How to terminate the local threads ? Trying to circumscribe the problem...
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@ -8,11 +8,13 @@ local_threads_number = 7 # in a more realistic model: local threads number
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# then, locations would be picked at random in this great global arrows state
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# but the goal of this "micro-model" is not to explore the algorithms of choice of an arrow in the state
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# it is to fix the way the scheduler terminates the local threads that have done their task
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#
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# in a realistic model, the number of cycles is infinite (the user decides when to stop a running simulation)
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# and the size of the "renew" list (the list of the active local threads) depends on the computational power available
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# this "micro-model" allows to explore the ineractions [scheduler <-> local threads] when the the "renew" list size varies
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#
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renew_length = 16 # renew_length can vary from 1 to number_of_cycles (if more, a part of it remains empty)
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number_of_cycles = 20 # in a realistic model: number_of_cycles >> renew_length
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number_of_cycles = 24 # in a realistic model: number_of_cycles >> renew_length
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renew = []
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done = []
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@ -50,13 +52,14 @@ def disp(coord, id, start, prev, next):
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def local_thread(coord, id):
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start = datetime.now().timestamp()
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val = random.randint(1,1000)
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time.sleep(val / 1000)
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time.sleep(val / 1000) # pourquoi y a-t-il toujours au moins un local thread qui travaille (ou dort !...) pendant toute la durée de la simulation ?
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prev = arrows[coord]
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next = arrows[coord] = 10 + val % 89 # ou n'importe quelle autre modification !...
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done.append(id)
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for i in range(0, renew_length):
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if renew[i] == id:
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renew[i] = 0
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# time.sleep(100000) faute de savoir les arrêter, j'essaie de les "endormir"... mais ça ne marche pas !
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break
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disp(coord, id, start, prev, next)
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@ -68,9 +71,11 @@ for id in range (0, number_of_cycles):
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break
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# là où les local_thread qui se terminent ont écrit un zéro,
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# les local_thread nouvellement créés devraient apparaitre !?
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# c'est peut-être un problème d'affichage: comme les local_threads ne sont pas terminés,
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# ils continuent à afficher un zéro dans "renew" ?
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t = Thread(target=local_thread, args=(random.randint(0, len(arrows) - 1), id))
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t.start()
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time.sleep(1)
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time.sleep(1.5)
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print(' ',copy,' < initial global arrows state (to compare)')
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print('history: ',done) # done.sort() # print(done)
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@ -79,7 +84,6 @@ print('history: ',done) # done.sort() # print(done)
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"""
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Le **scheduler**, ou processus principal, effectue un calcul sur l'**état global**.
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Pour cela, il génère des threads de calcul locaux ou '**local_threads**'
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