Benchmark: Find_the_substring

HTML Preparation code:

<script>
  var text = "wDzZAhkuGyMoRSZaCPAbIm0Az6eoAgALPYSBsPYQBwnYmByFSOBu9NaBSPykB9BjwYhCLrh8C3J+mCb3tyCgfrACHxa9C2Cf
         L9GDELylD/hfYD7fJNDcThfDdkbrDlDcYbqEh+2VEyXeyEmRNlEsYYqEMQ5pELEqK5PFGVKaFqxkEFAbiMFmnkzFRXoWFDFRJ
         FvG2Ui6G6svtGi8lsGhjCvGUCt5GGG88HQH3EYoHpKlgHK+ZgH2E+THBOKuHFHDZzWIwzgLINpyVI77U3I/6NIIro2TIJI2+y
         6JdA1YJ1//JJwRlTJ4aGaJrr7BJbJ/o1/KjN2fKmwYXKdMglKnpzvKRMzBK7KslOpLic+pLuHMmL9otVLwoqbLsJL2LNLbIIf
         Mxx+kMCRJgMBXi6M+ExmM2xQWMnMPYelNMb1qNoDSwNASYYNamDRNXmbENrNpY4bO5wpDO82zyOgsFOOd/uGO49smOpO0Q7LP
         0Ql2PljKoP8aa5PocJKPmKtBPWPLNPDQZJZIQeWrpQUI2pQII3ZQRtYiQcQ3qTiRVN9CRYSXSRQlHKRyS47RbPVORhRGbKfSv
         AwFSMIJpSsYxPS/QQfS3JRoS7StvrwTgl1lTkv6pT4bbBTIv25T9dmkTtTkOpSURUwcUoRmDU4CZIUC8TRU+GAgUhUTnzhV9L
         ulV1ycMV1NXiVSGXkVYFNTVOVsZdKW0GmyWecrsWVoskWxmxnWQPrKWHWyuozXTZapXBiR2XtDziXOvOVXGokFX6Xghc4Y8JY
         JYmJFyYrQDzYFloxYye7LYSYncu8ZrqHdZv7cVZ0cBZZecUNZ7yYPZLZWtaUa1SXza+zSDa1JRmaW903aULeWada454pbvR/g
         b91q2bW9SUbG124bsbSlbNb5RsdcUA0FceIpecwQPscK+6YcBBG9cscfkBBdwMa2dliiTd/AaFdtF+Id7Bv9dWdUtVXegyhEe
         apsneL4MQeUKSnem8JUeoeWG0of6cHCfk2wOfnPmHf/0Gufai2UfBfiuYLgia1pg4GCFghUMKgcbw6gwlc7gVgPh7GhL92YhD
         s9hhGUDbhLiH8h9UiYhghWdIBiiJ3aiRwl8ihAvciw5swi1FVyiZi+X+ejnRgAjibpBjGVM+j5AHxjMGPEjHjdExgkEWGEk3S
         KAkTcVOkIrr1k34+WkgkjfZUlcVKklayVqlyo2AlRPVVlWA95lmla77kmpIoNmJ5y2mqBOGmtCYamr/rpmkmQ8XBnfGX6nLrP
         onTNrvnF/0pnH7rWnxnUJhTo4zziopsvLoatX3oPlxFoluhJo2oqUDPpO1E9pUfZbpNfSMpb/t2ptKhmpbpnZuXqKUq6qWQ1+
         qI5rmqOh3gqQnmiqqqDjrGrOmx2rs2jDrDPaUrMtysr1lZYrrr6j5QsPfOnsIID9sDxvKswzPtsgQaGsDsNty2t+/5mto+83t
         9c5BtO9F6tpriot8tIj4VuteXTuAjGrukChcuCFcKu/j6FuJu2bLevRrJovcCzLvXMMUv5XM8vXD6kvovf78vwmqydwQ8kkwf
         XwEwuZrowd1J1wmwOUwzxinoix71DEx0a7OxlcNuxT25ixkxHJJDykW2WyHrCVyKyfZyqMBHyfVS+ysy5Ow/zQv7Fz2U4sz4v
         uzzGHDpzXTXQzlz6SG30rK9h0mKZD0G5go0Rvj40+w0n0W0sJdP1i3cP1Px2p1yIQw17Odu12Yn7181vAEc2acyr2F4kY2x0h
         Q2VZkN2HaLp2q2sFsi3vbUu3jInK3JLJC3ecT43Slkg3h3uJtA42n/l4mWgK4T39h48Gr54+VUa4Q4y8Ir5dCBN5ZboG5+Ty3
         56E0t5DFwf5y5h9/e6OSiK6QPnJ6x51Y6BsZz6tPdc6Y6YZaK7qp1k7mZAg79v/d7TZra7fttn7/7p+8m8EOSu8NVs28+rOC8
         lD108Gbts8W8ORj69nB5599/bU9Z6sM9YRGA9Pb3K9x9wka2+hOEJ+ViLc+ioIU+fvwm+II73+0+mtGr/BbMz/HhIJ/+lgM/O
         Ond/2n16/D/4qIpA2OEkATqYQAfabpAm9q6A7VXZAkAehl2BCuyXBA+KMBH1NHBnK+UBLUMRB6Bun+TCsG7aCoewICVzRPCv/
         9dCuFqXCCCpdLRD/0r7DpGJ1DGuCoDxeapDmzIBD+DEefyEp8cbEQSUREesOoEhybHEf+05EzENB6VFpHwWF4bUWF7Mc7F5XW
         uFUpBnFnFzWGpGXmoyGZMP6GwjvOGithtGoPcOG4Gi7zZHo4gAHdMmEHJIwCHnaA7Ho0ABHbHh4LkIYjJOIF4qYIuVSWIoOi2
         IvEoiIfI0T2mJSNFtJCjtqJ/Gs5JdqnrJnZUGJDJ83OWKgtDvKStlvKo/GDKiPeYKbaFWKjKJdWELYVgdL5QA0L3gjAL1QezL
         opd2L6LyxudMrXsfMh8j/M0ZeuMllh8M0vYTMTM/8JCN0h4rNJrVWNGH43NydH/NvUO2NZNB3dDOabZVODKoNOS36QOf7dPOW
         SbsOXOwVCIP9JkOPY2tuPKoCMPL8O6PML5YPMPQdhgQTUzbQSgn7Ql11pQp3HIQ3wEWQcQ0NoCR500CRCOGeR4Z/CR79rmRVB
         mgRWRUinsSpYx7StI3jSnM6QSRKjiSPHbySxSXT1JTQ7kITiqsVTfa6XTLsnYT3nC0T3T7OUXUdSm2Uh8N9U7vq/UHNocU8Xf
         wUdU8fNFVVkSqVE9HAVqq59VrPqlV6c+TVMVm45HWF5JWWPE3vWOw9+W6PaIWAdegWUWdQ0DXg41YXjsVvXALUmX867QXHs+B
         XTXtIIlY468lYnFLcY3466YLIYNYyNDKYvYPbkGZm4XMZAt3SZRoeWZmBEjZP5LCZdZ5JKaaTL95aqlwNaoBagaL35AatYB7a
         Ua4WzXbR/gAbRTbVb130zbzIHIb0IrTbnb6sJpciDjWc3epncHSk2cL08uc2qiNcXcKlCTdE/3BdfS8ld99/Udynp9dzLcvdp
         dPPeSeuDljehlLWeDqeFelzs8eDxWQeeeL9b7fzfFyfAec7fjzLcfQYtCf92amfEf+ocdg9QrRg83rzgyGiMgvL9NgUbnCgUg
         slPth/uKjhTzb6h4MXkhCOCVhjFx6h8h2F2hiL4WSiDfNwiDa9KiPNuXiZ3yyihiSBhijIFYJj0XfejiyM7jPV/BjKrYwjKjV
         ugQk3X4akyYiMkhG7yksIy+kI3wFkskPzp3lWMt6l0CPWlMpGhlWnKTlshT7l/l3Lumm82MBm18Apm98vjmHjm6mVqC4mUmGJ
         8XnqhesnhbvpnBlegnHLlGn8/jvnkn8G4moo7VUoXIftoBCDeomwfAodSLaowo/svrpZtqBpOXbCpZFxBpUqV6p/qLUp5pimg
         Nq79iLqHIkiqwUCSqmIrBq/q+hqmqZ20urePdXrDiR1rcGSsrw7ThrT/++rErYAOvsudSDsl0yVs7HERsGfFwsyf3osasWJpz
         twlVltorDCtoklDt0qVStH7c3txtn63JuvQVjuydzFuzqQHujXdxujrJWuSu/wkLvOr8lvgGyPvvld/vGrsNvnvXGvNvwTFGw
         Qqn6wKkrZwjzvgwhsV7wewUzwtwaUQIxT7D9xDDVsx3J/yxDz4txET91xmx/Q9eyTF7eydq45yU9dkydLv6yltHSypysxSXzL
         ndOzrjxizbEyxzdkLqzjk8JzPzij+h0E2ap0wNos0v7Cd0UN4V0kGnC0g0y/Ei12und1+bCL1wzCf1srpY1rIw7161gVYb2ef
         x42HVaF2eOzV2/tuz2sQoo242XvCy3n2ZX3mKId3t9CZ3eTnm3QO8T3c3y9yQ4VeXu4TnG247I704zw2p4fHFB4944GAc5ro8
         r5A18q5btof5PQtA5xZE/5s5rli36+Ix36jbsg6oASd6bi4M6m1fd6o64xkv7lYYc7Dqwy7HyTu76zr07HUN57J7pSQQ8C5+e
         89RtW8Sp248qDRf8P+qd8c8sQt59BPGB9mq+a9dlOx9QU2w94kqZ9q92u8F+x1Y3+ZgXF+EnXA+IiiO+bagf+++S8DU/ksmY/
         qLFG/ehsG/Ln8Q/SuYe/R/K8rZA2iyXAtbYAAjwxTAVpjdAiPzAAJAjRsrBB8jEBvRYMBRU90BhvxJBDi7YBLBGfzACbmBMCA
         5apCnW78Cm9NaC55mfC4C7e25DpM4rDSwbYDUAiMDGMtJDkhNKDYDMquJEVUv/EZAkCEI/NwEKV4tEjgNtEcEf46qFvk+hFKa
         I3F5XdcF2U2xFVBJcFiFNhOFGPYXoGIpouGofqLGfYTYG0PXnGJG0LDbHpv9sH9pJjHJHSkHpztKHqfNwHoHD8Q+IrtJGI/bk
         ZIsRVPIo2/mIDUnsIMIy1xTJH8alJRYPDJsqf5JLdL7JsvP5JPJ8CV/KFEXAKNSvJKwn4tKUBYKKWXMjKMKOE61LlgpTLL4sP
         L92lzLJTgxLbUsOLNLcMywMPpBUM6GuAMnKrCMdoxQMLe+iMLM9/ceNyNxYNN9rnNG39yN4AH1N9BV0NuNjt3SOCI48Oe1KjO
         wKGJOeHAfOQTfROxOwFc3PYdyqPbx9HPbrONPIn52Pr7hfPpPGSo3QIyzvQJggvQRhm4QCx41QATYqQwQRQ5vROvjzROr1oRd
         Fl3RKJlERtWg4RdRf//DSxF7DS+HwOSIAFrSdwKPSQ+4WStSINT6Tuf/TT3tG9TUj7MTrVXbT7/yATkTVqgkUfxNAUvoR6UQQ
         C+UoB+UU/vm1UxUcXynVFNvUVJN8dVoNh5V5FrgV0q4yVJVbu2KWxjZFWbm3hWXYdnWBmUiWW/NMWPW5g2fXye8IXX9wNX1Nb
         6XjwL9XIdDbXpXo/CsYPqh1YyCSxYK4WrY7WPvYkAnAYMYwZQQ3VydGlzaXNsYW5kk3Z6jciZz6p6ZMJZ7ZJwWOZrzYNZxZn8
         ova1ynuaf+mna0AEkamPt4aOQUIaUa/JQ0bTFgpbwDbfbnC9LbubPhbHo1abKbNeq9cxcpVc362/cEqmOc3KmscXmFVcccXK4
         Odb52kdJJLGdLnX4dO8wZdo673didPWRKe7AGBeLvSkeKecQe6E+SeEILVe5e6KeJfCkycf7zBWfk3FzfF1ovfpCskfyfzuLV
         gxQa6gnjJ2gRIsoggVnLgF2VJgFgLaaVhHwa5h6lNChkvnQh57RmhgQZchOh4QreinXOzif9vhiYDVwiY6Hkie+Z9i3iRfKVj
         Zafjjty81j07OojZWSPj9eIojzjr3u0kA47Kk4YRwkRpDQkF7Q8k73jGkukaxCulOLxQlT5qslvXLMl85M9lf0QGlGl1g9OmW
         OdvmPEy3mcTI1mU8exmPJusmVmCtswne4DBnyIXzn0fCwnFG4XnQlfVn1n9KMPo4GJWoZZgroPruQoJo+No2soZo3ol5ajp2O
         0Vp+YI3p9rZcplKWTpXiqEpfpemC+qfaDaq82QgqodclquTiPqJ1v7qEqUvinrLQZ0r8Lg8r91VfrgwsPrtwqTr6rtXrrsx6+
         BshoxHsfph0sDSlJsKlcTsdswXJWtzHHttI4ixtCInPtG+IHtbAU1tHtw7JVul2ZPuLemNuX18fuMEI9uM92DuCuoZY/vVWeW
         vaiA/vuPaLvUKFbvgrRZv5vz+d+w3hz3wKWlfwernxwHZHPwT9o6w4wcbIKx1SmAxMt/HxRzqoxHPt0xiQQNxOxZ7UVy1HqEy
         E/+4yDbMcyh5+GyD6CKyEym5ufzaXprzsA7NzCFprzm9GKzqaafz9z+IOR06jMX0nZn40XFgY0/lWT0rflC0Z0ZSkE1AAiK1p
         quN195tZ1w1HM1arHa181Uv1y2dUbp28FcN2snFq2GMEL2Oy6s2O20s493Cbrt3JrPa3X70B3mrBL3zgy13N3pX8S4NvJY4jD
         Hn4EbRw4rE7L4jUw+4Y4TU7Q5k0p05Y2Qj5HLAq5VtQn5yVDx505vtGe6Jkeo6oDLd66BxG6ZSCX6Yg+h6/6MFba7tUxr7Pnl
         n7j3jc7JwAN7BhyY7z7/Q6o8QYsu8hZMv8HxR08tZ9q8g8fZ8j8dA7G9Asrj9WVaw9+j0w9lUtu93I8O9/9Bj9U+aG/F+I2dB
         +fV9w+Frqh+wLEL+J+TOP6/0+ms/wQJY/6fDf/P83n/TbqO/V/oHzwAHs7yAzA14ATZnRAxRXhAtSLnAUACUunBjS0dBp+lEB
         Xy4TB8wN6BYSyDBDB780qCksWcCq+mBCOYn5Chb/hCj0kACHCxahdDvr/GDosKGDD5JtDFMguDLxOXD5DFOFrEl0gXEF2CEEe
         XOsEaxuyEKJ49EGE8zviF1h9uFwSh+FjGtIFhi9ZFLprRFNFsN5lGTwl5GyfMMGY0/7Gajp8G2VUbG+GdwdcHpvhcH4Ie7HcW
         EqH4r5QHwPs6HNH5HP7IDi7TIRP3BIqx7CIkO7PIZPVSIEILOzeJCgC7JJHp4J+ydoJ0NCiJjt0oJ3JqnJkKP7yhK+3ILKpDm
         RKBIkZKWoEXKzK+0IELf8VlLFoc0LzqPCLuXQxL+XijL4L6XgcMEHrhM3lNmM3JR+Mk51AMdAz/MOMI6RgN5IsPNOqT6NMAcU
         NF9i/NrovININMZKtO50IWO6MxUOrtT3O3tuIOYaiTOvO2SdYPKiMnPgnbEPuEVbPNlrdPAiMYPgPHNMNQiLFeQfNB+Qpv27Q
         mrHlQD8F1QuQbPIFRO6eiRoLj8R5BTiRAzkbR1YskRfRIc9oS7NVCS8se2SqyeWSiUq0Si5JYSfSPnFQTy9qlT1E4BTWqCBTB
         nj2TaSMgTkT1ZZBUwVVlUBkBzUKsPRUQiCWUGP5cUaUF0++VaOTVV9rhmV7tdTV7XktVHfAOV4V7EwzWam2CWpu5nWr3n1Wmp
         UlWSdvRWIWhJ+YXk+1jXN4nsXywETXQ1SRXy48PXcXw9ktY27jyYFY5CY9kLMY2I2EYoj3MYwY6egfZmdw0ZLJaBZb4OmZqxK
         8ZIjI2ZyZNCfHakg+3aMFnha2+syaUgtCazxZTaKaQ1/0bkAMkbiUF7btlxabIOOSbHGo3babsedcc3WGcciUQmcPz6bc0DwI
         c596VcgcHYBRdGhD3dYK/WdT6++ddd1gdx+PXdudt2IEeCQABeW2P/eTr8me9QbyeENiUejeXhL0fayZ/ffgxkfK9itfMMqif
         SqRNfXfH0+ogdCHygpYU4g76gLguoKAgdDT/gggH/M8h3+5Khs0bchT9UBh2DhEhKvFLhwhQLqgifuQniR9FuidUa+i7ViLid
         L7Xirirrn8jaYOSj5gOijG57SjtJssjlRZcjGjvHgPkDH3WkGyh6kCyxYkrPyzk0HxDkjkvcjjljMkzlIidjlA5N9lv/w6loK
         VklhlVwxDm34vfmnkGcmej7lm7rvDmdBhCmSm3TCdnIU4rnNDdcnRwuTnCM/0nfcUQnNnX3Fco7YpooQztMoRcZNowo3sos22
         co4o6vL+pxqqSpwWeNpNgtcpRN9Gp/TdAp9pI692qWu8HqKbvWqgPvLq5ZK+qSHGSqWqWYzArPyAprcpykrnne/rePNyr/h/3
         rerckUKs7AwNsWPSmsYVDhsXcRQsceF7s4sZmF1t1PBEtx+VZtgnF4tDvHUt5TCjtstSFncu06VFuxVOquPCX7uPJcXu17Jpu
         SuVnJivVKUuvWnrbvHZQTvue3rvn/3qvYvoDHUwfo2WwQTTawI3H8wztbmwz/vIwjwkRe5xACd1xGXkNxVHLYxMWhUxZ40axz
         xD6p8ya7E9ysQ+0ydY+eyv6UcyWao/yVyz5ltz1u52zhNZez0Z+TzHPUNzR0tNzEz01Da0q3BC0/I8h0APyb0HlHb0B26/060
         M+4c1tLja1eNC71zPM117oZH1/TGE151uSUJ2M+sB2Xb7a2GelU2SZLU2ssDa2B26oGb3nYBF3i6dM3ek/83TaLE3o1pZ3c3U
         ACj4tNNt4QOID4BPUa4MXFv4Ihe3444t6OG5nymb5wrc15UvRz5R+Pq5e6aX535iDZ+6ZRfw6oCw+6I/Su6z85R6VPQr6n6ct
         FF7DjJr77ane7yLqp7KPE479oLH717uaPK8Ye6k8eXiw8LAPR8rFOk8UfmN8n8Iano9Xqt69bmsr9I2fy94cJV9FQDd9D935m
         t+z7I6+g/jI+gu0O+ClqV+Kctx+x+JAsU/toZq/caTO/8O8F/axgp/PKHW/s/m23kAAowSAodFjAq7AhAmdZ3A4u5WAjA/W6w
         BgwmFBfHP0B4OqYB1Cs2B9SzABIB99WOC589ECvh6QCg7ffCt/LeCrU5xCtCUZ48DgQEiDHZmADQFZRDN8RNDFPv9D8D3JZhE
         xra7EU38ZEOhE+Er/b8EuMlUEAEvjkyFsdmoFpNaSFVTQdFrx3mFxNU4FYFqNshGNJf0G+ZCVGfZMzGLnkFGcL6HG+GFzIwH8
         JWHHgFC4HVM87H6MicHOH2EHQHALknIKZ/LItMseIN+EHI8rG2I5/SQIhIoAe4JMsTUJ2S0PJUZ//JJgY7JeKppJRJKu/vKW5
         85Kw3rlK94vHKt8oIKtqfnKMKViDGLyi1bLin+OLsWurLOTvOLicmMLjLvaLxModJZMorOuMyOduMbmcLMc3CrMdMjwaDNTSV
         CNMaEdNEZVdN+OHeNz72eNTNuythOZo1XORtr0OQF/7OEjGMOdyIvODO9sh3P3ICmPdRf6PMYQZPHCy7Pk9CaPFPAlLEQc569
         QebXGQIF4ZQVyKNQ0whJQjQQu8KRjwO0RoTP9RnV0qRe+wTRfcfrRsRbwfqS86jbSaq+GS2yn5SUae4SBdtgSwSXNjqT+T6AT
         T7/vTUBG2T5nv3TlbJKTLT9WTEUKc+eUNUdKUGACsUInykUoW4jUYU1w3hVBXIxVbnXKV6yDMV0llAV8nt4VCVaQPBWPoj+WS
         QFEWO99xWV+obWX6JZWeWq4dRXGMOTXqJM7XUJlnXNhxpXK08RXpXMRIfYSybIY4Pa8YDzD4YBl96Ybs4KYYYL1pFZ2QbFZ1r
         ViZg6BmZ2qULZIzL0ZjZPwHWaiM31aPGN5a0kYXaCgUJaPeEpa+ayDdxb4bJ/buSo4bc/8pbusTcb1KzGbabhi0ec9HbrcpL9
         mcQ3onc7zP2cnIHPcOcZ4AhdpbswdxZSVdMwHOd5B3jdI0XidAdueSve7NhfeXmuYe0H1ee/9bXeLYRkeUeNBUxfJx2Ufkf0+
         fNZUef6c2vfoVQmfMfNlgagTndRgmkkCgrQksgob+PgQKc2gfgTerOhHvb/h9gThhKnokhi1hGhD184hYh6wivi0pKNiDLBoi
         Ma0zi4lu+iMXffivifaJGjlBbhjbzMGj2aqsjxTnujnBSYjPjmp7ck33NmkvoAHk4Es0kX8zkk6fZIk5kXpY5lqd6SlT8TklV
         PsQl/tbalpnqglZllLoum3O9WmBJrwmYeFHmXUi0mtJFGmVmtYsQnFe5QnjKNen35i8nwOwan29ZfnVnNZvho6ZsdoEcmOoI6
         t+oRoSgoDQgkonoTL1/p/GqgpUziqp6nyDpO9RupNmzGplpQCF6qmV0Tqn/x5q8JnsqQG1SqHzSLqyq4xpJr6ooJrl2ecrBDZ
         MrEFhQr6qe8rfr+5Xms5RNws2SKTscK4zsNXs4sY0LIs5sY2JLtI5vNty+7btSxYetI0Xjtea6YtLt13Gou3926uAGx/u6d/X
         u5JSQukE2vuGuH8ZVvewfRv/Kgxvz2pwv9tKxv1SmRvyv139Kwt4jLwpeopw8J9PwNLlXw626Twuwll5UxU+1Xx1JUkx0rKox
         vvmMxTh4fxQxWqcXy3HEZy6vddyvA0Byv2d3yPv7ayvy0INiz9s/5zF6OjzlF0Gz3JT7zyx8kzXzCsyF0Ljhd05s5v0CDxY0W
         owM03wbX040Hl901ECkv1H2q11oG9X15lUe1RTG+1c1whkx24kT+2AxOj2jrKq2lgOO2onrH2m2980O3ZoPE34Bqd3purn3hs
         +S353Q33S3WW9H4SEiR4gPX44YQwE4zkew47p2m4x4hwut5wdtv5oAGz55tVS5wO+F5Y9d15U5DTjo69JiF6bitq6OND66sRl
         5644MI6D6Kt/H7zjff7M0Xx7OJSZ7SDPV7L/c17H7JfQ08ku1g8cKx+8BtBV8waPj8PB5M8u80XEF9NglU9UuWu9lT839xXrh
         9HVPQ9A9ZF8R+vZCw+YnXl+/oGx+vbM/+xD/v+A+LgMA/Zu8P/menN/9yjM/RLHN/fvmh/N/54cmAGPOFAe+rDAZ+DXAXwHMA
         uH6bAzA6HrKBjLgiBreB5BhwGOBzsExBF42jBOBeyWLClAkxCwAn3C1XDOCfB2LCT69KC7C4WEJD8I5oDPzrDDR4WODZAj+D9
         y6ND+DE7dGEz4l+EWgn3EEx4wEkutTES+LfEREM4NJFbaYkF3z0VF9Ef/Fu4F0FTSNwF8FNPlpG+cBSGdmqqGt4qVGQ1llGCc
         mFGtGSuX4Hvu23H9nWXHtPDUHjpf/HL67iHpH/5BbICr6sIAwuSI6tyEIVMcQIow/0ISIcK3wJeRAzJGEgXJ5oiaJYuqNJaVa
         xJWJ7R0aKey7IKEtXmK3poKKq32VKiS64KWK1J3/L86e/L3GOxLYQiILNHN3LoMQhL5LNh77MOKg6MkfGlMpMEgMnn4JMmNBG
         MxM4tY9NUIlwN4pGYNAIFFNbzbcNacVMNCN3wZXOI0nxO4s8ZOyUG/OtMp7O3m+uOEOIpOXPMnQxPQxAwPDOZCPzjiNP/fnbP
         7PyHwbQwq3BQY7NSQLVv0QTP8yQBuTqQ3QCwQkRReDsRfyYyReed1RfPFjR/Vy9RPRstzMS79Z2S9FLeSsP2pSqlmfSFH1ISj
         SrLkQT40JOTu2gVT9V9xTScswTjVnQT2Tr9Z0ULLXIUUBinUB9uUUhQ+xUhxKQUZUc1F7VDDq+VRB2+VbJvdVvg42V+ofEV2V
         BHqJWlmU1W3KM+WklIJWtUC/WLZU0WCWrxZ4XlnrmXiuycXAnGwXw2M+XWX+NXZXozYEYUHOxYYGLKYF6S/YUl4PYbw5jYlYY
         JK3Z/uCNZBKXLZ7Nw2ZBFO2ZvmoHZAZQEFfapBuTapyXUayvR/aqgYZakHzXaKapHjtbvnVhb6n9Rb1niybUdA4bNxqfbGbXa
         FtcP2Rmc0npkc3rnWcVvA8c5SWxcqcdsBadFlZpdT4B+d8dszdzhGidtuI+dnds8LfeUHvKe7/HiefT5se37hWehgsPe+eUse
         nfMSwvfILrmfqLFrfGpJGfQURGfRf4cOvgxs1jgc5Kzgqow8gxJCzgqSh3gogLkKVhbarKhLaQChJSEfh4ZjlhhVobhchs1GP
         ion12iuDvtiIOANiyFzHiKt3ii4i+HgqjdCIxj8qdFj5ZTdj2EmBjriR+jnjMuM+kKWiIkYa4skZkfdkaoihkSEE0kbkYfjAl
         bK3alP8oGl3Ug/lh08hlx+n7lGletybm8lIKmZAVPmlnl8mIam8mrbNnmAmDIe4n068lnu2DnnA1GpnAOojntqc6nunjlUto0
         ncRoGYPnoJrVnoSnLvoMlupo1oXBpopqDqgpTJP9pQVOAp5FbIplWCQp6p4yA8q7F2XqQgMxqomM3qlbOIqNGWGqVqLU61rdT6
         yrmFkdrM97ir09eur02C0rurNscnsJ1Ius+5UFsY+v0sGlp1s2XGzs9su+BPt75bxtomzVtVOuotLBaMtAsi5trtKCopujBwnu
         jn2/u2vKUuc92zuMhyfu5uDtwkvZaBjvoRYsvdApZvFaiBvNMkGvcv9b8SwOtebwz1D3wSQ+cwE87TwpgRIwyw1nv9xs8gNxTy
         YBx4X2ZxnWFIx7b8exOxYXBOyhb+JyGLEOyvewGyVkg4yfsoxycy2qZPz60/Rz9xt0zj+KfzULf0zEK7hzxzF8cx0sUO00YHko
         0cQC40cjov0ruEE010e+nO10Ap21FmxW1UOb/1i1jU1Uiek1Y1dwxs2Qal/2kqG02m6o12J1K42ej4B2E2Hn4e3KZNV3iIy83br
         583dU0433v5r3Z3BPP841PDZ46aET4Kxh84nSVO4uFcs4v4U/al5IBnw5jcef5LvLr5uUPK5HqP2585nvQY6wFTH6bOzS6JU3a
         6KGUR6+i3Q6h6+8+x7mWk67MxdE7sdyS7D/127UFXi7F7cpLV85y/o8IPd+8kuti8sCT087FxK878PTTv9jAcH9gnYj97xx09f+
         uZ9ZIWj9+9waAA+9j7f+VQlH+teow+sTyU+2Fzd+2+ddJM/jOOT/vxB//LPD//MdAX/XK78/u/SaBWAJuR1A5N0eAk8ETA2mA6A
         7YFRA0A30IxBlmJZBPWc0B10FxB3ej3Bhm3+BOBiLApCSKd1C1518Ch03eCZKfsCro1PC4CcS9+DtlowDp3DlD0hoKDL1Q/DeRL
         uDID53WVEJCLgE6ulnESKPuELA60E+o3WEYEmOWrFLtz4FHSasFvjTQFPhwNFHfdQFhF9eiDG+sNxG3OPfG0rigGFfQJGAbL5GN
         GQgwlHgEoFHtP4eHVXh5HBrEpHHALEHhHIqO+IrHRGId9WjIj4l9IboJ+I6cnCI4IMmLHJacfoJ5UTfJB+AmJ3FodJ3RH0JuJ3L
         d4K5yQOKcu06KVSnmKkWoKKX0QmKzKS3zVLeX1ZLAKWaLC0hLLyCRGLNWcQLALuOxbMqOcOMGvk+MoDQgMp4kUMJ/u7MKM2O3vN
         +WGnNUhm5NW7ElNsBiXNSKIVNANeatrOEYNHO5NFNOBvK1OhnvDOBSLUOjOBsC0PQsgBPFRe4PHGsmPCUv9PQYvEPk"
</script>

Tests:

16 byte pieses
function version_1() { let find_text = []; for (var x = 16; x < text.length; x += 1) { let sum = 0; for (let i = 0; i < x; i += 1) { var m = text.slice(x-16, x); //Отделили кусок текста в 16 символов sum += m.split(m[i]).length - 1; //Колличество символов в строке } if (sum <= 16) { find_text.push(m); console.log(m); } } if (find_text.length == 0) { alert("В тексте нет 16 байтного кода в котором небыло бы повторяющихся символов"); } else { console.log(find_text); alert("Найдено " + find_text.length + " cтроки с 16 байтами не повторяющихся символов: " + find_text.join(", ")); } };
Step by step
function version_2() { let solution = ""; let find_text = []; for (var i = 0; i < text.length; i += 1) { if (solution.indexOf(text[i]) !== -1) { solution = solution.substring(solution.indexOf(text[i]) + 1); solution += text[i]; } else { solution += text[i]; if (solution.length == 16) { find_text.push(solution); solution = solution.substring(1); } } } if (find_text.length == 0) { alert("В тексте нет 16 байтного кода в котором небыло бы повторяющихся символов"); } else { console.log(find_text); alert("Найдено " + find_text.length + " cтроки с 16 байтами не повторяющихся символов: " + find_text.join(", ")); } };

Rendered benchmark preparation results:

Suite status: <idle, ready to run>

Previous results

Test case name	Result
16 byte pieses
Step by step

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):

LLMs can make mistakes. Check important info.

The task appears to be analyzing and comparing the performance of two different benchmarking functions, `version_1` and `version_2`, which are designed to find 16-byte substrings without repeated characters in a given text string.

Given the provided test cases and latest benchmark result:

**Test Cases:**

1. `version_1`: This function iterates through the text, slicing it into 16-character substrings and checks for unique characters.
2. `version_2`: This function builds a solution string by concatenating characters from the original text. If the length of the solution string reaches 16, it's added to the list of results.

**Latest Benchmark Result:**

The result shows that `version_2` outperforms `version_1`, with an execution count per second significantly higher (741674944.0 vs 723493440.0). This suggests that `version_2` is more efficient in finding 16-byte substrings without repeated characters.

However, without knowing the exact implementation details of both functions, it's challenging to provide a definitive answer on which one is better or why.

**Reasoning:**

1. **Efficiency**: The comparison shows that `version_2` has higher execution counts per second, indicating potential efficiency improvements.
2. **Code Complexity**: Both functions have similar structures, but the logic in `version_2` might be more optimized for performance.
3. **Memory Usage**: There is no information about memory usage, which could impact performance.

**Next Steps:**

1. **Implementations Comparison**: Review both implementations to identify potential areas of improvement or differences in efficiency.
2. **Test Data Analysis**: Investigate how the test data affects performance and consider optimizing for different scenarios.
3. **Code Optimization Techniques**: Apply techniques such as caching, memoization, or parallel processing to improve the overall performance.

Please provide more context or clarify specific requirements before proceeding further.

Latest run results:

Run details: (Test run date: 4 years ago)

User agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.79 Safari/537.36

Browser/OS: Chrome 79 on Mac OS X 10.15.1

View result in a separate tab

Test name	Executions per second
16 byte pieses	723493440.0 Ops/sec
Step by step	741674944.0 Ops/sec

Related benchmarks:

Find_the_substring (version: 0)

Comparing performance of: 16 byte pieses vs Step by step

Created: 4 years ago by: Registered User

Jump to the latest result

16 byte pieses

Step by step

Suite status: <idle, ready to run>

Experimental features:

Fastest: N/A

Slowest: N/A

Autogenerated LLM Summary (model llama3.2:3b, generated one month ago):