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  • Converting python collaborative filtering code to use Map Reduce

    - by Neil Kodner
    Using Python, I'm computing cosine similarity across items. given event data that represents a purchase (user,item), I have a list of all items 'bought' by my users. Given this input data (user,item) X,1 X,2 Y,1 Y,2 Z,2 Z,3 I build a python dictionary {1: ['X','Y'], 2 : ['X','Y','Z'], 3 : ['Z']} From that dictionary, I generate a bought/not bought matrix, also another dictionary(bnb). {1 : [1,1,0], 2 : [1,1,1], 3 : [0,0,1]} From there, I'm computing similarity between (1,2) by calculating cosine between (1,1,0) and (1,1,1), yielding 0.816496 I'm doing this by: items=[1,2,3] for item in items: for sub in items: if sub >= item: #as to not calculate similarity on the inverse sim = coSim( bnb[item], bnb[sub] ) I think the brute force approach is killing me and it only runs slower as the data gets larger. Using my trusty laptop, this calculation runs for hours when dealing with 8500 users and 3500 items. I'm trying to compute similarity for all items in my dict and it's taking longer than I'd like it to. I think this is a good candidate for MapReduce but I'm having trouble 'thinking' in terms of key/value pairs. Alternatively, is the issue with my approach and not necessarily a candidate for Map Reduce?

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  • Most optimized way to calculate modulus in C

    - by hasanatkazmi
    I have minimize cost of calculating modulus in C. say I have a number x and n is the number which will divide x when n == 65536 (which happens to be 2^16): mod = x % n (11 assembly instructions as produced by GCC) or mod = x & 0xffff which is equal to mod = x & 65535 (4 assembly instructions) so, GCC doesn't optimize it to this extent. In my case n is not x^(int) but is largest prime less than 2^16 which is 65521 as I showed for n == 2^16, bit-wise operations can optimize the computation. What bit-wise operations can I preform when n == 65521 to calculate modulus.

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  • JavaScript replace with callback - performance question

    - by Tomalak
    In JavaScript, you can define a callback handler in regex string replace operations: str.replace(/str[123]|etc/, replaceCallback); Imagine you have a lookup object of strings and replacements. var lookup = {"str1": "repl1", "str2": "repl2", "str3": "repl3", "etc": "etc" }; and this callback function: var replaceCallback = function(match) { if (lookup[match]) return lookup[match]; else return match; } How would you assess the performance of the above callback? Are there solid ways to improve it? Would if (match in lookup) //.... or even return lookup[match] | match; lead to opportunities for the JS compiler to optimize, or is it all the same thing?

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  • Efficient code to avoid circular references in c# object model

    - by Kumar
    I have an excel like grid where values can be typed referencing other rows To check for circular references when a new value is entered, i traverse the tree and create a list of values referenced thus far, if the current value is found in this list, i return an error thus avoiding a circular reference. This is infrequent enough where extreme performance is not an issue but... Question - is there a better way ? I'm told it's not the most optimal but no answer was provided so on to the experts @ SO :)

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  • Optimizing PHP require_once's for low disk i/o?

    - by buggedcom
    Q1) I'm designing a CMS (-who isn't!) but priority is being given to caching. Literally everything is cached. DB rows, DB id queries, Configuration data, processed data, compiled templates. Currently it has two layers of caching. The first is a opcode cache or memory cache such as apc, eaccelerator, xcache or memcached. If an entry is not found in there it is then searched for in the secondary slow cache, ie php includes. Are the opcode caches actually faster than doing a require_once to a php file with a var_export'd array of data in it? My tests are inconclusive as my development box (5.3 of XAMPP) keeps throwing errors installing any of the aforementioned programs. Q2) The CMS has numerous helper classes that are autoloaded on demand instead of loading all files. Mostly each has a require before it so no autoloading needs to take place, however this is not the question. Because a page script can have up to 50/60 helper files included I have a feeling that if the site was under pressure it would buckle because of all the i/o that this incurs. Ignore for the moment that there is output cache in place that would remove the need for what I am about to suggest, and also that opcode caches would render this moot. What I have tried to do is join all the helper files required for the scripts execution in one single file. This is achievable and works well, however it has a side effect of greatly increasing the memory usage dramatically even though technically the same code is being used. What are your thoughts and opinions on this?

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  • Optimizing a 3D World Javascript Animation

    - by johnny
    Hi! I've recently come up with the idea to create a tag cloud like animation shaped like the earth. I've extracted the coastline coordinates from ngdc.noaa.gov and wrote a little script that displayed it in my browser. Now as you can imagine, the whole coastline consists of about 48919 points, which my script would individually render (each coordinate being represented by one span). Obviously no browser is capable of rendering this fluently - but it would be nice if I could render as much as let's say 200 spans (twice as much as now) on my old p4 2.8 Ghz (as a representative benchmark). Are there any javascript optimizations I could use in order to speed up the display of those spans? One 'coordinate': <div id="world_pixels"> <span id="wp_0" style="position:fixed; top:0px; left:0px; z-index:1; font-size:20px; cursor:pointer;cursor:hand;" onmouseover="magnify_world_pixel('wp_0');" onmouseout="shrink_world_pixel('wp_0');" onClick="set_askcue_bar('', 'new york')">new york</span> </div> The script: $(document).ready(function(){ world_pixels = $("#world_pixels span"); world_pixels.spin(); setInterval("world_pixels.spin()",1500); }); z = new Array(); $.fn.spin = function () { for(i=0; i<this.length; i++) { /*actual screen coordinates: x/y/z --> left/font-size/top 300/13/0 300/6/300 | / |/ 0/13/300 ----|---- 600/13/300 /| / | 300/20/300 300/13/600 */ /*scale font size*/ var resize_x = 1; /*scale width*/ var resize_y = 2.5; /*scale height*/ var resize_z = 2.5; var from_left = 300; var from_top = 20; /*actual math coordinates: 1 -1 | / |/ 1 ----|---- -1 /| / | 1 -1 */ //var get_element = document.getElementById(); //var font_size = parseInt(this.style.fontSize); var font_size = parseInt($(this[i]).css("font-size")); var left = parseInt($(this[i]).css("left")); if (coast_line_array[i][1]) { } else { var top = parseInt($(this[i]).css("top")); z[i] = from_top + (top - (300 * resize_z)) / (300 * resize_z); //global beacause it's used in other functions later on var top_new = from_top + Math.round(Math.cos(coast_line_array[i][2]/90*Math.PI) * (300 * resize_z) + (300 * resize_z)); $(this[i]).css("top", top_new); coast_line_array[i][3] = 1; } var x = resize_x * (font_size - 13) / 7; var y = from_left + (left- (300 * resize_y)) / (300 * resize_y); if (y >= 0) { this[i].phi = Math.acos(x/(Math.sqrt(x^2 + y^2))); } else { this[i].phi = 2*Math.PI - Math.acos(x/(Math.sqrt(x^2 + y^2))); i } this[i].theta = Math.acos(z[i]/Math.sqrt(x^2 + y^2 + z[i]^2)); var font_size_new = resize_x * Math.round(Math.sin(coast_line_array[i][4]/90*Math.PI) * Math.cos(coast_line_array[i][0]/180*Math.PI) * 7 + 13); var left_new = from_left + Math.round(Math.sin(coast_line_array[i][5]/90*Math.PI) * Math.sin(coast_line_array[i][0]/180*Math.PI) * (300 * resize_y) + (300 * resize_y)); //coast_line_array[i][6] = coast_line_array[i][7]+1; if ((coast_line_array[i][0] + 1) > 180) { coast_line_array[i][0] = -180; } else { coast_line_array[i][0] = coast_line_array[i][0] + 0.25; } $(this[i]).css("font-size", font_size_new); $(this[i]).css("left", left_new); } } resize_x = 1; function magnify_world_pixel(element) { $("#"+element).animate({ fontSize: resize_x*30+"px" }, { duration: 1000 }); } function shrink_world_pixel(element) { $("#"+element).animate({ fontSize: resize_x*6+"px" }, { duration: 1000 }); } I'd appreciate any suggestions to optimize my script, maybe there is even a totally different approach on how to go about this. The whole .js file which stores the array for all the coordinates is available on my page, the file is about 2.9 mb, so you might consider pulling the .zip for local testing: metaroulette.com/files/31218.zip metaroulette.com/files/31218.js P.S. the php I use to create the spans: <?php //$arbitrary_characters = array('a','b','c','ddsfsdfsdf','e','f','g','h','isdfsdffd','j','k','l','mfdgcvbcvbs','n','o','p','q','r','s','t','uasdfsdf','v','w','x','y','z','0','1','2','3','4','5','6','7','8','9',); $arbitrary_characters = array('cat','table','cool','deloitte','askcue','what','more','less','adjective','nice','clinton','mars','jupiter','testversion','beta','hilarious','lolcatz','funny','obama','president','nice','what','misplaced','category','people','religion','global','skyscraper','new york','dubai','helsinki','volcano','iceland','peter','telephone','internet', 'dialer', 'cord', 'movie', 'party', 'chris', 'guitar', 'bentley', 'ford', 'ferrari', 'etc', 'de facto'); for ($i=0; $i<96; $i++) { $arb_digits = rand (0,45); $arbitrary_character = $arbitrary_characters[$arb_digits]; //$arbitrary_character = "."; echo "<span id=\"wp_$i\" style=\"position:fixed; top:0px; left:0px; z-index:1; font-size:20px; cursor:pointer;cursor:hand;\" onmouseover=\"magnify_world_pixel('wp_$i');\" onmouseout=\"shrink_world_pixel('wp_$i');\" onClick=\"set_askcue_bar('', '$arbitrary_character')\">$arbitrary_character</span>\n"; } ?>

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  • Why is Magento so slow?

    - by mr-euro
    Is Magento usually so terrible slow? This is my first experience with it and the admin panel simply takes ages to load and save changes. It is a default installation with the test data. The server it is hosted on serves other non-Magento sites super fast. What is it about the PHP code that Magento uses that makes it so slow, and what can be done to fix it?

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  • Fastest way to list all primes below N in python

    - by jbochi
    This is the best algorithm I could come up with after struggling with a couple of Project Euler's questions. def get_primes(n): numbers = set(range(n, 1, -1)) primes = [] while numbers: p = numbers.pop() primes.append(p) numbers.difference_update(set(range(p*2, n+1, p))) return primes >>> timeit.Timer(stmt='get_primes.get_primes(1000000)', setup='import get_primes').timeit(1) 1.1499958793645562 Can it be made even faster? EDIT: This code has a flaw: Since numbers is an unordered set, there is no guarantee that numbers.pop() will remove the lowest number from the set. Nevertheless, it works (at least for me) for some input numbers: >>> sum(get_primes(2000000)) 142913828922L #That's the correct sum of all numbers below 2 million >>> 529 in get_primes(1000) False >>> 529 in get_primes(530) True EDIT: The rank so far (pure python, no external sources, all primes below 1 million): Sundaram's Sieve implementation by myself: 327ms Daniel's Sieve: 435ms Alex's recipe from Cookbok: 710ms EDIT: ~unutbu is leading the race.

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  • Why better isolation level means better performance in MS SQL Server

    - by Oleg Zhylin
    When measuring performance on my query I came up with a dependency between isolation level and elapsed time that was surprising to me READUNCOMMITTED - 409024 READCOMMITTED - 368021 REPEATABLEREAD - 358019 SERIALIZABLE - 348019 Left column is table hint, and the right column is elapsed time in microseconds (sys.dm_exec_query_stats.total_elapsed_time). Why better isolation level gives better performance? This is a development machine and no concurrency whatsoever happens. I would expect READUNCOMMITTED to be the fasted due to less locking overhead.

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  • How to measure the time HTTP requests spend sitting in the accept-queue?

    - by David Jones
    I am using Apache2 on Ubuntu 9.10, and I am trying to tune my configuration for a web application to reduce latency of responses to HTTP requests. During a moderately heavy load on my small server, there are 24 apache2 processes handling requests. Additional requests get queued. Using "netstat", I see 24 connections are ESTABLISHED and 125 connections are TIME_WAIT. I am trying to figure out if that is considered a reasonable backlog. Most requests get serviced in a fraction of a second, so I am assuming requests move through the accept-queue fairly quickly, probably within 1 or 2 seconds, but I would like to be more certain. Can anyone recommend an easy way to measure the time an HTTP request sits in the accept-queue? The suggestions I have come across so far seem to start the clock after the apache2 worker accepts the connection. I'm trying to quantify the accept-queue delay before that. thanks in advance, David Jones

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  • GCC (ld) option to strip unreferenced data/functions

    - by legends2k
    I've written an program which uses a library which has numerous functuions, but I only limited functions from it. GCC is the compiler I use. Once I've created a binary, when I used nm to see the symbols in it, it shows all the unwanted (unreferenced) functions which are never used. How do I removed those unreferenced functions and data from the executable? Is the -s option right? I'm tols that it strips all symbol table and relocation data from the binary, but does this remove the function and data too? I'm not sure on how to verify this too, since after using -s nm doesn't work since it's stripped all sym. table data too.

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  • io operations in compilers

    - by Aastha
    How are constructs of io operations handled by a compiler? Like the RTL mapping for memory related operations which is done in a compiler at the time of target code generation, where and how exactly is the same done for io operations? How are the appeoaches different for processors supporting MMIO and I/O mapped I/O? Are there any optimizations done for the io operations in compilers?

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  • Optimizing a large iteration of PHP objects (EAV-based)

    - by Aron Rotteveel
    I am currently working on a project that utilizes the EAV model. This turns out to work quite well, but like many others I am now stumbling upon some performance issues. The data set in this particular case consists of aproximately 2500 entities, each with aprox. 150 attributes. Each entity and each attribute is represented by a PHP-object. Since most parts of the application only iterate through a filtered set of entities, we have not had very large issues yet. Now, however, I am working on an algorithm that requires iteration over the entire dataset, which causes a major impact on performance. This information is perhaps not very much to work with, but since this is an architectural problem, I am hoping for a architectural pattern to help me on the way as well. Each entity, including it's attributes takes up aprox. 500KB of memory.

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  • CSS files that don't end with .css

    - by Yongho
    Is there a disadvantage to using a dynamic Python file to generate the CSS for a webpage? I'd like computers with an administrator cookie to show special admin panel CSS, and show regular CSS for all other users. I'm planning to use: <link rel="stylesheet" href="/css.py" type="text/css" />

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  • speeding up website load using multiple servers/domains

    - by Mohammad
    When Yahoo! developer guide says "Deploying your content across multiple, geographically dispersed servers will make your pages load faster from the user's perspective". And as an explanation I read somewhere, that browsers will load up to 5 things simultaneously from the same domain. Would a subdomain, for example cdn.example.com be considered a new domain, in the previous statement?

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  • Bubble sort algorithm implementations (Haskell vs. C)

    - by kingping
    Hello. I have written 2 implementation of bubble sort algorithm in C and Haskell. Haskell implementation: module Main where main = do contents <- readFile "./data" print "Data loaded. Sorting.." let newcontents = bubblesort contents writeFile "./data_new_ghc" newcontents print "Sorting done" bubblesort list = sort list [] False rev = reverse -- separated. To see rev2 = reverse -- who calls the routine sort (x1:x2:xs) acc _ | x1 > x2 = sort (x1:xs) (x2:acc) True sort (x1:xs) acc flag = sort xs (x1:acc) flag sort [] acc True = sort (rev acc) [] False sort _ acc _ = rev2 acc I've compared these two implementations having run both on file with size of 20 KiB. C implementation took about a second, Haskell — about 1 min 10 sec. I have also profiled the Haskell application: Compile for profiling: C:\Temp ghc -prof -auto-all -O --make Main Profile: C:\Temp Main.exe +RTS -p and got these results. This is a pseudocode of the algorithm: procedure bubbleSort( A : list of sortable items ) defined as: do swapped := false for each i in 0 to length(A) - 2 inclusive do: if A[i] > A[i+1] then swap( A[i], A[i+1] ) swapped := true end if end for while swapped end procedure I wonder if it's possible to make Haskell implementation work faster without changing the algorithm (there's are actually a few tricks to make it work faster, but neither implementations have these optimizations)

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  • Datastore performance, my code or the datastore latency

    - by fredrik
    I had for the last month a bit of a problem with a quite basic datastore query. It involves 2 db.Models with one referring to the other with a db.ReferenceProperty. The problem is that according to the admin logs the request takes about 2-4 seconds to complete. I strip it down to a bare form and a list to display the results. The put works fine, but the get accumulates (in my opinion) way to much cpu time. #The get look like this: outputData['items'] = {} labelsData = Label.all() for label in labelsData: labelItem = label.item.name if labelItem not in outputData['items']: outputData['items'][labelItem] = { 'item' : labelItem, 'labels' : [] } outputData['items'][labelItem]['labels'].append(label.text) path = os.path.join(os.path.dirname(__file__), 'index.html') self.response.out.write(template.render(path, outputData)) #And the models: class Item(db.Model): name = db.StringProperty() class Label(db.Model): text = db.StringProperty() lang = db.StringProperty() item = db.ReferenceProperty(Item) I've tried to make it a number of different way ie. instead of ReferenceProperty storing all Label keys in the Item Model as a db.ListProperty. My test data is just 10 rows in Item and 40 in Label. So my questions: Is it a fools errand to try to optimize this since the high cpu usage is due to the problems with the datastore or have I just screwed up somewhere in the code? ..fredrik

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  • Does query plan optimizer works well with joined/filtered table-valued functions?

    - by smoothdeveloper
    In SQLSERVER 2005, I'm using table-valued function as a convenient way to perform arbitrary aggregation on subset data from large table (passing date range or such parameters). I'm using theses inside larger queries as joined computations and I'm wondering if the query plan optimizer work well with them in every condition or if I'm better to unnest such computation in my larger queries. Does query plan optimizer unnest table-valued functions if it make sense? If it doesn't, what do you recommend to avoid code duplication that would occur by manually unnesting them? If it does, how do you identify that from the execution plan? code sample: create table dbo.customers ( [key] uniqueidentifier , constraint pk_dbo_customers primary key ([key]) ) go /* assume large amount of data */ create table dbo.point_of_sales ( [key] uniqueidentifier , customer_key uniqueidentifier , constraint pk_dbo_point_of_sales primary key ([key]) ) go create table dbo.product_ranges ( [key] uniqueidentifier , constraint pk_dbo_product_ranges primary key ([key]) ) go create table dbo.products ( [key] uniqueidentifier , product_range_key uniqueidentifier , release_date datetime , constraint pk_dbo_products primary key ([key]) , constraint fk_dbo_products_product_range_key foreign key (product_range_key) references dbo.product_ranges ([key]) ) go . /* assume large amount of data */ create table dbo.sales_history ( [key] uniqueidentifier , product_key uniqueidentifier , point_of_sale_key uniqueidentifier , accounting_date datetime , amount money , quantity int , constraint pk_dbo_sales_history primary key ([key]) , constraint fk_dbo_sales_history_product_key foreign key (product_key) references dbo.products ([key]) , constraint fk_dbo_sales_history_point_of_sale_key foreign key (point_of_sale_key) references dbo.point_of_sales ([key]) ) go create function dbo.f_sales_history_..snip.._date_range ( @accountingdatelowerbound datetime, @accountingdateupperbound datetime ) returns table as return ( select pos.customer_key , sh.product_key , sum(sh.amount) amount , sum(sh.quantity) quantity from dbo.point_of_sales pos inner join dbo.sales_history sh on sh.point_of_sale_key = pos.[key] where sh.accounting_date between @accountingdatelowerbound and @accountingdateupperbound group by pos.customer_key , sh.product_key ) go -- TODO: insert some data -- this is a table containing a selection of product ranges declare @selectedproductranges table([key] uniqueidentifier) -- this is a table containing a selection of customers declare @selectedcustomers table([key] uniqueidentifier) declare @low datetime , @up datetime -- TODO: set top query parameters . select saleshistory.customer_key , saleshistory.product_key , saleshistory.amount , saleshistory.quantity from dbo.products p inner join @selectedproductranges productrangeselection on p.product_range_key = productrangeselection.[key] inner join @selectedcustomers customerselection on 1 = 1 inner join dbo.f_sales_history_..snip.._date_range(@low, @up) saleshistory on saleshistory.product_key = p.[key] and saleshistory.customer_key = customerselection.[key] I hope the sample makes sense. Much thanks for your help!

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  • Efficient algorithm for creating an ideal distribution of groups into containers?

    - by Inshim
    I have groups of students that need to be allocated into classrooms of a fixed capacity (say, 100 chairs in each). Each group must only be allocated to a single classroom, even if it is larger than the capacity (ie there can be an overflow, with students standing up) I need an algorithm to make the allocations with minimum overflows and under-capacity classrooms. A naive algorithm to do this allocation is horrendously slow when having ~200 groups, with a distribution of about half of them being under 20% of the classroom size. Any ideas where I can find at least some good starting point for making this algorithm lightning fast? Thanks!

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  • Why index_merge is not used here?

    - by user198729
    Setup: mysql> create table t(a integer unsigned,b integer unsigned); mysql> insert into t(a,b) values (1,2),(1,3),(2,4); mysql> create index i_t_a on t(a); mysql> create index i_t_b on t(b); mysql> explain select * from t where a=1 or b=4; +----+-------------+-------+------+---------------+------+---------+------+------+-------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-------+------+---------------+------+---------+------+------+-------------+ | 1 | SIMPLE | t | ALL | i_t_a,i_t_b | NULL | NULL | NULL | 3 | Using where | +----+-------------+-------+------+---------------+------+---------+------+------+-------------+ Is there something I'm missing?

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  • Is it possible to implement bitwise operators using integer arithmetic?

    - by Statement
    Hello World! I am facing a rather peculiar problem. I am working on a compiler for an architecture that doesn't support bitwise operations. However, it handles signed 16 bit integer arithmetics and I was wondering if it would be possible to implement bitwise operations using only: Addition (c = a + b) Subtraction (c = a - b) Division (c = a / b) Multiplication (c = a * b) Modulus (c = a % b) Minimum (c = min(a, b)) Maximum (c = max(a, b)) Comparisons (c = (a < b), c = (a == b), c = (a <= b), et.c.) Jumps (goto, for, et.c.) The bitwise operations I want to be able to support are: Or (c = a | b) And (c = a & b) Xor (c = a ^ b) Left Shift (c = a << b) Right Shift (c = a b) (All integers are signed so this is a problem) Signed Shift (c = a b) One's Complement (a = ~b) (Already found a solution, see below) Normally the problem is the other way around; how to achieve arithmetic optimizations using bitwise hacks. However not in this case. Writable memory is very scarce on this architecture, hence the need for bitwise operations. The bitwise functions themselves should not use a lot of temporary variables. However, constant read-only data & instruction memory is abundant. A side note here also is that jumps and branches are not expensive and all data is readily cached. Jumps cost half the cycles as arithmetic (including load/store) instructions do. On other words, all of the above supported functions cost twice the cycles of a single jump. Some thoughts that might help: I figured out that you can do one's complement (negate bits) with the following code: // Bitwise one's complement b = ~a; // Arithmetic one's complement b = -1 - a; I also remember the old shift hack when dividing with a power of two so the bitwise shift can be expressed as: // Bitwise left shift b = a << 4; // Arithmetic left shift b = a * 16; // 2^4 = 16 // Signed right shift b = a >>> 4; // Arithmetic right shift b = a / 16; For the rest of the bitwise operations I am slightly clueless. I wish the architects of this architecture would have supplied bit-operations. I would also like to know if there is a fast/easy way of computing the power of two (for shift operations) without using a memory data table. A naive solution would be to jump into a field of multiplications: b = 1; switch (a) { case 15: b = b * 2; case 14: b = b * 2; // ... exploting fallthrough (instruction memory is magnitudes larger) case 2: b = b * 2; case 1: b = b * 2; } Or a Set & Jump approach: switch (a) { case 15: b = 32768; break; case 14: b = 16384; break; // ... exploiting the fact that a jump is faster than one additional mul // at the cost of doubling the instruction memory footprint. case 2: b = 4; break; case 1: b = 2; break; }

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  • Permutations of Varying Size

    - by waiwai933
    I'm trying to write a function in PHP that gets all permutations of all possible sizes. I think an example would be the best way to start off: $my_array = array(1,1,2,3); Possible permutations of varying size: 1 1 // * See Note 2 3 1,1 1,2 1,3 // And so forth, for all the sets of size 2 1,1,2 1,1,3 1,2,1 // And so forth, for all the sets of size 3 1,1,2,3 1,1,3,2 // And so forth, for all the sets of size 4 Note: I don't care if there's a duplicate or not. For the purposes of this example, all future duplicates have been omitted. What I have so far in PHP: function getPermutations($my_array){ $permutation_length = 1; $keep_going = true; while($keep_going){ while($there_are_still_permutations_with_this_length){ // Generate the next permutation and return it into an array // Of course, the actual important part of the code is what I'm having trouble with. } $permutation_length++; if($permutation_length>count($my_array)){ $keep_going = false; } else{ $keep_going = true; } } return $return_array; } The closest thing I can think of is shuffling the array, picking the first n elements, seeing if it's already in the results array, and if it's not, add it in, and then stop when there are mathematically no more possible permutations for that length. But it's ugly and resource-inefficient. Any pseudocode algorithms would be greatly appreciated. Also, for super-duper (worthless) bonus points, is there a way to get just 1 permutation with the function but make it so that it doesn't have to recalculate all previous permutations to get the next? For example, I pass it a parameter 3, which means it's already done 3 permutations, and it just generates number 4 without redoing the previous 3? (Passing it the parameter is not necessary, it could keep track in a global or static). The reason I ask this is because as the array grows, so does the number of possible combinations. Suffice it to say that one small data set with only a dozen elements grows quickly into the trillions of possible combinations and I don't want to task PHP with holding trillions of permutations in its memory at once.

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  • How to improve my LDAP schema?

    - by asmaier
    Hello, I have a OpenLDAP Database and it holds some project objects that look like dn: cn=Proj1,ou=Project,ou=ua,dc=org cn: Proj1 objectClass: top objectClass: posixGroup member: 001ag member: 002ag System: ABEL System: PCx Budget: ABEL:1000000:0.3 Budget: PCx:300000:0.3 One can see that the Budget attribute is a ":"-separated string, where the first part holds the name of the system the budget is for, the second part holds some budget (which may change every month) and the last entry is a conversion factor for the budget of that system. Seeing this, I thought this is bad database design, since attribute values should always be atomic. But how can I improve that in LDAP, so that I can do a direct ldapsearch or a direct ldapmodify of the budget of System "ABEL" instead of writing a script, that will have to parse and split the ":"-separated string?

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  • I need some help optimizing my database schema

    - by Steffan
    Here's a layout of my data: Heading 1: Sub heading Sub heading Sub heading Sub heading Sub heading Heading 2: Sub heading Sub heading Sub heading Sub heading Sub heading Heading 3: Sub heading Sub heading Sub heading Sub heading Sub heading Heading 4: Sub heading Sub heading Sub heading Sub heading Sub heading Heading 5: Sub heading Sub heading Sub heading Sub heading Sub heading These headings need to have a 'Completion Status' boolean value which gets linked to a user Id. Currently, this is how my table looks: id | userID | field_1 | field_2 | field_3 | field_4 | etc... ----------------------------------------------------------------------- 1 | 1 | 0 | 0 | 1 | 0 | ----------------------------------------------------------------------- 2 | 2 | 1 | 0 | 1 | 1 | Each field represents one Sub Heading. Having this many columns in my table looks awfully inefficient... How can I go about optimizing this? I can't think of any way to neaten it up :/

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  • Date arithmetic using integer values

    - by Dave Jarvis
    Problem String concatenation is slowing down a query: date(extract(YEAR FROM m.taken)||'-1-1') d1, date(extract(YEAR FROM m.taken)||'-1-31') d2 This is realized in code as part of a string, which follows (where the p_ variables are integers): date(extract(YEAR FROM m.taken)||''-'||p_month1||'-'||p_day1||''') d1, date(extract(YEAR FROM m.taken)||''-'||p_month2||'-'||p_day2||''') d2 This part of the query runs in 3.2 seconds with the dates, and 1.5 seconds without, leading me to believe there is ample room for improvement. Question What is a better way to create the date (presumably without concatenation)? Many thanks!

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