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  • Using XCode and instruments to improve iPhone app performance

    - by MrDatabase
    I've been experimenting with Instruments off and on for a while and and I still can't do the following (with any sensible results): determine or estimate the average runtime of a function that's called many times. For example if I'm driving my gameLoop at 60 Hz with a CADisplayLink I'd like to see how long the loop takes to run on average... 10 ms? 30 ms etc. I've come close with the "CPU activity" instrument but the results are inconsistent or don't make sense. The time profiler seems promising but all I can get is "% of runtime"... and I'd like an actual runtime.

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  • High performance querying - Sugestions please

    - by Alex Takitani
    Supposing that I have millions of user profiles, with hundreds of fields (name, gender, preferred pet and so on...). With database would You choose? Suppose that You have a Facebook like load. Speed is a must. Open Source preferred. I've read a lot about Cassandra, HBase, Mongo, Mysql... I just can't decide.....

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  • Performance issues with testing on an ADP2

    - by Stuart
    I have an Android Developer Phone with Android 1.6 installed, sometimes it will take 30 seconds for the home screen to appear after a call or exiting from an application. Why is my phone so slow? Should I replace the memory card? Also, when is the 2.0 coming out for the ADP2? How do I install it?

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  • Vb.exe performance time

    - by vinodacharyabva
    Hi I am running a vb.exe through automation. In exe I have return a code which takes a data from database and saves that data into file. I ran that .exe for the first time. It took 1 mins. For testing baseline I called same .exe 5 times one after the other. But it took nearly 10 mins to generate. My question is if it takes 1 min for 1 report to generate then it should take 5 mins to generate 5 report but why it is taking 10 mins (more than the double). Is there any problem while calling a exe one after the other?

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  • Horrible eclipse performance on macbook pro running 10.5.8

    - by user246114
    Hi I am using eclipse galileo on my macbook pro. After a few minutes it starts dragging really badly, like it takes 8 seconds to open a file. I don't have many files open at all. I already modified the config file to increase ram and all that stuff. Is there something wrong with this version of eclipse, never had it run so poorly on here, Thanks

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  • Horrible eclipse performance on macbook pro running 10.5.8

    - by user246114
    Hi I am using eclipse galileo on my macbook pro. After a few minutes it starts dragging really badly, like it takes 8 seconds to open a file. I don't have many files open at all. I already modified the config file to increase ram and all that stuff. Is there something wrong with this version of eclipse, never had it run so poorly on here, Thanks

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  • MonoTouch - foreach vs for loops (performance)

    - by ifwdev
    Normally I'm well aware that a consideration like this is premature optimization. Right now I have some event handlers being attached inside a foreach loop. I am wondering if this style might be prone to leaks or inefficient memory use due to closures being created. Is there any validity to this thinking?

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  • optimizing any OS for maximum informix client/server performance

    - by Frank Developer
    Is there any Informix documentation for optimizing any operating system where an ifx engine is running? For example, in Linux, strip-down to a bare minimum all unnecessary binaries, daemons, utilities, tune kernel parameters, optimize raw and cooked devices (hdparm), place swap space on beginning tracks of a disk, etc. Someday, maybe, Informix can create its own proprietary and dedicated PICK-like O/S to provide the most optimized environment for a standalone ifx server? The general idea is for the OS where ifx sits on have the smallest footprint and lowest overhead impact.

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  • Improve performance of sorting files by extension

    - by DxCK
    With a given array of file names, the most simpliest way to sort it by file extension is like this: Array.Sort(fileNames, (x, y) => Path.GetExtension(x).CompareTo(Path.GetExtension(y))); The problem is that on very long list (~800k) it takes very long to sort, while sorting by the whole file name is faster for a couple of seconds! Theoretical, there is a way to optimize it: instead of using Path.GetExtension() and compare the newly created extension-only-strings, we can provide a Comparison than compares starting from the LastIndexOf('.') without creating new strings. Now, suppose i found the LastIndexOf('.'), i want to reuse native .NET's StringComparer and apply it only to the part on string after the LastIndexOf('.'). Didn't found a way to do that. Any ideas?

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  • SQL Server CTE referred in self joins slow

    - by Kharlos Dominguez
    Hello, I have written a table-valued UDF that starts by a CTE to return a subset of the rows from a large table. There are several joins in the CTE. A couple of inner and one left join to other tables, which don't contain a lot of rows. The CTE has a where clause that returns the rows within a date range, in order to return only the rows needed. I'm then referencing this CTE in 4 self left joins, in order to build subtotals using different criterias. The query is quite complex but here is a simplified pseudo-version of it WITH DataCTE as ( SELECT [columns] FROM table INNER JOIN table2 ON [...] INNER JOIN table3 ON [...] LEFT JOIN table3 ON [...] ) SELECT [aggregates_columns of each subset] FROM DataCTE Main LEFT JOIN DataCTE BananasSubset ON [...] AND Product = 'Bananas' AND Quality = 100 LEFT JOIN DataCTE DamagedBananasSubset ON [...] AND Product = 'Bananas' AND Quality < 20 LEFT JOIN DataCTE MangosSubset ON [...] GROUP BY [ I have the feeling that SQL Server gets confused and calls the CTE for each self join, which seems confirmed by looking at the execution plan, although I confess not being an expert at reading those. I would have assumed SQL Server to be smart enough to only perform the data retrieval from the CTE only once, rather than do it several times. I have tried the same approach but rather than using a CTE to get the subset of the data, I used the same select query as in the CTE, but made it output to a temp table instead. The version referring the CTE version takes 40 seconds. The version referring the temp table takes between 1 and 2 seconds. Why isn't SQL Server smart enough to keep the CTE results in memory? I like CTEs, especially in this case as my UDF is a table-valued one, so it allowed me to keep everything in a single statement. To use a temp table, I would need to write a multi-statement table valued UDF, which I find a slightly less elegant solution. Did some of you had this kind of performance issues with CTE, and if so, how did you get them sorted? Thanks, Kharlos

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  • vectorizing loops in Matlab - performance issues

    - by Gacek
    This question is related to these two: http://stackoverflow.com/questions/2867901/introduction-to-vectorizing-in-matlab-any-good-tutorials http://stackoverflow.com/questions/2561617/filter-that-uses-elements-from-two-arrays-at-the-same-time Basing on the tutorials I read, I was trying to vectorize some procedure that takes really a lot of time. I've rewritten this: function B = bfltGray(A,w,sigma_r) dim = size(A); B = zeros(dim); for i = 1:dim(1) for j = 1:dim(2) % Extract local region. iMin = max(i-w,1); iMax = min(i+w,dim(1)); jMin = max(j-w,1); jMax = min(j+w,dim(2)); I = A(iMin:iMax,jMin:jMax); % Compute Gaussian intensity weights. F = exp(-0.5*(abs(I-A(i,j))/sigma_r).^2); B(i,j) = sum(F(:).*I(:))/sum(F(:)); end end into this: function B = rngVect(A, w, sigma) W = 2*w+1; I = padarray(A, [w,w],'symmetric'); I = im2col(I, [W,W]); H = exp(-0.5*(abs(I-repmat(A(:)', size(I,1),1))/sigma).^2); B = reshape(sum(H.*I,1)./sum(H,1), size(A, 1), []); But this version seems to be as slow as the first one, but in addition it uses a lot of memory and sometimes causes memory problems. I suppose I've made something wrong. Probably some logic mistake regarding vectorizing. Well, in fact I'm not surprised - this method creates really big matrices and probably the computations are proportionally longer. I have also tried to write it using nlfilter (similar to the second solution given by Jonas) but it seems to be hard since I use Matlab 6.5 (R13) (there are no sophisticated function handles available). So once again, I'm asking not for ready solution, but for some ideas that would help me to solve this in reasonable time. Maybe you will point me what I did wrong.

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  • JavaScript tags, performance and W3C

    - by Thomas
    Today I was looking for website optimization content and I found an article talking about move JavaScript scripts to the bottom of the HTML page. Is this valid with W3C's recommendations? I learned that all JavaScript must be inside of head tag... Thank you.

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  • real time stock quotes, StreamReader performance optimization

    - by sean717
    I am working on a program that extracts real time quote for 900+ stocks from a website. I use HttpWebRequest to send HTTP request to the site and store the response to a stream and open a stream using the following code: HttpWebResponse response = (HttpWebResponse)request.GetResponse(); Stream stream = response.GetResponseStream (); StreamReader reader = new StreamReader( stream ) the size of the received HTML is large (5000+ lines), so it takes a long time to parse it and extract the price. For 900 files, It takes about 6 mins for parsing and extracting. Which my boss isn't happy with, he told me he'd want the whole process to be done in TWO mins. I've identified the part of the program that takes most of time to finish is parsing and extracting. I've tried to optimize the code to make it faster, the following is what I have now after some optimization: // skip lines at the top for(int i=0;i<1500;++i) reader.ReadLine(); // read the line that contains the price string theLine = reader.ReadLine(); // ... extract the price from the line now it takes about 4 mins to process all the files, there is still a significant gap to what my boss's expecting. So I am wondering, is there other way that I can further speed up the parsing and extracting and have everything done within 2 mins?

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  • Mysql regexp performance question

    - by Tim
    Rumour has it that this; SELECT * FROM lineage_string where lineage like '%179%' and lineage regexp '(^|/)179(/|$)' Would be faster than this; SELECT * FROM lineage_string where lineage regexp '(^|/)179(/|$)' Can anyone confirm ? Or know a decent way to test the speed of such queries. Thanks

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  • how to avoid sub-query to gain performance

    - by chun
    hi i have a reporting query which have 2 long sub-query SELECT r1.code_centre, r1.libelle_centre, r1.id_equipe, r1.equipe, r1.id_file_attente, r1.libelle_file_attente,r1.id_date, r1.tranche, r1.id_granularite_de_periode,r1.granularite, r1.ContactsTraites, r1.ContactsenParcage, r1.ContactsenComm, r1.DureeTraitementContacts, r1.DureeComm, r1.DureeParcage, r2.AgentsConnectes, r2.DureeConnexion, r2.DureeTraitementAgents, r2.DureePostTraitement FROM ( SELECT cc.id_centre_contact, cc.code_centre, cc.libelle_centre, a.id_equipe, a.equipe, a.id_file_attente, f.libelle_file_attente, a.id_date, g.tranche, g.id_granularite_de_periode, g.granularite, sum(Nb_Contacts_Traites) as ContactsTraites, sum(Nb_Contacts_en_Parcage) as ContactsenParcage, sum(Nb_Contacts_en_Communication) as ContactsenComm, sum(Duree_Traitement/1000) as DureeTraitementContacts, sum(Duree_Communication / 1000 + Duree_Conference / 1000 + Duree_Com_Interagent / 1000) as DureeComm, sum(Duree_Parcage/1000) as DureeParcage FROM agr_synthese_activite_media_fa_agent a, centre_contact cc, direction_contact dc, granularite_de_periode g, media m, file_attente f WHERE m.id_media = a.id_media AND cc.id_centre_contact = a.id_centre_contact AND a.id_direction_contact = dc.id_direction_contact AND dc.direction_contact ='INCOMING' AND a.id_file_attente = f.id_file_attente AND m.media = 'PHONE' AND ( ( g.valeur_min = date_format(a.id_date,'%d/%m') and g.granularite = 'Jour') or ( g.granularite = 'Heure' and a.id_th_heure = g.id_granularite_de_periode) ) GROUP by cc.id_centre_contact, a.id_equipe, a.id_file_attente, a.id_date, g.tranche, g.id_granularite_de_periode) r1, ( (SELECT cc.id_centre_contact,cc.code_centre, cc.libelle_centre, a.id_equipe, a.equipe, a.id_date, g.tranche, g.id_granularite_de_periode,g.granularite, count(distinct a.id_agent) as AgentsConnectes, sum(Duree_Connexion / 1000) as DureeConnexion, sum(Duree_en_Traitement / 1000) as DureeTraitementAgents, sum(Duree_en_PostTraitement / 1000) as DureePostTraitement FROM activite_agent a, centre_contact cc, granularite_de_periode g WHERE ( g.valeur_min = date_format(a.id_date,'%d/%m') and g.granularite = 'Jour') AND cc.id_centre_contact = a.id_centre_contact GROUP BY cc.id_centre_contact, a.id_equipe, a.id_date, g.tranche, g.id_granularite_de_periode ) UNION (SELECT cc.id_centre_contact,cc.code_centre, cc.libelle_centre, a.id_equipe, a.equipe, a.id_date, g.tranche, g.id_granularite_de_periode,g.granularite, count(distinct a.id_agent) as AgentsConnectes, sum(Duree_Connexion / 1000) as DureeConnexion, sum(Duree_en_Traitement / 1000) as DureeTraitementAgents, sum(Duree_en_PostTraitement / 1000) as DureePostTraitement FROM activite_agent a, centre_contact cc, granularite_de_periode g WHERE ( g.granularite = 'Heure' AND a.id_th_heure = g.id_granularite_de_periode) AND cc.id_centre_contact = a.id_centre_contact GROUP BY cc.id_centre_contact,a.id_equipe, a.id_date, g.tranche, g.id_granularite_de_periode) ) r2 WHERE r1.id_centre_contact = r2.id_centre_contact AND r1.id_equipe = r2.id_equipe AND r1.id_date = r2.id_date AND r1.tranche = r2.tranche AND r1.id_granularite_de_periode = r2.id_granularite_de_periode GROUP BY r1.id_centre_contact , r1.id_equipe, r1.id_file_attente, r1.id_date, r1.tranche, r1.id_granularite_de_periode ORDER BY r1.code_centre, r1.libelle_centre, r1.equipe, r1.libelle_file_attente, r1.id_date, r1.id_granularite_de_periode,r1.tranche the EXPLAIN shows | id | select_type | table | type| possible_keys | key | key_len | ref| rows | Extra | '1', 'PRIMARY', '<derived3>', 'ALL', NULL, NULL, NULL, NULL, '2520', 'Using temporary; Using filesort' '1', 'PRIMARY', '<derived2>', 'ALL', NULL, NULL, NULL, NULL, '4378', 'Using where; Using join buffer' '3', 'DERIVED', 'a', 'ALL', 'fk_Activite_Agent_centre_contact', NULL, NULL, NULL, '83433', 'Using temporary; Using filesort' '3', 'DERIVED', 'g', 'ref', 'Index_granularite,Index_Valeur_min', 'Index_Valeur_min', '23', 'func', '1', 'Using where' '3', 'DERIVED', 'cc', 'ALL', 'PRIMARY', NULL, NULL, NULL, '6', 'Using where; Using join buffer' '4', 'UNION', 'g', 'ref', 'PRIMARY,Index_granularite', 'Index_granularite', '23', '', '24', 'Using where; Using temporary; Using filesort' '4', 'UNION', 'a', 'ref', 'fk_Activite_Agent_centre_contact,fk_activite_agent_TH_heure', 'fk_activite_agent_TH_heure', '5', 'reporting_acd.g.Id_Granularite_de_periode', '2979', 'Using where' '4', 'UNION', 'cc', 'ALL', 'PRIMARY', NULL, NULL, NULL, '6', 'Using where; Using join buffer' NULL, 'UNION RESULT', '<union3,4>', 'ALL', NULL, NULL, NULL, NULL, NULL, '' '2', 'DERIVED', 'g', 'range', 'PRIMARY,Index_granularite,Index_Valeur_min', 'Index_granularite', '23', NULL, '389', 'Using where; Using temporary; Using filesort' '2', 'DERIVED', 'a', 'ALL', 'fk_agr_synthese_activite_media_fa_agent_centre_contact,fk_agr_synthese_activite_media_fa_agent_direction_contact,fk_agr_synthese_activite_media_fa_agent_file_attente,fk_agr_synthese_activite_media_fa_agent_media,fk_agr_synthese_activite_media_fa_agent_th_heure', NULL, NULL, NULL, '20903', 'Using where; Using join buffer' '2', 'DERIVED', 'cc', 'eq_ref', 'PRIMARY', 'PRIMARY', '4', 'reporting_acd.a.Id_Centre_Contact', '1', '' '2', 'DERIVED', 'f', 'eq_ref', 'PRIMARY', 'PRIMARY', '4', 'reporting_acd.a.Id_File_Attente', '1', '' '2', 'DERIVED', 'dc', 'eq_ref', 'PRIMARY', 'PRIMARY', '4', 'reporting_acd.a.Id_Direction_Contact', '1', 'Using where' '2', 'DERIVED', 'm', 'eq_ref', 'PRIMARY', 'PRIMARY', '4', 'reporting_acd.a.Id_Media', '1', 'Using where' don't know it very clear, but i think is the problem of seems it take full scaning than i change all the sub-query to views(create view as select sub-query), and the result is the same thanks for any advice

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  • list or container O(1)-ish insertion/deletion performance, with array semantics

    - by Chris Kaminski
    I'm looking for a collection that offers list semantics, but also allows array semantics. Say I have a list with the following items: apple orange carrot pear then my container array would: container[0] == apple container[1] == orangle container[2] == carrot Then say I delete the orange element: container[0] == apple container[1] == carrot I don't particularly care if sort order is maintained, I'd just like the array values to function as accelerators to the list items, and I want to collapse gaps in the array without having to do an explicit resizing.

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  • Mysql select - improve performances

    - by realshadow
    Hey, I am working on an e-shop which sells products only via loans. I display 10 products per page in any category, each product has 3 different price tags - 3 different loan types. Everything went pretty well during testing time, query execution time was perfect, but today when transfered the changes to the production server, the site "collapsed" in about 2 minutes. The query that is used to select loan types sometimes hangs for ~10 seconds and it happens frequently and thus it cant keep up and its hella slow. The table that is used to store the data has approximately 2 milion records and each select looks like this: SELECT * FROM products_loans WHERE KOD IN("X17/Q30-10", "X17/12", "X17/5-24") AND 369.27 BETWEEN CENA_OD AND CENA_DO; 3 loan types and the price that needs to be in range between CENA_OD and CENA_DO, thus 3 rows are returned. But since I need to display 10 products per page, I need to run it trough a modified select using OR, since I didnt find any other solution to this. I have asked about it here, but got no answer. As mentioned in the referencing post, this has to be done separately since there is no column that could be used in a join (except of course price and code, but that ended very, very badly). Here is the show create table, kod and CENA_OD/CENA_DO very indexed via INDEX. CREATE TABLE `products_loans` ( `KOEF_ID` bigint(20) NOT NULL, `KOD` varchar(30) NOT NULL, `AKONTACIA` int(11) NOT NULL, `POCET_SPLATOK` int(11) NOT NULL, `koeficient` decimal(10,2) NOT NULL default '0.00', `CENA_OD` decimal(10,2) default NULL, `CENA_DO` decimal(10,2) default NULL, `PREDAJNA_CENA` decimal(10,2) default NULL, `AKONTACIA_SUMA` decimal(10,2) default NULL, `TYP_VYHODY` varchar(4) default NULL, `stage` smallint(6) NOT NULL default '1', PRIMARY KEY (`KOEF_ID`), KEY `CENA_OD` (`CENA_OD`), KEY `CENA_DO` (`CENA_DO`), KEY `KOD` (`KOD`), KEY `stage` (`stage`) ) ENGINE=InnoDB DEFAULT CHARSET=utf8 And also selecting all loan types and later filtering them trough php doesnt work good, since each type has over 50k records and the select takes too much time as well... Any ides about improving the speed are appreciated. Edit: Here is the explain +----+-------------+----------------+-------+---------------------+------+---------+------+--------+-------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+----------------+-------+---------------------+------+---------+------+--------+-------------+ | 1 | SIMPLE | products_loans | range | CENA_OD,CENA_DO,KOD | KOD | 92 | NULL | 190158 | Using where | +----+-------------+----------------+-------+---------------------+------+---------+------+--------+-------------+ I have tried the combined index and it improved the performance on the test server from 0.44 sec to 0.06 sec, I cant access the production server from home though, so I will have to try it tomorrow.

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  • Stopping cookies being set from a domain (aka "cookieless domain") to increase site performance

    - by Django Reinhardt
    I was reading in Google's documentation about improving site speed. One of their recommendations is serving static content (images, css, js, etc.) from a "cookieless domain": Static content, such as images, JS and CSS files, don't need to be accompanied by cookies, as there is no user interaction with these resources. You can decrease request latency by serving static resources from a domain that doesn't serve cookies. Google then says that the best way to do this is to buy a new domain and set it to point to your current one: To reserve a cookieless domain for serving static content, register a new domain name and configure your DNS database with a CNAME record that points the new domain to your existing domain A record. Configure your web server to serve static resources from the new domain, and do not allow any cookies to be set anywhere on this domain. In your web pages, reference the domain name in the URLs for the static resources. This is pretty straight forward stuff, except for the bit where it says to "configure your web server to serve static resources from the new domain, and do not allow any cookies to be set anywhere on this domain". From what I've read, there's no setting in IIS that allows you to say "serve static resources", so how do I prevent ASP.NET from setting cookies on this new domain? At present, even if I'm just requesting a .jpg from the new domain, it sets a cookie on my browser, even though our application's cookies are set to our old domain. For example, ASP.NET sets an ".ASPXANONYMOUS" cookie that (as far as I'm aware) we're not telling it to do. Apologies if this is a real newb question, I'm new at this! Thanks.

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  • pyInotify performance

    - by tranimatronic
    I have a very large directory tree I am wanting pyInotify to watch. Is it better to have pyInotify watch the entire tree or is it better to have a number of watches reporting changes to specific files ? Thanks

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  • Efficient file buffering & scanning methods for large files in python

    - by eblume
    The description of the problem I am having is a bit complicated, and I will err on the side of providing more complete information. For the impatient, here is the briefest way I can summarize it: What is the fastest (least execution time) way to split a text file in to ALL (overlapping) substrings of size N (bound N, eg 36) while throwing out newline characters. I am writing a module which parses files in the FASTA ascii-based genome format. These files comprise what is known as the 'hg18' human reference genome, which you can download from the UCSC genome browser (go slugs!) if you like. As you will notice, the genome files are composed of chr[1..22].fa and chr[XY].fa, as well as a set of other small files which are not used in this module. Several modules already exist for parsing FASTA files, such as BioPython's SeqIO. (Sorry, I'd post a link, but I don't have the points to do so yet.) Unfortunately, every module I've been able to find doesn't do the specific operation I am trying to do. My module needs to split the genome data ('CAGTACGTCAGACTATACGGAGCTA' could be a line, for instance) in to every single overlapping N-length substring. Let me give an example using a very small file (the actual chromosome files are between 355 and 20 million characters long) and N=8 import cStringIO example_file = cStringIO.StringIO("""\ header CAGTcag TFgcACF """) for read in parse(example_file): ... print read ... CAGTCAGTF AGTCAGTFG GTCAGTFGC TCAGTFGCA CAGTFGCAC AGTFGCACF The function that I found had the absolute best performance from the methods I could think of is this: def parse(file): size = 8 # of course in my code this is a function argument file.readline() # skip past the header buffer = '' for line in file: buffer += line.rstrip().upper() while len(buffer) = size: yield buffer[:size] buffer = buffer[1:] This works, but unfortunately it still takes about 1.5 hours (see note below) to parse the human genome this way. Perhaps this is the very best I am going to see with this method (a complete code refactor might be in order, but I'd like to avoid it as this approach has some very specific advantages in other areas of the code), but I thought I would turn this over to the community. Thanks! Note, this time includes a lot of extra calculation, such as computing the opposing strand read and doing hashtable lookups on a hash of approximately 5G in size. Post-answer conclusion: It turns out that using fileobj.read() and then manipulating the resulting string (string.replace(), etc.) took relatively little time and memory compared to the remainder of the program, and so I used that approach. Thanks everyone!

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