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  • Why doesn't gcc remove this check of a non-volatile variable?

    - by Thomas
    This question is mostly academic. I ask out of curiosity, not because this poses an actual problem for me. Consider the following incorrect C program. #include <signal.h> #include <stdio.h> static int running = 1; void handler(int u) { running = 0; } int main() { signal(SIGTERM, handler); while (running) ; printf("Bye!\n"); return 0; } This program is incorrect because the handler interrupts the program flow, so running can be modified at any time and should therefore be declared volatile. But let's say the programmer forgot that. gcc 4.3.3, with the -O3 flag, compiles the loop body (after one initial check of the running flag) down to the infinite loop .L7: jmp .L7 which was to be expected. Now we put something trivial inside the while loop, like: while (running) putchar('.'); And suddenly, gcc does not optimize the loop condition anymore! The loop body's assembly now looks like this (again at -O3): .L7: movq stdout(%rip), %rsi movl $46, %edi call _IO_putc movl running(%rip), %eax testl %eax, %eax jne .L7 We see that running is re-loaded from memory each time through the loop; it is not even cached in a register. Apparently gcc now thinks that the value of running could have changed. So why does gcc suddenly decide that it needs to re-check the value of running in this case?

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  • Does the .NET CLR Really Optimize for the Current Processor

    - by dewald
    When I read about the performance of JITted languages like C# or Java, authors usually say that they should/could theoretically outperform many native-compiled applications. The theory being that native applications are usually just compiled for a processor family (like x86), so the compiler cannot make certain optimizations as they may not truly be optimizations on all processors. On the other hand, the CLR can make processor-specific optimizations during the JIT process. Does anyone know if Microsoft's (or Mono's) CLR actually performs processor-specific optimizations during the JIT process? If so, what kind of optimizations?

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  • help optimize sql query

    - by msony
    I have tracking table tbl_track with id, session_id, created_date fields I need count unique session_id for one day here what i got: select count(0) from ( select distinct session_id from tbl_track where created_date between getdate()-1 and getdate() group by session_id )tbl im feeling that it could be better solution for it

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  • efficacy of register allocation algorithms!

    - by aksci
    i'm trying to do a research/project on register allocation using graph coloring where i am to test the efficiency of different optimizing register allocation algorithms in different scenarios. how do i start? what are the prerequisites and the grounds with which i can test them. what all algos can i use? thank you!

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  • Can my loop be optimized any more? (C++)

    - by Sagekilla
    Below is one of my inner loops that's run several thousand times, with input sizes of 20 - 1000 or more. Is there anything I can do to help squeeze any more performance out of this? I'm not looking to move this code to something like using tree codes (Barnes-Hut), but towards optimizing the actual calculations happening inside, since the same calculations occur in the Barnes-Hut algorithm. Any help is appreciated! typedef double real; struct Particle { Vector pos, vel, acc, jerk; Vector oldPos, oldVel, oldAcc, oldJerk; real mass; }; class Vector { private: real vec[3]; public: // Operators defined here }; real Gravity::interact(Particle *p, size_t numParticles) { PROFILE_FUNC(); real tau_q = 1e300; for (size_t i = 0; i < numParticles; i++) { p[i].jerk = 0; p[i].acc = 0; } for (size_t i = 0; i < numParticles; i++) { for (size_t j = i+1; j < numParticles; j++) { Vector r = p[j].pos - p[i].pos; Vector v = p[j].vel - p[i].vel; real r2 = lengthsq(r); real v2 = lengthsq(v); // Calculate inverse of |r|^3 real r3i = Constants::G * pow(r2, -1.5); // da = r / |r|^3 // dj = (v / |r|^3 - 3 * (r . v) * r / |r|^5 Vector da = r * r3i; Vector dj = (v - r * (3 * dot(r, v) / r2)) * r3i; // Calculate new acceleration and jerk p[i].acc += da * p[j].mass; p[i].jerk += dj * p[j].mass; p[j].acc -= da * p[i].mass; p[j].jerk -= dj * p[i].mass; // Collision estimation // Metric 1) tau = |r|^2 / |a(j) - a(i)| // Metric 2) tau = |r|^4 / |v|^4 real mij = p[i].mass + p[j].mass; real tau_est_q1 = r2 / (lengthsq(da) * mij * mij); real tau_est_q2 = (r2*r2) / (v2*v2); if (tau_est_q1 < tau_q) tau_q = tau_est_q1; if (tau_est_q2 < tau_q) tau_q = tau_est_q2; } } return sqrt(sqrt(tau_q)); }

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  • Good Starting Points for Optimizing Database Calls in Ruby on Rails?

    - by viatropos
    I have a menu in Rails which grabs a nested tree of Post models, each which have a Slug model associated via a polymorphic association (using the friendly_id gem for slugs and awesome_nested_set for the tree). The database output in development looks like this (here's the full gist): SQL (0.4ms) SELECT COUNT(*) AS count_id FROM "posts" WHERE ("posts".parent_id = 39) CACHE (0.0ms) SELECT "posts".* FROM "posts" WHERE ("posts"."id" = 13) LIMIT 1 CACHE (0.0ms) SELECT "slugs".* FROM "slugs" WHERE ("slugs".sluggable_id = 13 AND "slugs".sluggable_type = 'Post') ORDER BY id DESC LIMIT 1 Slug Load (0.4ms) SELECT "slugs".* FROM "slugs" WHERE ("slugs".sluggable_id = 40 AND "slugs".sluggable_type = 'Post') ORDER BY id DESC LIMIT 1 SQL (0.3ms) SELECT COUNT(*) AS count_id FROM "posts" WHERE ("posts".parent_id = 40) CACHE (0.0ms) SELECT "posts".* FROM "posts" WHERE ("posts"."id" = 13) LIMIT 1 CACHE (0.0ms) SELECT "slugs".* FROM "slugs" WHERE ("slugs".sluggable_id = 13 AND "slugs".sluggable_type = 'Post') ORDER BY id DESC LIMIT 1 Slug Load (0.4ms) SELECT "slugs".* FROM "slugs" WHERE ("slugs".sluggable_id = 41 AND "slugs".sluggable_type = 'Post') ORDER BY id DESC LIMIT 1 ... Rendered shared/_menu.html.haml (907.6ms) What are some quick things I should always do to optimize this from the start (easy things)? Some things I'm thinking now are: Can Rails 3 eager load the whole Post tree + associated Slugs in one DB call? Can I do that easily with named scopes or custom SQL? What is best practice in this situation? Not really thinking about memcached in this situation as that can be applied to much more than just this.

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  • Try to fill the GAE datastore but the code consumes to much cpu time. How to optimize this?

    - by Neverland
    I try to get the list of images in Amazon EC2 inside the Google datastore. I want to realize this with a cron job inside the GAE. class AmazonEC2uswest(db.Model): ami = db.StringProperty(required=True) mani = db.StringProperty() typ = db.StringProperty() arch = db.StringProperty() state = db.StringProperty() owner = db.StringProperty() class CronAMIsAmazonUS_WEST(webapp.RequestHandler): def get(self): aws_access_key_id_admin = "<secret>" aws_secret_access_key_admin = "<secret>" conn_us_west = boto.ec2.connect_to_region('us-west-1', aws_access_key_id=aws_access_key_id_admin, aws_secret_access_key=aws_secret_access_key_admin, is_secure = False) liste_images_us_west = conn_us_west.get_all_images() laenge_liste_images_us_west = len(liste_images_us_west) for i in range(laenge_liste_images_us_west): datastore_uswest_AMIs = AmazonEC2uswest(ami=liste_images_us_west[i].id, mani=str(liste_images_us_west[i].location), typ=liste_images_us_west[i].type, arch=liste_images_us_west[i].architecture, state=liste_images_us_west[i].state, owner=liste_images_us_west[i].ownerId) datastore_uswest_AMIs.put() The problem: Getting the list with get_all_images() lasts only a few seconds. But writing the data to the Google datastore needs way too much CPU time. My IBM T42p (P4M with 2GHz) needs for that piece of code approx. 1 Minute! Is it possible to optimize my code in a way that it needs fewer CPU time?

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  • One letter game problem?

    - by Alex K
    Recently at a job interview I was given the following problem: Write a script capable of running on the command line as python It should take in two words on the command line (or optionally if you'd prefer it can query the user to supply the two words via the console). Given those two words: a. Ensure they are of equal length b. Ensure they are both words present in the dictionary of valid words in the English language that you downloaded. If so compute whether you can reach the second word from the first by a series of steps as follows a. You can change one letter at a time b. Each time you change a letter the resulting word must also exist in the dictionary c. You cannot add or remove letters If the two words are reachable, the script should print out the path which leads as a single, shortest path from one word to the other. You can /usr/share/dict/words for your dictionary of words. My solution consisted of using breadth first search to find a shortest path between two words. But apparently that wasn't good enough to get the job :( Would you guys know what I could have done wrong? Thank you so much. import collections import functools import re def time_func(func): import time def wrapper(*args, **kwargs): start = time.time() res = func(*args, **kwargs) timed = time.time() - start setattr(wrapper, 'time_taken', timed) return res functools.update_wrapper(wrapper, func) return wrapper class OneLetterGame: def __init__(self, dict_path): self.dict_path = dict_path self.words = set() def run(self, start_word, end_word): '''Runs the one letter game with the given start and end words. ''' assert len(start_word) == len(end_word), \ 'Start word and end word must of the same length.' self.read_dict(len(start_word)) path = self.shortest_path(start_word, end_word) if not path: print 'There is no path between %s and %s (took %.2f sec.)' % ( start_word, end_word, find_shortest_path.time_taken) else: print 'The shortest path (found in %.2f sec.) is:\n=> %s' % ( self.shortest_path.time_taken, ' -- '.join(path)) def _bfs(self, start): '''Implementation of breadth first search as a generator. The portion of the graph to explore is given on demand using get_neighboors. Care was taken so that a vertex / node is explored only once. ''' queue = collections.deque([(None, start)]) inqueue = set([start]) while queue: parent, node = queue.popleft() yield parent, node new = set(self.get_neighbours(node)) - inqueue inqueue = inqueue | new queue.extend([(node, child) for child in new]) @time_func def shortest_path(self, start, end): '''Returns the shortest path from start to end using bfs. ''' assert start in self.words, 'Start word not in dictionnary.' assert end in self.words, 'End word not in dictionnary.' paths = {None: []} for parent, child in self._bfs(start): paths[child] = paths[parent] + [child] if child == end: return paths[child] return None def get_neighbours(self, word): '''Gets every word one letter away from the a given word. We do not keep these words in memory because bfs accesses a given vertex only once. ''' neighbours = [] p_word = ['^' + word[0:i] + '\w' + word[i+1:] + '$' for i, w in enumerate(word)] p_word = '|'.join(p_word) for w in self.words: if w != word and re.match(p_word, w, re.I|re.U): neighbours += [w] return neighbours def read_dict(self, size): '''Loads every word of a specific size from the dictionnary into memory. ''' for l in open(self.dict_path): l = l.decode('latin-1').strip().lower() if len(l) == size: self.words.add(l) if __name__ == '__main__': import sys if len(sys.argv) not in [3, 4]: print 'Usage: python one_letter_game.py start_word end_word' else: g = OneLetterGame(dict_path = '/usr/share/dict/words') try: g.run(*sys.argv[1:]) except AssertionError, e: print e

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  • The best way to implement drawing features like Keynote

    - by Shamseddine
    Hi all, I'm trying to make a little iPad tool's for drawing simple geometrical objects (rect, rounded rect, ellipse, star, ...). My goal is to make something very close to Keynote (drawing feature), i.e. let the user add a rect (for instance), resizing it and moving it. I want too the user can select many objects and move them together. I've thought about at least 3 differents ways to do that : Extends UIView for each object type, a class for Rect, another for Ellipse, ... With custom drawing method. Then add this view as subview of the global view. Extends CALayer for each object type, a class for Rect, another for Ellipse, ... With custom drawing method. Then add this layer as sublayer of the global view layer's. Extends NSObject for each object type, a class for Rect, another for Ellipse, ... With just a drawing method which will get as argument a CGContext and a Rect and draw directly the form in it. Those methods will be called by the drawing method of the global view. I'm aware that the two first ways come with functions to detect touch on each object, to add easily shadows,... but I'm afraid that they are a little too heavy ? That's why I thought about the last way, which it seems to be straight forward. Which way will be the more efficient ??? Or maybe I didn't thought another way ? Any help will be appreciated ;-) Thanks.

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  • speed up wamp server + drupal on windows vista

    - by Andrew Welch
    Hi, My localhost performance with drupal six is pretty slow. I found a solution to add a # before the :: localhost line of the system32/etc/hosts file but this was something I had already done and didn't help much. does anyone know of any other optimisations that might work? tHanks Andy

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  • Webcrawler, feedback?

    - by Jan Kuboschek
    Hey folks, every once in a while I have the need to automate data collection tasks from websites. Sometimes I need a bunch of URLs from a directory, sometimes I need an XML sitemap (yes, I know there is lots of software for that and online services). Anyways, as follow up to my previous question I've written a little webcrawler that can visit websites. Basic crawler class to easily and quickly interact with one website. Override "doAction(String URL, String content)" to process the content further (e.g. store it, parse it). Concept allows for multi-threading of crawlers. All class instances share processed and queued lists of links. Instead of keeping track of processed links and queued links within the object, a JDBC connection could be established to store links in a database. Currently limited to one website at a time, however, could be expanded upon by adding an externalLinks stack and adding to it as appropriate. JCrawler is intended to be used to quickly generate XML sitemaps or parse websites for your desired information. It's lightweight. Is this a good/decent way to write the crawler, provided the limitations above? http://pastebin.com/VtgC4qVE - Main.java http://pastebin.com/gF4sLHEW - JCrawler.java http://pastebin.com/VJ1grArt - HTMLUtils.java Thanks for your feedback in advance! :)

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  • PHP Increasing write to page speed.

    - by Frederico
    I'm currently writing out xml and have done the following: header ("content-type: text/xml"); header ("content-length: ".strlen($xml)); $xml being the xml to be written out. I'm near about 1.8 megs of text (which I found via firebug), it seems as the writing is taking more time than the script to run.. is there a way to increase this write speed? Thank you in advance.

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  • Memory efficient int-int dict in Python

    - by Bolo
    Hi, I need a memory efficient int-int dict in Python that would support the following operations in O(log n) time: d[k] = v # replace if present v = d[k] # None or a negative number if not present I need to hold ~250M pairs, so it really has to be tight. Do you happen to know a suitable implementation (Python 2.7)? EDIT Removed impossible requirement and other nonsense. Thanks, Craig and Kylotan! To rephrase. Here's a trivial int-int dictionary with 1M pairs: >>> import random, sys >>> from guppy import hpy >>> h = hpy() >>> h.setrelheap() >>> d = {} >>> for _ in xrange(1000000): ... d[random.randint(0, sys.maxint)] = random.randint(0, sys.maxint) ... >>> h.heap() Partition of a set of 1999530 objects. Total size = 49161112 bytes. Index Count % Size % Cumulative % Kind (class / dict of class) 0 1 0 25165960 51 25165960 51 dict (no owner) 1 1999521 100 23994252 49 49160212 100 int On average, a pair of integers uses 49 bytes. Here's an array of 2M integers: >>> import array, random, sys >>> from guppy import hpy >>> h = hpy() >>> h.setrelheap() >>> a = array.array('i') >>> for _ in xrange(2000000): ... a.append(random.randint(0, sys.maxint)) ... >>> h.heap() Partition of a set of 14 objects. Total size = 8001108 bytes. Index Count % Size % Cumulative % Kind (class / dict of class) 0 1 7 8000028 100 8000028 100 array.array On average, a pair of integers uses 8 bytes. I accept that 8 bytes/pair in a dictionary is rather hard to achieve in general. Rephrased question: is there a memory-efficient implementation of int-int dictionary that uses considerably less than 49 bytes/pair?

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  • F# - Facebook Hacker Cup - Double Squares

    - by Jacob
    I'm working on strengthening my F#-fu and decided to tackle the Facebook Hacker Cup Double Squares problem. I'm having some problems with the run-time and was wondering if anyone could help me figure out why it is so much slower than my C# equivalent. There's a good description from another post; Source: Facebook Hacker Cup Qualification Round 2011 A double-square number is an integer X which can be expressed as the sum of two perfect squares. For example, 10 is a double-square because 10 = 3^2 + 1^2. Given X, how can we determine the number of ways in which it can be written as the sum of two squares? For example, 10 can only be written as 3^2 + 1^2 (we don't count 1^2 + 3^2 as being different). On the other hand, 25 can be written as 5^2 + 0^2 or as 4^2 + 3^2. You need to solve this problem for 0 = X = 2,147,483,647. Examples: 10 = 1 25 = 2 3 = 0 0 = 1 1 = 1 My basic strategy (which I'm open to critique on) is to; Create a dictionary (for memoize) of the input numbers initialzed to 0 Get the largest number (LN) and pass it to count/memo function Get the LN square root as int Calculate squares for all numbers 0 to LN and store in dict Sum squares for non repeat combinations of numbers from 0 to LN If sum is in memo dict, add 1 to memo Finally, output the counts of the original numbers. Here is the F# code (See code changes at bottom) I've written that I believe corresponds to this strategy (Runtime: ~8:10); open System open System.Collections.Generic open System.IO /// Get a sequence of values let rec range min max = seq { for num in [min .. max] do yield num } /// Get a sequence starting from 0 and going to max let rec zeroRange max = range 0 max /// Find the maximum number in a list with a starting accumulator (acc) let rec maxNum acc = function | [] -> acc | p::tail when p > acc -> maxNum p tail | p::tail -> maxNum acc tail /// A helper for finding max that sets the accumulator to 0 let rec findMax nums = maxNum 0 nums /// Build a collection of combinations; ie [1,2,3] = (1,1), (1,2), (1,3), (2,2), (2,3), (3,3) let rec combos range = seq { let count = ref 0 for inner in range do for outer in Seq.skip !count range do yield (inner, outer) count := !count + 1 } let rec squares nums = let dict = new Dictionary<int, int>() for s in nums do dict.[s] <- (s * s) dict /// Counts the number of possible double squares for a given number and keeps track of other counts that are provided in the memo dict. let rec countDoubleSquares (num: int) (memo: Dictionary<int, int>) = // The highest relevent square is the square root because it squared plus 0 squared is the top most possibility let maxSquare = System.Math.Sqrt((float)num) // Our relevant squares are 0 to the highest possible square; note the cast to int which shouldn't hurt. let relSquares = range 0 ((int)maxSquare) // calculate the squares up front; let calcSquares = squares relSquares // Build up our square combinations; ie [1,2,3] = (1,1), (1,2), (1,3), (2,2), (2,3), (3,3) for (sq1, sq2) in combos relSquares do let v = calcSquares.[sq1] + calcSquares.[sq2] // Memoize our relevant results if memo.ContainsKey(v) then memo.[v] <- memo.[v] + 1 // return our count for the num passed in memo.[num] // Read our numbers from file. //let lines = File.ReadAllLines("test2.txt") //let nums = [ for line in Seq.skip 1 lines -> Int32.Parse(line) ] // Optionally, read them from straight array let nums = [1740798996; 1257431873; 2147483643; 602519112; 858320077; 1048039120; 415485223; 874566596; 1022907856; 65; 421330820; 1041493518; 5; 1328649093; 1941554117; 4225; 2082925; 0; 1; 3] // Initialize our memoize dictionary let memo = new Dictionary<int, int>() for num in nums do memo.[num] <- 0 // Get the largest number in our set, all other numbers will be memoized along the way let maxN = findMax nums // Do the memoize let maxCount = countDoubleSquares maxN memo // Output our results. for num in nums do printfn "%i" memo.[num] // Have a little pause for when we debug let line = Console.Read() And here is my version in C# (Runtime: ~1:40: using System; using System.Collections.Generic; using System.Diagnostics; using System.IO; using System.Linq; using System.Text; namespace FBHack_DoubleSquares { public class TestInput { public int NumCases { get; set; } public List<int> Nums { get; set; } public TestInput() { Nums = new List<int>(); } public int MaxNum() { return Nums.Max(); } } class Program { static void Main(string[] args) { // Read input from file. //TestInput input = ReadTestInput("live.txt"); // As example, load straight. TestInput input = new TestInput { NumCases = 20, Nums = new List<int> { 1740798996, 1257431873, 2147483643, 602519112, 858320077, 1048039120, 415485223, 874566596, 1022907856, 65, 421330820, 1041493518, 5, 1328649093, 1941554117, 4225, 2082925, 0, 1, 3, } }; var maxNum = input.MaxNum(); Dictionary<int, int> memo = new Dictionary<int, int>(); foreach (var num in input.Nums) { if (!memo.ContainsKey(num)) memo.Add(num, 0); } DoMemoize(maxNum, memo); StringBuilder sb = new StringBuilder(); foreach (var num in input.Nums) { //Console.WriteLine(memo[num]); sb.AppendLine(memo[num].ToString()); } Console.Write(sb.ToString()); var blah = Console.Read(); //File.WriteAllText("out.txt", sb.ToString()); } private static int DoMemoize(int num, Dictionary<int, int> memo) { var highSquare = (int)Math.Floor(Math.Sqrt(num)); var squares = CreateSquareLookup(highSquare); var relSquares = squares.Keys.ToList(); Debug.WriteLine("Starting - " + num.ToString()); Debug.WriteLine("RelSquares.Count = {0}", relSquares.Count); int sum = 0; var index = 0; foreach (var square in relSquares) { foreach (var inner in relSquares.Skip(index)) { sum = squares[square] + squares[inner]; if (memo.ContainsKey(sum)) memo[sum]++; } index++; } if (memo.ContainsKey(num)) return memo[num]; return 0; } private static TestInput ReadTestInput(string fileName) { var lines = File.ReadAllLines(fileName); var input = new TestInput(); input.NumCases = int.Parse(lines[0]); foreach (var lin in lines.Skip(1)) { input.Nums.Add(int.Parse(lin)); } return input; } public static Dictionary<int, int> CreateSquareLookup(int maxNum) { var dict = new Dictionary<int, int>(); int square; foreach (var num in Enumerable.Range(0, maxNum)) { square = num * num; dict[num] = square; } return dict; } } } Thanks for taking a look. UPDATE Changing the combos function slightly will result in a pretty big performance boost (from 8 min to 3:45): /// Old and Busted... let rec combosOld range = seq { let rangeCache = Seq.cache range let count = ref 0 for inner in rangeCache do for outer in Seq.skip !count rangeCache do yield (inner, outer) count := !count + 1 } /// The New Hotness... let rec combos maxNum = seq { for i in 0..maxNum do for j in i..maxNum do yield i,j }

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  • How Do You Profile & Optimize CUDA Kernels?

    - by John Dibling
    I am somewhat familiar with the CUDA visual profiler and the occupancy spreadsheet, although I am probably not leveraging them as well as I could. Profiling & optimizing CUDA code is not like profiling & optimizing code that runs on a CPU. So I am hoping to learn from your experiences about how to get the most out of my code. There was a post recently looking for the fastest possible code to identify self numbers, and I provided a CUDA implementation. I'm not satisfied that this code is as fast as it can be, but I'm at a loss as to figure out both what the right questions are and what tool I can get the answers from. How do you identify ways to make your CUDA kernels perform faster?

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  • Any difference between lazy loading Javascript files vs. placing just before </body>

    - by mhr
    Looked around, couldn't find this specific question discussed. Pretty sure the difference is negligible, just curious as to your thoughts. Scenario: All Javascript that doesn't need to be loaded before page render has been placed just before the closing </body> tag. Are there any benefits or detriments to lazy loading these instead through some Javascript code in the head that executes when the DOM load/ready event is fired? Let's say that this only concerns downloading one entire .js file full of functions and not lazy loading several individual files as needed upon usage. Hope that's clear, thanks.

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  • How to optimise MySQL query containing a subquery?

    - by aidan
    I have two tables, House and Person. For any row in House, there can be 0, 1 or many corresponding rows in Person. But, of those people, a maximum of one will have a status of "ACTIVE", the others will all have a status of "CANCELLED". e.g. SELECT * FROM House LEFT JOIN Person ON House.ID = Person.HouseID House.ID | Person.ID | Person.Status 1 | 1 | CANCELLED 1 | 2 | CANCELLED 1 | 3 | ACTIVE 2 | 1 | ACTIVE 3 | NULL | NULL 4 | 4 | CANCELLED I want to filter out the cancelled rows, and get something like this: House.ID | Person.ID | Person.Status 1 | 3 | ACTIVE 2 | 1 | ACTIVE 3 | NULL | NULL 4 | NULL | NULL I've achieved this with the following sub select: SELECT * FROM House LEFT JOIN ( SELECT * FROM Person WHERE Person.Status != "CANCELLED" ) Person ON House.ID = Person.HouseID ...which works, but breaks all the indexes. Is there a better solution that doesn't? I'm using MySQL and all relevant columns are indexed. EXPLAIN lists nothing in possible_keys. Thanks.

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  • Combine query results from one table with the defaults from another

    - by pulegium
    This is a dumbed down version of the real table data, so may look bit silly. Table 1 (users): id INT username TEXT favourite_food TEXT food_pref_id INT Table 2 (food_preferences): id INT food_type TEXT The logic is as follows: Let's say I have this in my food preference table: 1, 'VEGETARIAN' and this in the users table: 1, 'John', NULL, 1 2, 'Pete', 'Curry', 1 In which case John defaults to be a vegetarian, but Pete should show up as a person who enjoys curry. Question, is there any way to combine the query into one select statement, so that it would get the default from the preferences table if the favourite_food column is NULL? I can obviously do this in application logic, but would be nice just to offload this to SQL, if possible. DB is SQLite3...

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  • How to use constraint programming for optimizing shopping baskets?

    - by tangens
    I have a list of items I want to buy. The items are offered by different shops and different prices. The shops have individual delivery costs. I'm looking for an optimal shopping strategy (and a java library supporting it) to purchase all of the items with a minimal total price. Example: Item1 is offered at Shop1 for $100, at Shop2 for $111. Item2 is offered at Shop1 for $90, at Shop2 for $85. Delivery cost of Shop1: $10 if total order < $150; $0 otherwise Delivery cost of Shop2: $5 if total order < $50; $0 otherwise If I buy Item1 and Item2 at Shop1 the total cost is $100 + $90 +$0 = $190. If I buy Item1 and Item2 at Shop2 the total cost is $111 + $85 +$0 = $196. If I buy Item1 at Shop1 and Item2 at Shop2 the total cost is $100 + $10 + $85 + $0 = 195. I get the minimal price if I order Item1 and Item2 at Shop1: $190 What I tried so far I asked another question before that led me to the field of constraint programming. I had a look at cream and choco, but I did not figure out how to create a model to solve my problem. | shop1 | shop2 | shop3 | ... ----------------------------------------- item1 | p11 | p12 | p13 | item2 | p21 | p22 | p23 | . | | | | . | | | | ----------------------------------------- shipping | s1 | s2 | s3 | limit | l1 | l2 | l3 | ----------------------------------------- total | t1 | t2 | t3 | ----------------------------------------- My idea was to define these constraints: each price "p xy" is defined in the domain (0, c) where c is the price of the item in this shop only one price in a line should be non zero if one or more items are bought from one shop and the sum of the prices is lower than limit, then add shipping cost to the total cost shop total cost is the sum of the prices of all items in a shop total cost is the sum of all shop totals The objective is "total cost". I want to minimize this. In cream I wasn't able to express the "if then" constraint for conditional shipping costs. In choco these constraints exist, but even for 5 items and 10 shops the program was running for 10 minutes without finding a solution. Question How should I express my constraints to make this problem solvable for a constraint programming solver?

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  • Float compile-time calculation not happening?

    - by Klaim
    A little test program: #include <iostream> const float TEST_FLOAT = 1/60; const float TEST_A = 1; const float TEST_B = 60; const float TEST_C = TEST_A / TEST_B; int main() { std::cout << TEST_FLOAT << std::endl; std::cout << TEST_C << std::endl; std::cin.ignore(); return 0; } Result : 0 0.0166667 Tested on Visual Studio 2008 & 2010. I worked on other compilers that, if I remember well, made the first result like the second result. Now my memory could be wrong, but shouldn't TEST_FLOAT have the same value than TEST_C? If not, why? Is TEST_C value resolved at compile time or at runtime? I always assumed the former but now that I see those results I have some doubts...

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  • Very slow Eclipse 4.2, how to make it more responsive?

    - by Laurent
    I'm using Eclipse PDT on a rather large PHP project and the IDE is almost unusable. It takes nearly 30 seconds to open a file, and other actions, like selecting a folder in the file explorer, editing some text, etc. are equally slow. I followed various instructions to speed it up but nothing seems to work. This is my current eclipse.ini file. Any idea how I can improve it? -startup plugins/org.eclipse.equinox.launcher_1.3.0.v20120522-1813.jar --launcher.library plugins/org.eclipse.equinox.launcher.win32.win32.x86_1.1.200.v20120522-1813 -showsplash org.eclipse.platform --launcher.XXMaxPermSize 256m --launcher.defaultAction openFile -vmargs -server -Dosgi.requiredJavaVersion=1.7 -Xmn128m -Xms1024m -Xmx1024m -Xss2m -XX:PermSize=128m -XX:MaxPermSize=128m -XX:+UseParallelGC System: Eclipse 4.2.0, Windows 7, 4 GB RAM

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  • Jruby rspec to be run parallely

    - by Priyank
    Hi. Is there something like Spork for Jruby too? We want to parallelize our specs to run faster and pre-load the classes while running the rake task; however we have not been able to do so. Since our project is considerable in size, specs take about 15 minutes to complete and this poses a serious challenge to quick turnaround. Any ideas are more than welcome. Cheers

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  • Optimizing Python code with many attribute and dictionary lookups

    - by gotgenes
    I have written a program in Python which spends a large amount of time looking up attributes of objects and values from dictionary keys. I would like to know if there's any way I can optimize these lookup times, potentially with a C extension, to reduce the time of execution, or if I need to simply re-implement the program in a compiled language. The program implements some algorithms using a graph. It runs prohibitively slowly on our data sets, so I profiled the code with cProfile using a reduced data set that could actually complete. The vast majority of the time is being burned in one function, and specifically in two statements, generator expressions, within the function: The generator expression at line 202 is neighbors_in_selected_nodes = (neighbor for neighbor in node_neighbors if neighbor in selected_nodes) and the generator expression at line 204 is neighbor_z_scores = (interaction_graph.node[neighbor]['weight'] for neighbor in neighbors_in_selected_nodes) The source code for this function of context provided below. selected_nodes is a set of nodes in the interaction_graph, which is a NetworkX Graph instance. node_neighbors is an iterator from Graph.neighbors_iter(). Graph itself uses dictionaries for storing nodes and edges. Its Graph.node attribute is a dictionary which stores nodes and their attributes (e.g., 'weight') in dictionaries belonging to each node. Each of these lookups should be amortized constant time (i.e., O(1)), however, I am still paying a large penalty for the lookups. Is there some way which I can speed up these lookups (e.g., by writing parts of this as a C extension), or do I need to move the program to a compiled language? Below is the full source code for the function that provides the context; the vast majority of execution time is spent within this function. def calculate_node_z_prime( node, interaction_graph, selected_nodes ): """Calculates a z'-score for a given node. The z'-score is based on the z-scores (weights) of the neighbors of the given node, and proportional to the z-score (weight) of the given node. Specifically, we find the maximum z-score of all neighbors of the given node that are also members of the given set of selected nodes, multiply this z-score by the z-score of the given node, and return this value as the z'-score for the given node. If the given node has no neighbors in the interaction graph, the z'-score is defined as zero. Returns the z'-score as zero or a positive floating point value. :Parameters: - `node`: the node for which to compute the z-prime score - `interaction_graph`: graph containing the gene-gene or gene product-gene product interactions - `selected_nodes`: a `set` of nodes fitting some criterion of interest (e.g., annotated with a term of interest) """ node_neighbors = interaction_graph.neighbors_iter(node) neighbors_in_selected_nodes = (neighbor for neighbor in node_neighbors if neighbor in selected_nodes) neighbor_z_scores = (interaction_graph.node[neighbor]['weight'] for neighbor in neighbors_in_selected_nodes) try: max_z_score = max(neighbor_z_scores) # max() throws a ValueError if its argument has no elements; in this # case, we need to set the max_z_score to zero except ValueError, e: # Check to make certain max() raised this error if 'max()' in e.args[0]: max_z_score = 0 else: raise e z_prime = interaction_graph.node[node]['weight'] * max_z_score return z_prime Here are the top couple of calls according to cProfiler, sorted by time. ncalls tottime percall cumtime percall filename:lineno(function) 156067701 352.313 0.000 642.072 0.000 bpln_contextual.py:204(<genexpr>) 156067701 289.759 0.000 289.759 0.000 bpln_contextual.py:202(<genexpr>) 13963893 174.047 0.000 816.119 0.000 {max} 13963885 69.804 0.000 936.754 0.000 bpln_contextual.py:171(calculate_node_z_prime) 7116883 61.982 0.000 61.982 0.000 {method 'update' of 'set' objects}

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  • How efficient is PHP's substr?

    - by zildjohn01
    I'm writing a parser in PHP which must be able to handle large in-memory strings, so this is a somewhat important issue. (ie, please don't "premature optimize" flame me, please) How does the substr function work? Does it make a second copy of the string data in memory, or does it reference the original? Should I worry about calling, for example, $str = substr($str, 1); in a loop?

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  • Combining javascripts into a single file

    - by toomanyairmiles
    Having read up recently on yahoo's web optimisation tips and using YSlow I've implemented a few of their ideas on one of my sites http://www.gwynfryncottages.com you can see the file here http://www.gwynfryncottages.com/js/gw-custom.js. While this technique seems to work perfectly on most occasions, and really does speed up the site, I do notice a significantly higher number of errors where the javascripts don't load or don't load completely while I'm working on the site so three questions:- is combining scripts this way a good idea at all in terms of reliablity? is there any way to measure the number of errors? is there any way to 'pre-load' the javascript or ensure that the number of loading errors is reduced?

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