils.get_chunk_size(v)
 
@@ -147,15 +144,6 @@ class ThreadsLinearAlgebraUtils(ABC):
 
         return chunks
 
-
-    @staticmethod
-    @time_measurement(time_accumulator)
-    def dot_product(v1, v2):
-        chunks = ThreadsLinearAlgebraUtils.divide_vectors_to_chunks(v1, v2)
-        with ThreadPoolExecutor(max_workers=ThreadsLinearAlgebraUtils.NUM_THREADS) as executor:
-            results = executor.map(lambda pair: SequentialLinearAlgebraUtils.dot_product(*pair), chunks)
-        return sum(results)
-
     @staticmethod
     @time_measurement(time_accumulator)
     def matrix_vector_multiply(A, x):
@@ -178,23 +166,6 @@ class ThreadsLinearAlgebraUtils(ABC):
         total_sum = sum(results)
         return total_sum**0.5
 
-    @staticmethod
-    @time_measurement(time_accumulator)
-    def vector_scalar_divide(x, scalar):
-        chunks = ThreadsLinearAlgebraUtils.divide_vector_or_matrix_to_chunks(x)
-
-        with ThreadPoolExecutor(max_workers=ThreadsLinearAlgebraUtils.NUM_THREADS) as executor:
-            results = executor.map(lambda chunk: SequentialLinearAlgebraUtils.vector_scalar_divide(chunk, scalar), chunks)
-        return [item for sublist in results for item in sublist]
-
-    @staticmethod
-    @time_measurement(time_accumulator)
-    def matrix_scalar_multiply(A, w):
-        chunks = ThreadsLinearAlgebraUtils.divide_vector_or_matrix_to_chunks(A)
-        with ThreadPoolExecutor(max_workers=ThreadsLinearAlgebraUtils.NUM_THREADS) as executor:
-            results = executor.map(lambda chunk: SequentialLinearAlgebraUtils.matrix_scalar_multiply(w, chunk), chunks)
-        return [item for sublist in results for item in sublist]
-
     @staticmethod
     @time_measurement(time_accumulator)
     def vector_vector_subtraction(v1, v2):
@@ -221,54 +192,6 @@ class ThreadsLinearAlgebraUtils(ABC):
         
         return [item for sublist in results for item in sublist]
 
-    @staticmethod
-    @time_measurement(time_accumulator)
-    def matrix_norm(A):
-        chunks = ThreadsLinearAlgebraUtils.divide_vector_or_matrix_to_chunks(A)
-
-        def partial_norm(chunk):
-            return sum(element ** 2 for row in chunk for element in row)
-
-        with ThreadPoolExecutor(max_workers=ThreadsLinearAlgebraUtils.NUM_THREADS) as executor:
-            results = executor.map(partial_norm, chunks)
-
-        total_sum = sum(results)
-        return math.sqrt(total_sum)
-    
-    @staticmethod
-    @time_measurement(time_accumulator)
-    def divide_matrixes_to_chunks(A, B):
-        num_rows = len(A)
-        num_threads = ThreadsLinearAlgebraUtils.NUM_THREADS
-        if num_threads > num_rows:
-            num_threads = num_rows
-        if num_rows == 0:
-            return []
-        chunk_size = num_rows // num_threads
-        remainder = num_rows % num_threads
-        chunks = []
-        start = 0
-        for _ in range(num_threads):
-            end = start + chunk_size + (1 if remainder > 0 else 0)
-            chunks.append((A[start:end], B[start:end]))
-            start = end
-            if remainder > 0:
-                remainder -= 1
-        return chunks
-
-    @staticmethod
-    @time_measurement(time_accumulator)
-    def matrix_matrix_subtraction(A, B):
-
-        def subtract_chunk(pair):
-            chunk_A, chunk_B = pair
-            return [[chunk_A[i][j] - chunk_B[i][j] for j in range(len(chunk_A[0]))] for i in range(len(chunk_A))]
-
-        chunks = ThreadsLinearAlgebraUtils.divide_matrixes_to_chunks(A, B)
-        with ThreadPoolExecutor(max_workers=ThreadsLinearAlgebraUtils.NUM_THREADS) as executor:
-            results = executor.map(subtract_chunk, chunks)
-        return [row for chunk in results for row in chunk]
-
 
 @time_measurement(time_accumulator)
 def process_row(params):

code/requirements.txt

@@ -1,3 +1,4 @@
 scipy
 pytest
 dask 
+numba

code/tests.py

@@ -2,6 +2,7 @@ import pytest
 import numpy as np
 from matrix_generator import MatrixGenerator
 from richardson_method import RichardsonMethod
+from threads_indep import RichardsonMethodThreads
 from processing_type import ProcessingType
 from time_measurement import time_measurement, tests_time
 
@@ -67,11 +68,15 @@ def test_richardson_vs_cg(n: int, processing_type: ProcessingType, matrix_type:
     else:
         raise ValueError("Invalid matrix type specified. Choose 'spd', 'poli3', or 'nemeth12'.")
     
-    richardson_solver = RichardsonMethod(processing_type, A, b, lambda_min, lambda_max, max_iterations, size=A.shape[0], tol=1e-7)
-
     solution_richardson, info_richardson = None, None
+
+    if processing_type != ProcessingType.THREADS:
+        richardson_solver = RichardsonMethod(processing_type, A, b, lambda_min, lambda_max, max_iterations, size=A.shape[0], tol=1e-7)
         with capsys.disabled():
             solution_richardson, info_richardson = richardson_solver.solve()
+    else:
+        with capsys.disabled():
+            solution_richardson, info_richardson = RichardsonMethodThreads(A, b, lambda_min, lambda_max, max_iterations, tol=1e-7)
 
     # Przechwytywanie wyjścia po solve
     captured = capsys.readouterr()

code/threads_indep.py

@@ -0,0 +1,93 @@
+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
+
+
+@njit(parallel=True)
+def numba_matrix_vector_multiply(A, input_x, Ax):
+    for i in prange(len(A)):
+        acc = 0.0
+        for j in range(len(input_x)):
+            acc += A[i][j] * input_x[j]
+        Ax[i] = acc
+
+@njit(parallel=True)
+def numba_vector_vector_subtraction(b, Ax, residual):
+    for i in prange(len(b)):
+        residual[i] = b[i] - Ax[i]
+
+@njit(nopython=True)
+def numba_scalar_vector_multiply(omega, vector, result):
+    omega_real = omega.real 
+    for i in range(len(vector)):
+        result[i] = omega_real * 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()
+
+    x0 = x0 if x0 is not None else [0.0] * len(b)
+    x = x0[:]
+
+    omega = 2 / (lambda_min + lambda_max)
+    n = len(b)
+
+    for iteration in range(max_iterations):
+        Ax = [0.0] * n
+        matrix_vector_multiply(A, x, Ax)
+        longest_threads_time_accumulator.save_lap_and_reset()
+
+        residual = [0.0] * n
+        vector_vector_subtraction(b, Ax, residual)
+        longest_threads_time_accumulator.save_lap_and_reset()
+
+        change_vector = [0.0] * n
+        scalar_vector_multiply(omega, residual, change_vector)
+        longest_threads_time_accumulator.save_lap_and_reset()
+
+        _x = [0.0] * n
+        vector_vector_addition(x, change_vector, _x)
+        longest_threads_time_accumulator.save_lap_and_reset()
+
+        x = _x[:]
+
+        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

code/threads_indep_old.py

@@ -0,0 +1,103 @@
+import multiprocessing
+import gc
+import time
+from concurrent.futures import ThreadPoolExecutor
+from time_measurement import time_measurement_longest, longest_threads_time_accumulator, tests_time
+import linear_algebra_utils as linAlg
+
+
+@time_measurement_longest(longest_threads_time_accumulator)
+def matrix_vector_multiply(A, input_x, start, end, Ax):
+    Ax[start:end] = [sum(x*y for x, y in zip(row, input_x)) for row in A[start:end]]
+
+@time_measurement_longest(longest_threads_time_accumulator)
+def vector_vector_subtraction(b, Ax, start, end, residual):
+    residual[start:end] = [x-y for x, y in zip(b[start:end], Ax[start:end])]
+
+@time_measurement_longest(longest_threads_time_accumulator)
+def scalar_vector_multiply(omega, vector, start, end, result):
+    result[start:end] = [omega * x for x in vector[start:end]]
+
+@time_measurement_longest(longest_threads_time_accumulator)
+def vector_vector_addition(input_x, vector, start, end, output_x):
+    output_x[start:end] = [x+y for x, y in zip(input_x[start:end], vector[start:end])]
+
+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()
+
+    n = len(b)
+    x0 = x0 if x0 is not None else [0.0] * len(b)
+    x = x0[:]
+    omega = 2 / (lambda_min + lambda_max)
+    num_threads = multiprocessing.cpu_count()
+    chunk_size = n // num_threads
+
+    with ThreadPoolExecutor(max_workers=num_threads) as executor: # wątki są tworzone raz i nie są niszczone
+        for iteration in range(max_iterations):
+
+            Ax = [0] * len(x) # tutaj zostanie przypisany wynik z mnożenia macierzy A z wektorem x
+            futures = []
+
+            for i in range(num_threads):
+                start = i * chunk_size
+                end = n if i == num_threads - 1 else (i + 1) * chunk_size
+                futures.append(executor.submit(matrix_vector_multiply, A, x, start, end, Ax))
+            
+            for future in futures:
+                future.result()
+
+            longest_threads_time_accumulator.save_lap_and_reset()
+            residual = [0] * len(b) # tutaj zostanie przypisany wynik z vector_vector_subtraction
+            futures = []
+
+            for i in range(num_threads):
+                start = i * chunk_size
+                end = n if i == num_threads - 1 else (i + 1) * chunk_size
+                futures.append(executor.submit(vector_vector_subtraction, b, Ax, start, end, residual))
+            
+            for future in futures:
+                future.result()
+
+            longest_threads_time_accumulator.save_lap_and_reset()
+            change_vector = [0] * len(residual) # zostanie tu przypisany wynik scalar_vector_multiply po pracy wątków
+            futures = []
+
+            for i in range(num_threads):
+                start = i * chunk_size
+                end = n if i == num_threads - 1 else (i + 1) * chunk_size
+                futures.append(executor.submit(scalar_vector_multiply, omega, residual, start, end, change_vector))
+            
+            for future in futures:
+                future.result()
+
+            longest_threads_time_accumulator.save_lap_and_reset()
+            _x = x[:] # do _x zostanie przez wątki przypisany wynik pracy w danej iteracji
+            futures = []
+
+            for i in range(num_threads):
+                start = i * chunk_size
+                end = n if i == num_threads - 1 else (i + 1) * chunk_size
+                futures.append(executor.submit(vector_vector_addition, x, change_vector, start, end, _x))
+            
+            for future in futures:
+                future.result()
+                
+            longest_threads_time_accumulator.save_lap_and_reset()
+            x = _x[:]
+
+            
+            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

code/threads_too_simple.py

@@ -0,0 +1,60 @@
+import numpy as np
+import threading
+import multiprocessing
+import gc
+import time
+import sys
+from time_measurement import time_measurement_longest, longest_time_accumulator, tests_time
+import linear_algebra_utils as linAlg
+
+
+@time_measurement_longest(longest_time_accumulator)
+def RichardsonThread(A, b, x, _x, omega, start, end):
+    for i in range(start, end):
+        sigma = np.dot(A[i, :], _x) - A[i, i] * _x[i]
+        x[i] = (1 - omega) * _x[i] + omega * (b[i] - sigma) / A[i, i]
+
+def RichardsonMethodThreads(A, b, lambda_min, lambda_max, max_iterations, x0=None, tol=1e-5):
+    longest_time_accumulator.total_time = 0
+    longest_time_accumulator.start = sys.float_info.max
+    longest_time_accumulator.end = 0
+
+    gc.disable()
+    start_time = time.perf_counter()
+
+    n = len(b)
+    x0 = x0 if x0 is not None else [0.0] * len(b)
+    x = x0[:]
+    omega = 0.05#2 / (lambda_min + lambda_max)
+    num_threads = multiprocessing.cpu_count()
+    threads = []
+    chunk_size = n // num_threads
+    max_iterations = 1000
+
+    for _ in range(max_iterations):
+        _x = x[:]
+        for i in range(num_threads):
+            start = i * chunk_size # start jest indeksem w A. Wątki otrzymują kolejny punkt startowy będący wielokrotnością rozmiaru porcji na wątek
+            end = n if i == num_threads - 1 else (i + 1) * chunk_size
+            thread = threading.Thread(target=RichardsonThread, args=(A, b, x, _x, omega, start, end))
+            threads.append(thread)
+            thread.start()
+        
+        for thread in threads:
+            thread.join()
+        
+        # Ax = linAlg.SequentialLinearAlgebraUtils.matrix_vector_multiply(A, x)
+        # residual = linAlg.SequentialLinearAlgebraUtils.vector_vector_subtraction(b, Ax)
+        # 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_time_accumulator.total_time
+    
+    print(f"Total: {total_time:.3e}s, Seq: {sequential_time:.3e}s, Parallel (threads): {longest_time_accumulator.total_time:.3e}s, Tests time: {tests_time.total_time:.3e}s")
+    
+    return x, 0
+
+

code/time_measurement.py

@@ -1,14 +1,35 @@
 import time
+import sys
 from functools import wraps
 
 class TimeAccumulator:
     def __init__(self):
         self.total_time = 0
 
+class ComplexTimeAcumulator:
+    def __init__(self):
+        self.hard_reset()
+    
+    def hard_reset(self):
+        self.total_time = 0
+        self.reset()
+    
+    def reset(self):
+        self.lap_time = 0
+        self.start = sys.float_info.max
+        self.end = 0
+        
+    def save_lap_and_reset(self):
+        self.total_time += self.lap_time
+        self.reset()
+
+
 time_accumulator = TimeAccumulator()
 tests_time = TimeAccumulator()
 
-def time_measurement(accumulator):
+longest_threads_time_accumulator = ComplexTimeAcumulator()
+
+def time_measurement(accumulator: TimeAccumulator):
     def decorator(func):
         @wraps(func)
         def inner(*args, **kwargs):
@@ -20,6 +41,22 @@ def time_measurement(accumulator):
         return inner
     return decorator
 
+def time_measurement_longest(accumulator: ComplexTimeAcumulator):
+    def decorator(func):
+        @wraps(func)
+        def inner(*args, **kwargs):
+            start = time.perf_counter()
+            result = func(*args, **kwargs)
+            end = time.perf_counter()
+            if start < accumulator.start:
+                accumulator.start = start
+            if end > accumulator.end:
+                accumulator.end = end
+            accumulator.lap_time = accumulator.end - accumulator.start # "=" instead of "+="
+            return result
+        return inner
+    return decorator
+