402 lines
74 KiB
Plaintext
402 lines
74 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import requests\n",
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"import random\n",
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"import math\n",
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"import time\n",
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"import threading\n",
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"import logging\n",
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"logging.getLogger().setLevel(logging.INFO)\n",
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"\n",
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"# Pre generated primes\n",
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"first_primes_list = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29,\n",
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" 31, 37, 41, 43, 47, 53, 59, 61, 67,\n",
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" 71, 73, 79, 83, 89, 97, 101, 103,\n",
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" 107, 109, 113, 127, 131, 137, 139,\n",
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" 149, 151, 157, 163, 167, 173, 179,\n",
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" 181, 191, 193, 197, 199, 211, 223,\n",
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" 227, 229, 233, 239, 241, 251, 257,\n",
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" 263, 269, 271, 277, 281, 283, 293,\n",
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" 307, 311, 313, 317, 331, 337, 347, 349]\n",
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"\n",
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"\n",
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"def nBitRandom(n):\n",
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" return random.randrange(2**(n-1)+1, 2**n - 1)\n",
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" \n",
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"def getLowLevelPrime(n):\n",
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" '''Generate a prime candidate divisible\n",
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" by first primes'''\n",
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" while True:\n",
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" # Obtain a random number\n",
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" pc = nBitRandom(n)\n",
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" \n",
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" # Test divisibility by pre-generated\n",
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" # primes\n",
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" for divisor in first_primes_list:\n",
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" if pc % divisor == 0 and divisor**2 <= pc:\n",
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" break\n",
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" else: return pc\n",
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"\n",
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"def isMillerRabinPassed(mrc):\n",
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" '''Run 20 iterations of Rabin Miller Primality test'''\n",
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" maxDivisionsByTwo = 0\n",
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" ec = mrc-1\n",
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" while ec % 2 == 0:\n",
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" ec >>= 1\n",
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" maxDivisionsByTwo += 1\n",
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" assert(2**maxDivisionsByTwo * ec == mrc-1)\n",
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" \n",
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" def trialComposite(round_tester):\n",
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" if pow(round_tester, ec, mrc) == 1:\n",
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" return False\n",
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" for i in range(maxDivisionsByTwo):\n",
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" if pow(round_tester, 2**i * ec, mrc) == mrc-1:\n",
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" return False\n",
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" return True\n",
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" \n",
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" # Set number of trials here\n",
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" numberOfRabinTrials = 20\n",
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" for i in range(numberOfRabinTrials):\n",
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" round_tester = random.randrange(2, mrc)\n",
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" if trialComposite(round_tester):\n",
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" return False\n",
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" return True\n",
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" \n",
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"def random_large_prime(bits):\n",
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" while True:\n",
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" prime_candidate = getLowLevelPrime(bits)\n",
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" if not isMillerRabinPassed(prime_candidate):\n",
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" continue\n",
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" else:\n",
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" return prime_candidate\n",
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"\n",
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"def thread_function(i, fast, timeout):\n",
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" start = time.time()\n",
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"\n",
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" c = 5 # bits: 20: 200ms; 21: 350ms; 22: 700ms 23: 1.5s; 25: 6s; 26: 10s; 27: 24s\n",
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" bits = 19 if fast else 23\n",
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" last_report = time.time()\n",
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" processing_time = 0.0\n",
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" reqs = 0\n",
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" while True:\n",
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" iter_start = time.time()\n",
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" if iter_start - start > timeout:\n",
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" logging.info(\"Thread: %d\\treqs: %d\\tmean time: %.3fs\\t%s\"%(i, reqs, processing_time/reqs if reqs>0 else 0.0, \"fast\\t\" if fast else \"\"))\n",
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" results[i][iter_start] = processing_time/reqs if reqs>0 else 0.0\n",
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" return\n",
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" if iter_start - last_report > UNIT/2:\n",
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" if len(results[i])%2 == 0:\n",
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" logging.info(\"Thread: %d\\treqs: %d\\tmean time: %.3fs\\t%s\"%(i, reqs, processing_time/reqs if reqs>0 else 0.0, \"fast\\t\" if fast else \"\"))\n",
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" results[i][iter_start] = processing_time/reqs if reqs>0 else 0.0\n",
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" processing_time = 0.0\n",
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" reqs = 0\n",
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" last_report=iter_start\n",
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"\n",
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" factors = [random_large_prime(bits) for i in range(c)]\n",
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" factors.sort()\n",
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" n=math.prod(factors)\n",
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" req_addr = API_URL+'/factors/%d'%(n)\n",
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" r = requests.get(req_addr )\n",
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" if r.status_code > 299:\n",
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" logging.error(\"wrong status code from webservice for addr: \" + req_addr )\n",
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" else: \n",
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" result = r.json()\n",
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" if result != factors:\n",
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" logging.error(\"Wrong factors\")\n",
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"\n",
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" processing_time+=time.time() - iter_start\n",
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" reqs+=1\n",
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" time.sleep(0.5)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 4.2 - AWS"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"API_URL=\"http://3.219.135.136:8080\"\n",
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"UNIT = 60.0 # secs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"INFO:root:Thread: 0\treqs: 36\tmean time: 0.340s\tfast\t\n",
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"INFO:root:Thread: 0\treqs: 35\tmean time: 0.350s\tfast\t\n",
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"INFO:root:Thread: 1\treqs: 14\tmean time: 1.786s\t\n",
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"INFO:root:Thread: 0\treqs: 30\tmean time: 0.511s\tfast\t\n",
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"INFO:root:Thread: 1\treqs: 14\tmean time: 1.712s\t\n",
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"INFO:root:Thread: 0\treqs: 29\tmean time: 0.548s\tfast\t\n",
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"INFO:root:Thread: 2\treqs: 13\tmean time: 1.889s\t\n",
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"INFO:root:Thread: 1\treqs: 13\tmean time: 1.817s\t\n",
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"INFO:root:Thread: 0\treqs: 26\tmean time: 0.657s\tfast\t\n",
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"INFO:root:Thread: 2\treqs: 13\tmean time: 1.956s\t\n",
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"INFO:root:Thread: 1\treqs: 13\tmean time: 1.938s\t\n",
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"INFO:root:Thread: 0\treqs: 25\tmean time: 0.770s\tfast\t\n",
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"INFO:root:Thread: 3\treqs: 12\tmean time: 2.001s\t\n",
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"INFO:root:Thread: 2\treqs: 12\tmean time: 2.030s\t\n",
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"INFO:root:Thread: 0\treqs: 23\tmean time: 0.831s\tfast\t\n",
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"INFO:root:Thread: 1\treqs: 13\tmean time: 1.909s\t\n",
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"INFO:root:Thread: 3\treqs: 13\tmean time: 1.813s\t\n",
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"INFO:root:Thread: 2\treqs: 13\tmean time: 1.932s\t\n",
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"INFO:root:Thread: 0\treqs: 25\tmean time: 0.760s\tfast\t\n",
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"INFO:root:Thread: 1\treqs: 13\tmean time: 1.937s\t\n",
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"INFO:root:Thread: 4\treqs: 13\tmean time: 1.994s\t\n",
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"INFO:root:Thread: 3\treqs: 11\tmean time: 2.218s\t\n",
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"INFO:root:Thread: 2\treqs: 12\tmean time: 2.252s\t\n",
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"INFO:root:Thread: 0\treqs: 17\tmean time: 1.299s\tfast\t\n",
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"INFO:root:Thread: 1\treqs: 10\tmean time: 2.651s\t\n",
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"INFO:root:Thread: 4\treqs: 11\tmean time: 2.355s\t\n",
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"INFO:root:Thread: 3\treqs: 12\tmean time: 2.417s\t\n",
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"INFO:root:Thread: 2\treqs: 11\tmean time: 2.279s\t\n",
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"INFO:root:Thread: 0\treqs: 18\tmean time: 1.201s\tfast\t\n",
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"INFO:root:Thread: 1\treqs: 11\tmean time: 2.353s\t\n",
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"INFO:root:Thread: 4\treqs: 13\tmean time: 1.812s\t\n",
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"INFO:root:Thread: 3\treqs: 14\tmean time: 1.847s\t\n",
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"INFO:root:Thread: 2\treqs: 12\tmean time: 2.024s\t\n",
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"INFO:root:Thread: 0\treqs: 18\tmean time: 1.166s\tfast\t\n",
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"INFO:root:Thread: 1\treqs: 12\tmean time: 2.231s\t\n",
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"INFO:root:Thread: 4\treqs: 9\tmean time: 2.275s\t\n",
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"INFO:root:Thread: 3\treqs: 12\tmean time: 2.085s\t\n",
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"INFO:root:Thread: 2\treqs: 13\tmean time: 1.947s\t\n",
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"INFO:root:Thread: 0\treqs: 22\tmean time: 0.887s\tfast\t\n",
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"INFO:root:Thread: 1\treqs: 13\tmean time: 1.960s\t\n",
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"INFO:root:Thread: 3\treqs: 6\tmean time: 2.069s\t\n",
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"INFO:root:Thread: 2\treqs: 14\tmean time: 1.772s\t\n",
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"INFO:root:Thread: 0\treqs: 27\tmean time: 0.657s\tfast\t\n",
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"INFO:root:Thread: 2\treqs: 2\tmean time: 1.711s\t\n",
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"INFO:root:Thread: 1\treqs: 14\tmean time: 1.708s\t\n",
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"INFO:root:Thread: 0\treqs: 32\tmean time: 0.451s\tfast\t\n",
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"INFO:root:Thread: 1\treqs: 13\tmean time: 1.728s\t\n",
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"INFO:root:Thread: 0\treqs: 36\tmean time: 0.338s\tfast\t\n",
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"INFO:root:Thread: 0\treqs: 34\tmean time: 0.378s\tfast\t\n",
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"INFO:root:Thread: 0\treqs: 36\tmean time: 0.334s\tfast\t\n"
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]
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}
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],
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"source": [
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"START = time.time()\n",
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"slow_threads = 4\n",
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"\n",
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"results = [ {} for i in range(slow_threads+1)]\n",
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"\n",
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"t0 = threading.Thread(target=thread_function, args=(0, True, (5 + slow_threads*3) * UNIT))\n",
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"t0.start()\n",
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"time.sleep(2 * UNIT)\n",
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"for i in range(slow_threads):\n",
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" t = threading.Thread(target=thread_function, args=(i+1, False, (slow_threads-i) * 3 * UNIT))\n",
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" t.start()\n",
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" time.sleep(2 * UNIT)\n",
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"\n",
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"t0.join()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import scipy.stats as stats\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot():\n",
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" mu = 0\n",
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" std = 1\n",
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" for i, result in enumerate(results):\n",
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" x = [(x - START)/UNIT for x in result.keys()]\n",
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" y = result.values()\n",
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" plt.plot(x, y, label=\"t%d\"%(i,))\n",
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"\n",
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" plt.legend()\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# 4.1 - Hetzner"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"API_URL=\"http://95.217.175.98:8080\"\n",
|
||
|
|
"UNIT = 5.0 # secs"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"INFO:root:Thread: 0\treqs: 4\tmean time: 0.243s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 4\tmean time: 0.160s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 1\treqs: 2\tmean time: 1.580s\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 3\tmean time: 0.585s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 3\tmean time: 0.512s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 1\treqs: 2\tmean time: 1.289s\t\n",
|
||
|
|
"INFO:root:Thread: 2\treqs: 2\tmean time: 1.451s\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 2\tmean time: 1.323s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 1\treqs: 2\tmean time: 1.550s\t\n",
|
||
|
|
"INFO:root:Thread: 2\treqs: 1\tmean time: 2.738s\t\n",
|
||
|
|
"INFO:root:Thread: 3\treqs: 1\tmean time: 2.642s\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 2\tmean time: 1.370s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 2\treqs: 2\tmean time: 1.457s\t\n",
|
||
|
|
"INFO:root:Thread: 1\treqs: 2\tmean time: 1.997s\t\n",
|
||
|
|
"INFO:root:Thread: 3\treqs: 1\tmean time: 2.158s\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 2\tmean time: 1.973s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 4\treqs: 2\tmean time: 2.063s\t\n",
|
||
|
|
"INFO:root:Thread: 2\treqs: 1\tmean time: 2.384s\t\n",
|
||
|
|
"INFO:root:Thread: 1\treqs: 2\tmean time: 1.721s\t\n",
|
||
|
|
"INFO:root:Thread: 3\treqs: 1\tmean time: 3.673s\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 2\tmean time: 1.371s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 4\treqs: 1\tmean time: 3.717s\t\n",
|
||
|
|
"INFO:root:Thread: 2\treqs: 2\tmean time: 1.469s\t\n",
|
||
|
|
"INFO:root:Thread: 1\treqs: 1\tmean time: 3.726s\t\n",
|
||
|
|
"INFO:root:Thread: 3\treqs: 1\tmean time: 3.833s\t\n",
|
||
|
|
"INFO:root:Thread: 4\treqs: 1\tmean time: 2.686s\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 1\tmean time: 2.421s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 1\treqs: 1\tmean time: 2.063s\t\n",
|
||
|
|
"INFO:root:Thread: 2\treqs: 1\tmean time: 2.196s\t\n",
|
||
|
|
"INFO:root:Thread: 3\treqs: 2\tmean time: 1.929s\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 3\tmean time: 0.539s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 1\treqs: 2\tmean time: 1.196s\t\n",
|
||
|
|
"INFO:root:Thread: 2\treqs: 1\tmean time: 1.531s\t\n",
|
||
|
|
"INFO:root:Thread: 1\treqs: 1\tmean time: 1.502s\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 3\tmean time: 0.389s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 4\tmean time: 0.177s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 4\tmean time: 0.160s\tfast\t\n",
|
||
|
|
"INFO:root:Thread: 0\treqs: 1\tmean time: 0.254s\tfast\t\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"START = time.time()\n",
|
||
|
|
"slow_threads = 4\n",
|
||
|
|
"\n",
|
||
|
|
"results = [ {} for i in range(slow_threads+1)]\n",
|
||
|
|
"\n",
|
||
|
|
"t0 = threading.Thread(target=thread_function, args=(0, True, (5 + slow_threads*3) * UNIT))\n",
|
||
|
|
"t0.start()\n",
|
||
|
|
"time.sleep(2 * UNIT)\n",
|
||
|
|
"for i in range(slow_threads):\n",
|
||
|
|
" t = threading.Thread(target=thread_function, args=(i+1, False, (slow_threads-i) * 3 * UNIT))\n",
|
||
|
|
" t.start()\n",
|
||
|
|
" time.sleep(2 * UNIT)\n",
|
||
|
|
"\n",
|
||
|
|
"t0.join()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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"text/plain": [
|
||
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"<Figure size 432x288 with 1 Axes>"
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]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
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||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
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||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
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||
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|
"language_info": {
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||
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"codemirror_mode": {
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||
|
|
"name": "ipython",
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||
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|
"version": 3
|
||
|
|
},
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||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
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||
|
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"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
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||
|
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"pygments_lexer": "ipython3",
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||
|
|
"version": "3.8.5"
|
||
|
|
}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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||
|
|
}
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