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anafora arxitektonikh-better.html
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<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1 plus MathML 2.0//EN" "http://www.w3.org/Math/DTD/mathml2/xhtml-math11-f.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><!--This file was converted to xhtml by LibreOffice - see http://cgit.freedesktop.org/libreoffice/core/tree/filter/source/xslt for the code.--><head profile="http://dublincore.org/documents/dcmi-terms/"><meta http-equiv="Content-Type" content="application/xhtml+xml; charset=utf-8"/><title xml:lang="el-GR">- no title specified</title><meta name="DCTERMS.title" content="" xml:lang="el-GR"/><meta name="DCTERMS.language" content="el-GR" scheme="DCTERMS.RFC4646"/><meta name="DCTERMS.source" content="http://xml.openoffice.org/odf2xhtml"/><meta name="DCTERMS.creator" content="xristos ziskas"/><meta name="DCTERMS.issued" content="2018-03-11T16:05:00" scheme="DCTERMS.W3CDTF"/><meta name="DCTERMS.modified" content="2019-04-01T02:41:49.781468662" scheme="DCTERMS.W3CDTF"/><meta name="DCTERMS.provenance" content="" xml:lang="el-GR"/><meta name="DCTERMS.subject" content="," xml:lang="el-GR"/><link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" hreflang="en"/><link rel="schema.DCTERMS" href="http://purl.org/dc/terms/" hreflang="en"/><link rel="schema.DCTYPE" href="http://purl.org/dc/dcmitype/" hreflang="en"/><link rel="schema.DCAM" href="http://purl.org/dc/dcam/" hreflang="en"/><style type="text/css">
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"/></div><!--Next 'div' added for floating.--><div style="position:relative; left:NaNcm;"><span class="T43">Πολυτεχνείο Κρήτης</span></div></div><div style="clear:both; line-height:0; width:0; height:0; margin:0; padding:0;"> </div><p class="P20"><span class="T44">ΤΜΉΜΑ ΗΛΕΚΤΡΟΛΌΓΩΝ ΜΗΧΑΝΙΚΏΝ ΚΑΙ ΜΗΧΑΝΙΚΏΝ ΥΠΟΛΟΓΙΣΤΏΝ</span></p><p class="P18"> </p><p class="P18"> </p><p class="P18"> </p><p class="P1"><span class="T46">Αρχιτεκτονική Παράλληλων και Κατανεμημένων Υπολογιστών (</span><span class="Strong_20_Emphasis"><span class="T46">ΗΡΥ418) </span></span></p><p class="P24"> </p><p class="P20"><span class="T41">Αναφορά </span><span class="T42">1</span><span class="T41">ης Άσκησης</span></p><p class="P18"> </p><p class="P18"> </p><p class="P20"><span class="T2"> </span></p><p class="P21"><span class="T45"> OPENMP & POSIX THREADS</span></p><p class="P19"> </p><p class="P18"> </p><p class="P18"> </p><p class="P18"> </p><p class="P18"> </p><p class="P18"> </p><p class="P18"> </p><p class="P26"> </p><p class="P26"> </p><p class="P26"> </p><p class="P27"> </p><p class="P27"> </p><p class="P27"> </p><table border="0" cellspacing="0" cellpadding="0" class="Table1"><colgroup><col width="257"/><col width="257"/></colgroup><tr class="Table11"><td style="text-align:left;width:2.3125in; " class="Table1_A1"><p class="P31"><span class="T3">Ον/μο</span></p></td><td style="text-align:left;width:2.3118in; " class="Table1_A1"><p class="P31"><span class="T3">Α.Μ</span></p></td></tr><tr class="Table12"><td style="text-align:left;width:2.3125in; " class="Table1_A1"><p class="P31"><span class="T3">Ζησκας Χρήστος</span></p></td><td style="text-align:left;width:2.3118in; " class="Table1_A1"><p class="P31"><span class="T3">2014030191</span></p></td></tr><tr class="Table11"><td style="text-align:left;width:2.3125in; " class="Table1_A1"><p class="P31"><span class="T4">Μποκαλίδης Αναστασιος</span></p></td><td style="text-align:left;width:2.3118in; " class="Table1_A1"><p class="P31"><span class="T3">2014030069</span></p></td></tr></table><p class="P22"> </p><p class="P22"><span class="T23">IMPLEMENTATIONS</span></p><p class="P22"><span class="T5">SERIAL</span></p><p class="P22"><span class="T24">Το πρόγραμμα λειτουργεί για συγκεκριμένο αριθμό ορισμάτων από την main . Αριθμός ορισμάτων πέρα από το προκαθορισμένο οδηγεί σε αποτυχία εκτέλεσης. Τα ορίσματα αναγνωρίζονται ανεξαρτήτου θέσ</span><span class="T25">η</span><span class="T24">ς καταχώρησης. Αρχικοποιούνται οι δείκτες απόδοσης και οι χρόνοι.</span></p><p class="P22"><span class="T8">Οι δείκτες των string αρχικοποιούνται δυναμικά στην main και λαμβάνονται ως παράμετροι στις συναρτήσεις. </span><span class="T24">O πίνακας με τα score δημιουργείται δυναμικά στη main και το μέγεθος που δεσμεύεται είναι ανάλογο του γινομένου (m+1)*(n+1) .</span></p><p class="P22"><span class="T24">όπου </span></p><p class="P22"><span class="T24">m,n : ο συνολικός αριθμός των χαρακτήρων που λαμβάνει κάθε string μετά την ανάγνωση του.</span></p><p class="P4"> </p><p class="P22"><span class="T8">Οι συναρτήσεις που διαβάζουν από το dataset διευθύνουν τους pointer των string ώστε να δείχνουν στο ζητούμενο set χαρακτήρων. Η ανάγνωση του κάθε χαρακτήρα από το dataset γίνεται σειριακά απορρίπτοντας χαρακτήρες newline tab και γενικότερα μη αποδεκτούς χαρακτήρες για τη ζητούμενη άσκηση. Η τακτική ανάγνωση είναι αποτελεσματική για οποιοδήποτε τύπο αρχείου dataset</span></p><p class="P22"><span class="T8">Η δυναμική δέσμευση μνήμης γίνεται τόσο όσο υποδεικνύουν τα size του αρχείου. Τα malloc γίνονται για να είναι εύκολα διαχειρίσιμη η μνήμη αλλά και διότι σε ορισμένα αρχείο το μέγεθος των strings είναι τόσο μεγάλο ώστε να υπερφορτώνεται η στοίβα. Θα μπορούσε επιπλέον να χρησιμοποιηθεί η εντολή setrlimit() για να λυθεί εξίσου το πρόβλημα. Πιο αποδοτική η λύση της δυναμικής κατάτμησης.</span></p><p class="P22"><span class="T8">Τα στοιχεία της απόδοσης του προγράμματος παρουσιάζονται στο τερματικό κατά το πέρας του προγράμματος. Χρονομετρείται κάθε κομμάτι που αφορά τα στοιχεία πριν και μετά το τέλος </span><span class="T9">κάθε βήματος</span><span class="T8"> προς απόδοση – (execution ,calculation ,traceback κλπ) .</span></p><p class="P22"><span class="T8">H υλοποίηση ενδείκνυται για οποιοδήποτε αρχείο dataset</span></p><p class="P11"> </p><p class="P11"> </p><p class="P11"> </p><p class="P11"> </p><p class="P25"><span class="T15">Functions:</span></p><p class="P22"><span class="T16">terminal1,terminal2:</span><span class="T12"> Εύρεση της παραμέτρου που έχει εισαχθεί από το terminal ώστε να ληφθεί η μεταβλητή που θα χρησιμοποιηθεί για την υλοποίηση του πίνακα score και των περαιτέρω διαδικασιών που αφορούν το αρχείο εξόδου. Η αντιστοίχιση επιτυγχάνεται με την ταυτοποίηση αλφαριθμητικών. Η terminal1 εξάγει τις αριθμητικές μεταβλητές (match,mismatch,gap). </span></p><p class="P22"><span class="T12">Η terminal2 εξάγει τις αλφαριθμητικές μεταβλητές(path,id). </span></p><p class="P22"><span class="T12">Για εσφαλμένο αριθμό ορισμάτων η για εσφαλμένα στοιχεία ,το πρόγραμμα τερματίζει.</span></p><p class="P22"><span class="T12">Δέχεται ως ορίσματα τις μεταβλητές τις main. Αναγνωρίζουν τα στοιχεία ανεξάρτητα τις θέσεις που λαμβάνονται από την main</span></p><p class="P14"> </p><p class="P22"><span class="T17">ReadVariables:</span><span class="T8"> Διαβάζει τα στοιχεία που αφορούν τα μεγέθη των string που θα μελετηθούν στο αρχείο(pair size, Qmin, Qmax,Dmax). Δέχεται ως ορίσματα τους δείκτες των μεταβλητών που τα απαιτούμενα μεγέθη των string. Καλείται μια φορά κατά την εκτέλεση του προγράμματος για να διαβάσει τις μεταβλητές του dataset</span></p><p class="P14"> </p><p class="P22"><span class="T17">readQ,readD:</span><span class="T8"> Χρησιμοποιείται κάθε συνάρτηση για ανάγνωση του stirng Q και D αντίστοιχα. Οι συναρτήσεις λειτουργούν ως εξής:</span></p><p class="P22"><span class="T8">Loop ώστε να αναγνωσθεί το σύνολο των χαρακτήρων που υποδεικνύει το Qmax σειριακά. Χαρακτήρες newline και tab αγνοούνται χωρίς να λαμβάνονται υπόψη από το δείκτη που χρησιμοποιείται ως διάβασμα. </span></p><p class="P22"><span class="T8">Ο δείκτης αυξάνεται μόνο όταν θεωρεί αποδεκτό χαρακτήρα (αριθμητικό ,αλφαριθμητικό). Δέχεται ως ορίσματα δείκτη Q και D αντίστοιχα για τις συναρτήσεις, καθώς και τον αριθμό των χαρακτήρων που προτείνει το αρχείο για διάβασμα.</span></p><p class="P14"> </p><p class="P22"><span class="T17">calculation:</span><span class="T8"> Δέχεται ως ορίσματα τους δείκτες των string αλλά και το δείκτη για το score καθώς και τις θέσεις των string προς επεξεργασία. .Ο δείκτης για το score αρχικοποιείται στη main και τα κελιά του, τροποποιούνται στη συνάρτηση υπολογισμού. Η επεξεργασία των κελιών θα γίνει ως εξης : Κάθε χαρακτήρας του string Q </span><span class="T9">αναλύεται</span><span class="T8"> με κάθε χαρακτήρα του string D . Ο πίνακας score γεμίζει σύμφωνα με τον αλγόριθμο Smith-waterman.</span></p><p class="P3"><span class="T19">Ο αριθμός των κλήσεων της συνάρτησης ορίζεται από το γινόμενο των στοιχείων</span><span class="T20">(</span><span class="T19">Δυο for </span><span class="T20">iterations</span><span class="T19"> , το ένα </span><span class="Emphasis"><span class="T21">εμφωλευμέν</span></span><span class="Emphasis"><span class="T22">ο</span></span><span class="T19"> στο άλλο</span><span class="T20">). </span><span class="T19">Η τιμές των κελιών της πρώτης γραμμής και της πρώτης στήλης είναι αρχικοποιημένες με μηδενικά. Οπότε ο υπολογισμός ξεκινάει </span><span class="T20">από</span><span class="T19"> το κελί της πρώτης γραμμής και της πρώτης στήλης και συνεχίζει στα επόμενα . </span><span class="T20">Γ</span><span class="T19">ια τον υπολογισμό χρησιμοποιούνται τα άνω γειτονικά κελιά . Σε κάθε κλήση πραγματοποιείται έλεγχος για αναγνώριση της μέγιστης τιμή που υπάρχει στον πίνακα . Αν η τιμή ,που εξάγεται από τ</span><span class="T20">ο</span><span class="T19"> </span><span class="T20">αποτέλεσμα του υπολογισμού, </span><span class="T19">είναι μεγαλύτερη από την μέγιστη τιμή που έχει διατηρηθεί από προηγούμενες κλήσ</span><span class="T20">ει</span><span class="T19">ς , τότε η προσφάτως καταχωρημένη τιμή γίνεται η μέγιστη τιμή που διατηρεί ο πίνακας.</span></p><p class="P22"><span class="T8">Με το πέρας της διαδικασίας του υπολογισμό του score </span><span class="T9">συνεχίζει η διαδικασία του traceback</span><span class="T8">. Σαρώνονται τα στοιχεία του πίνακα και για τα κελιά που ισούνται με την μέγιστη τιμή καλείται η συνάρτηση οπισθοδρόμησης</span></p><p class="P15"> </p><p class="P22"><span class="T11">Traceback</span><span class="T8">: Όμοια διαδικασία με τον υπολογισμό του score ως προς την διαχείριση των κελιών . Για το εξεταζόμενο κελί , αναγνωρίζονται οι χαρακτήρες που αντιστοιχούν στα string . Αν είναι όμοια ακολουθείται το διαγώνιο κελί</span></p><p class="P22"><span class="T8">Αλλιώς η διαδρομή συνεχίζει ελέγχοντας το κελί με τη μέγιστη τιμή . Για ισότητα μεταξύ των κελιών </span><span class="T9">αλλά για διαφορετικούς χαρακτήρες</span><span class="T8"> ακολουθείται το κελί που βρίσκεται στην κατακόρυφο. Η διαδικασία συνεχίζει μέχρι ο αλγόριθμος να καταλήξει σε κελί με μηδενική τιμή. Σε αυτό το σημείο γίνεται και η εγγραφή των στοιχείων που αφορούν το ζευγάρι – Match, Start,Stop καθώς και τα string που προέκυψαν ως αποτέλεσμα του traceback- </span><span class="T9">στο αρχείο.</span></p><p class="P22"><span class="T8">Το μήκος των string είναι ίδιο και για τα δυο string χαρακτήρων.</span></p><p class="P15"> </p><p class="P15"> </p><p class="P14"> </p><p class="P14"> </p><p class="P14"> </p><p class="P14"> </p><p class="P14"> </p><p class="P14"> </p><p class="P16"> </p><p class="P22"><span class="T5">OPEN MP</span></p><p class="P2"><span class="T33"> Η λογική που έχει τηρηθεί αφορά την βελτίωση της απόδοσης που σχετίζεται με το σειριακό πρόγραμμα . Για την υλοποίηση σε παραλληλία OpenMP λαμβάνεται υπόψη η έννοια του parallelism. Parallelism αφορά την πραγματοποίηση ενεργών tasks την ίδια στιγμή </span><span class="T40">.</span><span class="T33">Το σειριακό πρόγραμμα τροποποιείται για το σκοπό αυτό. Η κατασκευή τους υλοποιείται με directive #pragma omp parallel για multiple threads . Απαιτείται ως επιπλέον είσοδος και ο αριθμός των threads. Στον σειριακό κώδικα προστίθενται τα κατάλληλα directives ώστε να μετατραπεί το κομμάτι υπολογισμού του score matrix σε παράλληλο. Έχει προηγηθεί </span><span class="Emphasis"><span class="T37">παραλληλοποίηση</span></span><span class="T33"> και του traceback αυτού του τμήματος αλλά παρατηρήθηκε ότι η απόδοση του δεν βελτιώνεται σε σταθερή συχνότητα . Επιπλέον ένα μέρος του αρχείου γράφεται με στην συνθήκη του βήματος traceback ώστε να εξοικονομείται ανεκμετάλλευτος χρόνος. </span></p><p class="P2"><span class="T33">Οπότε δεν ενδείκνυται η </span><span class="Emphasis"><span class="T37">παραλληλοποίηση</span></span><span class="T33"> του κομματιού αυτού.</span></p><p class="P22"><span class="T24"/></p><p class="P22"><span class="T24">Και στις δυο υλοποιήσεις χρησιμοποιείται το #pragma omp parallel για την εκτέλεση των thread και εκτελούνται μόνο μέσα σε αυτό το κομμάτι του κώδικα. Εκτός των bracket διατηρείται μόνο το master thread.</span></p><p class="P37"><span class="T26">Οι </span><span class="T28">g</span><span class="T26">lobal μεταβλητές και οι μεταβλητές της main μοιράζονται μεταξύ των threads και βρίσκονται στο heap. </span><span class="T28">O</span><span class="T24">ι μεταβλητές που δημιουργούνται στο παράλληλο region αποθηκεύονται στη στοίβα και είναι private. Η στοίβα είναι private</span></p><p class="P22"><span class="T29">Directive</span><span class="T24"> που έχουν χρησιμοποιηθεί:</span></p><p class="P22"><span class="T24">#pragma omp parallel shared() num_threads()</span></p><p class="P22"><span class="T24">#pragma omp barrier</span></p><p class="P22"><span class="T24">#pragma omp for nowait</span></p><p class="P22"><span class="T24">#pragma omp sections</span></p><p class="P22"><span class="T24">1) Δημιουργείται parallel region που θα εκτελεστεί από τον αριθμό των threads που έχουν ζητηθεί από τον compiler</span></p><p class="P22"><span class="T24">2)Αποτελεί φράγμα εκτέλεσης στον κώδικα καθώς τα threads αναμένουν σε αυτό το σημείο μέχρι να φτάσουν όλα τα threads που εκτελούν τον κώδικα.</span></p><p class="P22"><span class="T24">3)Χρησιμοποιείται το directive #pragma omp for για να γίνει share το task μεταξύ των threads. Έτσι οι επαναλήψεις γίνονται split μεταξύ των threads.</span></p><p class="P22"><span class="T24">4) Χρησιμοποιείται </span><span class="T30">nowait </span><span class="T31">όταν</span><span class="T26"> δεν υπάρχει λόγος να συγχρονιστούν τα threads στο τέλος των loops.</span></p><p class="P22"><span class="T26">5) Χρησιμοποιείται section . Δημιουργείται τμήμα κώδικα το οποίο θα γίνει από ένα thread και κάθε thread θα κάνει διαφορετικό section . Είναι αδιάφορο πιο thread α υλοποιήσει την δουλεία .για τον διαχωρισμό των loop στα threads</span></p><p class="P22"><span class="T24"/></p><p class="P39"> </p><p class="P22"><span class="T32">Fine Grane</span></p><p class="P3"><span class="T33">Χρησιμοποιείται ως shared οι pointers των string Q,D,scoreTable. </span><span class="T35">Για κάθε loop τα thread θα διαχειρίζονται από κοινού τα string καθώς και τον πίνακα του score και στο scope τους βρίσκονται οι μεταβλητές για match,missmatch,gap.</span><span class="T33"> Το parallel region θα εκτελεστεί από αριθμό threads </span><span class="T34">0 μέχρι num_threads που έχει ζητηθεί απο τον compiler</span><span class="T33"> .</span><span class="T35"> Η maxValue είναι global μεταβλητή για αυτό είναι </span><span class="Emphasis"><span class="T38">διαχειρίσιμη</span></span><span class="T35"> από κάθε thread. </span><span class="T33">Ξεκινάει το παράλληλο region πάνω στο υπολογισμό του scoring matrix </span><span class="T34">για την υλοποίηση με μέγεθος task ενός κελιού. Τ</span><span class="T36">ο directive </span><span class="T39">#pragma omp parallel διατηρεί για κάθε thread στη stack μεταβλητη που διαχειρίζεται τη θέση στο string Q </span><span class="T33">. Υλοποιείται for μέχρι το πλήθος του Qc. Χρησιμοποιείται το barrier έτσι ώστε τα threads να φτάσουν στο σημείο συγχρονισμένα </span><span class="T34">και για τον</span><span class="T35"> υπολογισμό των κελιών να έχουν προηγηθεί οι υπολογισμοί κελιών της ίδια γραμμής που είναι χρήσιμα για τα επόμενα </span><span class="T36">κελιά.</span><span class="T35"> </span><span class="T33"> Χρησιμοποιείται #pragma omp for nowait ώστε καθε thread να εκτελεί την συνάρτηση calculation για τον υπολογισμό του κελιού που αφορά το threads </span><span class="T34">και να μην περιμένουν συγχρονισμό.</span><span class="T33"> Όταν τελειώσουν οι υπολογισμοί του παράλληλου προγράμματος διατηρείται μόνο το master thread το οποίο συνεχίζει την εκτέλεση της διαδικασίας του traceback. </span><span class="T35">Το καθένα παίρνει περίπου γραμμές*στήλες προς threads επαναλήψεις. Η συγκεκριμένη υλοποίηση πάσχει </span><span class="T36">απο </span><span class="T35">load imbalance λόγο του </span><span class="T36">ότι μπορεί να υπάρχουν strings που διαφέρουν αισθητά τα μήκη τους.</span></p><p class="P33"> </p><p class="P34"> </p><p class="P22"><span class="T32">Couarse Grane</span></p><p class="P22"><span class="T27">Αποτελεί την</span><span class="T26"> υλοποίηση με μέγεθος task ένα ζεύγος </span><span class="T27">χαρακτήρων. </span><span class="T24">Χρησιμοποιείται παραλληλοποίηση του σειριακό </span><span class="T25">κώδικα</span><span class="T24"> σύμφωνα τον αριθμό των ζευγαριών που υπάρχουν στο αρχείο. Χρησιμοποιείται το section ώστε κάθε ένα απο τα threads να χωρίσουν το loop σε τμήματα και κάθε thread να υλοποιήσει το </span><span class="T25">δικό</span><span class="T24"> του τμήμα </span><span class="T25">από</span><span class="T24"> τα ζευγάρια. Τα τμήματα του calculation του score και του traceback </span><span class="T25">συνδυάζονται</span><span class="T24"> σ</span><span class="T25">ε νέα</span><span class="T24"> συνάρτηση η οποία επιδέχεται παραλληλοποίηση. Κάθε thread αποθηκεύει </span><span class="T24">στη στοίβα μεταβλητή που αφορά ποιο ζευγάρι θα εκτελέσει. Όταν τελειώσουν οι υπολογισμοί του παράλληλου προγράμματος διατηρείται μόνο το master thread το οποίο συνεχίζει την εκτέλεση της διαδικασίας του traceback. </span><span class="T25">Δημιουργείται load balance καθώς αν το φορτίο δεν κατανέμεται εξίσου στον αριθμό των threads, το thread που θα εκτελεστεί τελευταίο θα αναλάβει το μικρότερο φόρτο σε σύγκριση με τα υπόλοιπα που θα έχουν ομοιόμορφη κατανομή του task</span></p><p class="P28"> </p><p class="P5"> </p><p class="P11"> </p><p class="P12"> </p><p class="P22"><span class="T6">PTHREAD</span></p><p class="P23"><span class="T24">Για τις υλοποιήσεις σύμφωνα με το σχέδιο των Pthreads χρειάζεται ως επιπλέον </span><span class="T25">είσοδος ο αριθμός των threads, </span></p><p class="P7">Η παραλληλοποίηση το πρόγραμμά γίνεται ως εξής:</p><p class="P6">1).Δημιουργείται ένας συγκεκριμένος αριθμός από threads, και το φόρτο γίνεται divide του σε κάθε νήματος ανάλογα με τον αριθμό threads που παράγονται.</p><p class="P6">2)Αναθέτεται το task ως προς τον αριθμό των thread</p><p class="P6">3)Το νήμα της Main αδρανοποιείται σε ένα condition variable, έτσι ώστε να μην</p><p class="P6">προχωρήσει αναμένοντας κάποιο awake.</p><p class="P6">4)Τα νήματα τελειώνουν το task και γίνονται awake στη main, η οποία θα ενώσει τα νήματα και θα δημιουργήσει νέα.</p><p class="P6">5)Tα task <span class="T47">ολοκληρώνονται</span> στη main, θα τυπώνονται οι χρόνοι και οι αποδόσεις της υλοποίησης.</p><p class="P7">Για την υλοποίηση των pthread έχει τροποποιηθεί το serial πρόγραμμα ώστε να ανταποκρίνεται στη δημιουργία των thread. Ουσιαστική όλο το process αποδίδεται από συνάρτηση.</p><p class="P22"> </p><p class="P22"><span class="T18">Fine Grained</span></p><p class="P22"><span class="T25">Το</span><span class="T24"> task αφορά κελί. Στο for loop το master thread στέλνει με την pthread_create() τα νέα νήματα στη συνάρτηση που γίνονται οι υπολογισμοί. Στη συνέχεια πάει και το αρχικό thread στη συνάρτηση </span><span class="T25">για την εκτέλεση</span><span class="T24"> και αφού τελειώσει περιμένει τα υπόλοιπα με την pthread_join(). Έχει χωριστεί το φορτίο που λαμβάνει το κάθε thread σε κάθε loop iteration και για αυτό το λόγο έχει γίνει και τροποποίηση στη </span><span class="T24">συνάρτηση του υπολογισμού . </span><span class="T25">Στ</span><span class="T24">ην υλοποίηση με μέγεθος task ένα χαρακτήρα, το κάθε thread δεν υπολογίζει ποιες συγκρίσεις θα κάνει. Θα πάρει μέρος στον υπολογισμό όλων των κελιών, ξεκινώντας από το ψηφίο στην θέση </span><span class="T25">που αντιστοιχεί το threads </span><span class="T24">με βήμα </span><span class="T25">ίσο με </span><span class="T24">το πλήθος των νημάτων. Τα κελιά υπολογίζονται από παραπάνω από ένα thread και οι πράξεις γίνονται ατομικά με αποτέλεσμα να αυξηθεί η επικοινωνία. Δημιουργείται load imbalance αν ο αριθμός των ψηφίων δεν διαιρείται απόλυτα με τον αριθμό των νημάτων . </span><span class="T25">Σε εκείνη την περίπτωση το τελευταίο thread θα αναλάβει φόρτο ίσο με το υπόλοιπο της δίαίρεσης.</span></p><p class="P29"> </p><p class="P22"><span class="T18">Couarse Grained</span></p><p class="P37"><span class="T14">Ακολουθείται όμοια τακτική με την τακτική του coarse grain αλλα για διαφορετικό μέγεθος task. </span><span class="T13">Για την υλοποίηση με μέγεθος task ένα ζευγάρι, </span><span class="T14">στο κάθε νήμα κατανέμεται ολόκληρη η διαδικασία της του calculation και traceback για κάποιο ζευγάρι.</span><span class="T13"> </span><span class="T14"> Του . </span><span class="T13">Tα tasks μοιράζονται διαιρώντας το πλήθος τους με τον αριθμό των νημάτων και στρογγυλοποιώντας προς τα πάνω. Επειδή το μέγεθος του task είναι μεγάλο, δεν είναι βέλτιστο να πάρει αυτά που περισσεύουν το τελευταίο νήμα. </span><span class="T14">Υπάρχει μεγάλη πιθανότητα load balance διότι αν το φορτίο δεν κατανέμεται ομοιόμορφα , το thread που θα εκτελεστεί τελευταίο θα έχει επεξεργασθεί τα λιγότερα pairs</span><span class="T13"> . </span></p><p class="P17"> </p><p class="P13"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P17"> </p><p class="P22"><span class="T7">Αξιολόγηση αποτελεσμάτων</span></p><p class="P22"><span class="T8">Η αξιολόγηση του σειριακού προγράμματος αποτυπώνεται στο γράφημα που αφορά τον χρόνο εκτέλεσης στα διάφορα set δεδομένων. Η αύξηση του χρόνο εκτέλεσης οφείλεται στην ανάλογη αύξηση των ζευγαριών των string καθώς και στο μέγεθος τους. </span></p><p class="P22"><span class="T10">To</span><span class="T8"> Calculation time αποτυπώνει την χρονική διάρκεια εκτέλεσης του προγράμματος. </span></p><p class="P22"><span class="T8">Είναι μεγάλο εξαιτίας του φόρτου των δεδομένων </span><span class="T10">ζευγαριών</span><span class="T8"> . Ο συνολικός χρόνος του κάθε dataset εξαρτάται απο: StringQ characters(i)* String D characters(i)* pairNum</span></p><p class="P22"><span class="T8">όπου i το </span><span class="T9">ι οστό</span><span class="T8"> ζευγάρι</span></p><p class="P15"> </p><p class="P22"><span class="T8">το οποίο είναι τεράστιο αποτέλεσμα για τα συγκεκριμένα set </span></p><p class="P22"><span class="T8">Το Traceback time είναι μικρότερο χρονικά από το calculation καθώς traceback υφίστανται τα κελιά με τιμή ίση με τη μέγιστη τιμή του κελιού του πίνακα. Συγκριτικά </span><span class="T10">πολύ</span><span class="T8"> μικρότερος αριθμός κελιών που επιδίδονται στη διαδικασία του trace σε αντίθεση με το calculation. </span></p><p class="P22"><span class="T8">Στο διάγραμμα επιβεβαιώνεται και η ιδέα της μη παραλληλοποίηση του κώδικα traceback καθώς αποτελεί μικρο ποσοστό φόρτου της συνολικής διεργασίας.</span></p><p class="P15"> </p><!--Next 'div' was a 'text:p'.--><div class="P22"><!--Next '
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<p> </p>
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<img style="height:8,999000000000001cm;width:16cm;" alt="" src="data:image/*;base64,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"/></span></div><div style="clear:both; line-height:0; width:0; height:0; margin:0; padding:0;"> </div><p class="P22"><span class="T26">Για την αξιολόγηση αποτελεσμάτων λαμβάνεται η εξίσωση του speedup ως εξής</span></p><p class="P11"> </p><!--Next 'div' was a 'text:p'.--><div class="P22"><!--Next '
span' is a draw:frame.
--><span id="Object2"> <math xmlns="http://www.w3.org/1998/Math/MathML" display="block">
<mrow>
<msub>
<mi mathvariant="italic">SpeedUp</mi>
<mi mathvariant="italic">exec</mi>
</msub>
<mrow>
<msub>
<mtext/>
<mi mathvariant="italic">time</mi>
</msub>
<mo stretchy="false">=</mo>
<mfrac>
<msub>
<mi mathvariant="italic">ExecutionTime</mi>
<mi mathvariant="italic">serial</mi>
</msub>
<msub>
<mi mathvariant="italic">ExecutionTime</mi>
<mi mathvariant="italic">parallell</mi>
</msub>
</mfrac>
</mrow>
</mrow>
</math> </span></div><div style="clear:both; line-height:0; width:0; height:0; margin:0; padding:0;"> </div><!--Next 'div' was a 'text:p'.--><div class="P8"><!--Next '
div' is a draw:frame.
--><div style="height:3.1346in;width:5.7681in; padding:0; float:left; position:relative; left:0cm; top:-0,143cm; " class="fr2" id="Image1"><img style="height:7,9619cm;width:14,651cm;" alt="" 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style="clear:both; line-height:0; width:0; height:0; margin:0; padding:0;"> </div><p class="P22"><span class="T26">Παρατήρηση ότι λαμβάνοντας υπόψη τις εκτελέσεις των προγραμμάτων, οι παράλληλες υλοποιήσεις για το διαφορετικό αριθμό thread έχουν βελτιωμένο χρόνο εκτέλεσης. Για τη fine grained υλοποίηση του OMP επισημαίνεται η αυξανόμενη βελτίωση του χρόνο</span><span class="T27">υ</span><span class="T26"> και </span><span class="T27">η</span><span class="T26"> ανοδική βελτίωση του δείκτη speedup. Αυτό οφείλεται στην παραλληλοποίηση του task μεταξύ των thread. Όσο αυξάνεται ο αριθμός των thread υπάρχει βελτιωμένη έκδοση του προγράμματος. Για αριθμό threads=8 έχει σημειωθεί ότι η βελτίωση είναι μηδαμινή και θεωρείται το border της βελτίωσης.</span></p><p class="P22"><span class="T26">Για </span><span class="T27">τις</span><span class="T26"> </span><span class="T28">υλοποιήσεις</span><span class="T26"> του POSIX</span><span class="T27"> παρατηρείται σταθερή βελτίωση σε σχέση με το σειριακό η οποία δεν βελτιώνεται για τον αριθμό των threads λόγω της ανάγκης επικοινωνίας μεταξύ τους. </span></p><p class="P37"><span class="T26">Επίσης για 1 thread οι παράλληλοι αλγόριθμοι είναι αργοί εξίσου με το serial επειδή πληρώνουν το overhead για την δημιουργία των νημάτων κλπ χωρίς να μπορούν να εκμεταλλευτούν την παραλληλία. </span><span class="T27">Γενικά τα αποτελέσματα είναι τα αναμενόμενα.</span></p><p class="P8"/><p class="P8"> </p><p class="P8"> </p><p class="P22"><span class="T27">Όσο</span><span class="T26"> αφορά τον δείκτη Cell Updates Per Second έχει ορισθεί ακόλουθο δυο ποσοτήτων , του execution time και του calculation time</span></p><p class="P30"><span class="T26">ως</span></p><!--Next 'div' was a 'text:p'.--><div class="P22"><!--Next '
span' is a draw:frame.
--><span id="Object3"> <math xmlns="http://www.w3.org/1998/Math/MathML" display="block">
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<mi mathvariant="italic">time</mi>
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<mi mathvariant="italic">Cells</mi>
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</math> </span></div><div style="clear:both; line-height:0; width:0; height:0; margin:0; padding:0;"> </div><!--Next 'div' was a 'text:p'.--><div class="P22"><!--Next '
span' is a draw:frame.
--><span id="Object4"> <math xmlns="http://www.w3.org/1998/Math/MathML" display="block">
<mrow>
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<mi mathvariant="italic">CUPS</mi>
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<mi mathvariant="italic">exec</mi>
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<msub>
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<mi mathvariant="italic">time</mi>
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</math> </span></div><div style="clear:both; line-height:0; width:0; height:0; margin:0; padding:0;"> </div><p class="P9">Καθώς ο όγκος το δεδομένων μεγαλώνει από αριθμό σε αριθμό dataset αναμένεται ο αριθμός των <span class="T47">κελιών</span> να αυξάνεται από αρχείο σε αρχείο. Ταυτόχρονα η αύξηση του execution time και του calculation <span class="T47">αυξάνεται αναλόγως και επομένως διατηρεί σε σταθερά πλαίσια τον δείκτη CUPS για τους δυο χρόνους. Λαμβάνοντας υπόψη ότι το execution time είναι μεγαλύτερο του calculation time για τις διάφορες υλοποιήσεις ο δείκτης CUPS για το execution διατηρεί σταθερή διαφορά απέναντι στο CUPS για το calculation. Αυτό αποτυπώνεται και στο serial program.</span></p><!--Next 'div' was a 'text:p'.--><div class="P10"><!--Next 'div' is emulating the top height of a draw:frame.--><!--Next '
div' is a draw:frame.
--><div style="height:3.5429in;width:6.2992in; padding:0; float:left; position:relative; left:0cm; " class="fr6" id="Object5">
LibreOffice/6.0.7.3$Linux_X86_64 LibreOffice_project/00m0$Build-3
<p>CUPS-Serial</p>
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<p> </p>
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'div' added for floating.--><div style="position:relative; left:0cm;">Η διαφορά του δείκτη CUPScalc ως προς το δείκτη CUPSexec επιβεβαιώνεται.</div></div><div style="clear:both; line-height:0; width:0; height:0; margin:0; padding:0;"> </div><p class="P35">Αναλύοντας το CUPS για την υλοποίηση του OMP</p><!--Next 'div' was a 'text:p'.--><div class="P41"><!--Next 'div' is emulating the top height of a draw:frame.--><!--Next '
div' is a draw:frame.
--><div style="height:3.2437in;width:5.7681in; padding:0; float:left; position:relative; left:NaNcm; " class="fr3" id="Image2"><img style="height:8,239000000000001cm;width:14,651cm;" alt="" 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"/></div><!--Next 'div' added for floating.--><div style="position:relative; left:NaNcm;"><span class="T26">Το συμπέρασμα που εξάγεται απο το γράφημα επιβεβαιώνει την αρχική ιδέα . Οι δείκτες CUPS για το calculation υπερισχύουν του execution καθώς ακόμη και με τη βελτίωση των χρόνων ,το execution θα είναι πάντα μεγαλύτερο σε σχέση με το χρόνο του calculation . Οπότε το CUPScal θα συνεχίζει να είναι μεγαλύτερο.</span></div></div><div style="clear:both; line-height:0; width:0; height:0; margin:0; padding:0;"> </div><p class="P41"><span class="T26">Όμοια συμπεράσματα αποδεικνύονται και για τις υπόλοιπες υλοποιήσεις</span></p><p class="P41"><span class="T26">(βλέπε γραφήματα excel).</span></p><p class="P38"><span class="T26"/></p><p class="P36"><span class="T26">Γενικά από τα γραφήματα συμπεραίνεται ότι οι παράλληλοι αλγόριθμοι γίνονται πιο αποτελεσματικοί όσο ο όγκος των δεδομένων μεγαλώνει. Επίσης η απόδοση τους εξαρτάται από τη δομή των δεδομένων. Σύμφωνα με τις μετρήσεις μας, την καλύτερη επίδοση έχουν οι αλγόριθμοι για fine granularity, ανεξαρτήτως αν είναι υλοποιημένοι με OpenMP ή Pthreads. Έχουν το καλύτερο computation to communication ratio καθώς η μόνη επικοινωνία που χρειάζεται είναι η διαχείριση των θέσεων που λαμβάνουν για τα κελιά που θα υπολογίσουν, που έχουν τα νήματα και το task είναι αρκετά μικρό ώστε να μην δημιουργείται load imbalance.</span></p><p class="P8"> </p><p class="P8"> </p><p class="P43"><span class="T1">Συμπεράσματα και παρατηρήσεις.</span></p><p class="P40"><span class="T26">Το OpenMP API </span><span class="T27">γίνεται κατανοητό</span><span class="T26"> και γρήγορο στην εκμάθηση, στην χρήση και την συντήρηση του κώδικα. Ο </span><span class="T27">μεθοδολογία</span><span class="T26"> γραφής του είναι πολύ ξεκάθαρ</span><span class="T27">η με σημαντικό μειονέκτημα ότι </span><span class="T26">δεν υπάρχουν ιδιαίτερα περιθώρια για βελτιστοποιήσεις λόγω περιορισμών. </span><span class="T27">Η high level υλοποίηση δεν επιτρέπει στον συγγραφέα του κώδικα να αντιληφθεί</span><span class="T26"> πως συμπεριφέρεται στο background </span><span class="T27">η υλοποίηση του. Για το λόγο αυτό </span><span class="T26">μπορούν να υπάρξουν </span><span class="T27">δυσκολίες </span><span class="T26">για τ</span><span class="T27">ις</span><span class="T26"> οποί</span><span class="T27">ες</span><span class="T26"> δεν ευθύνεται άμεσα ο κώδικας . Αντίθετα τα Pthreads δεν έχουν την ίδια αυτοματοποίηση. </span><span class="T27">Ε</span><span class="T26">ίναι πιο χρονοβόρα στη δημιουργία του κώδικα </span><span class="T27">και στο συγχρονισμό καθώς είναι ευθύνη του δημιουργού πως θα διασφαλίσει την επικοινωνία μεταξύ των thread λόγω χάρη η ασφάλεια και η ελευθερία του έχει το thread αναλαμβάνοντας τη λειτουργία του mutex</span><span class="T26">. Τα πλεονεκτήματά τους είναι η δυνατότητα αλλαγής και βελτίωσης λεπτομερειών, καθώς και γ</span><span class="T27">νωστή η </span><span class="T26">ακριβ</span><span class="T27">ή</span><span class="T26">ς </span><span class="T27">λειτουργικότητα του κώδικα</span><span class="T26">.</span></p><p class="P42"><span class="T27">Σκοπός του OpenMp είναι </span><span class="T26">η υλοποίηση παραλληλισμού με σχετικά εύκολ</span><span class="T27">α βήματα</span><span class="T26"> ,ελάχιστες εντολές </span><span class="T27">και μικρή πολυπλοκότητα</span><span class="T26">. Οι εσωτερικοί έλεγχοι γίνονται </span><span class="T27">από</span><span class="T26"> το σύστημα οπότε δεν υπάρχει μεγάλη </span><span class="T27">ευχέρεια αλλαγή από </span><span class="T26">τον χρήστη. Το αρνητικό είναι ότι ίσως δεν κάνει την βέλτιστη χρήση της κοινής μνήμης</span></p></body></html>