Search Results

Search found 20904 results on 837 pages for 'disk performance'.

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

Page 95/837 | < Previous Page | 91 92 93 94 95 96 97 98 99 100 101 102  | Next Page >

  • Which .NET performance and/or memory profilers will allow me to profile a DLL?

    - by Eric
    I write a lot of .NET based plug-ins for other programs which are usually compiled as a DLL which is up to the native application to start up. I've been using Equatec's profiler, which works great, but now would like something with more features, including the ability to profile memory usage. I tried out Red Gate's Ant Profiler, but as far as I can see there is no way to profile a DLL. The only option is to profile an EXE. So my question is what other profiling tools are available that will allow me to profile a single library DLL rather than an EXE. I'm assuming this would require injecting profile code into the library as Equatec does?

    Read the article

  • What performance indicators can I use to convince management that I need my development PC upgraded?

    - by Aaron Daniels
    At work, my PC is slow. I feel that I can be way more productive if I just wasn't waiting for Visual Studio and everything else to respond. My PC isn't bad (dual-core, 3GB of RAM), but there is a lot of corporate software and whatnot to slow everything down and sometimes lock it up. Now, some developers have begun getting Windows 7 machines with 8 GB of RAM. Of course, I start salivating at this. However, I was told that I "had to justify" why I should get a new machine. I can think of a lot of different things, but I am curious as to what every one else on SO would have to say. NOTE: Ideally, these reasons should be specifically related to .NET development in Visual Studio on a Windows machine. This isn't a "how can I make my machine faster" question.

    Read the article

  • Tips for improving performance of DB that is above size 40 GB (Sql Server 2005) and growing monthly

    - by HotTester
    The current DB or our project has crossed over 40 GB this month and on an average it is growing monthly by around 3 GB. Now all the tables are best normalized and proper indexing has been used. But still as the size is growing it is taking more time to fire even basic queries like 'select count(1) from table'. So can u share some more points that will help in this front. Database is Sql Server 2005. Further if we implement Partitioning wouldn't it create a overhead ? Thanks in advance.

    Read the article

  • Is there a performance gain from defining routes in app.yaml versus one large mapping in a WSGIAppli

    - by jgeewax
    Scenario 1 This involves using one "gateway" route in app.yaml and then choosing the RequestHandler in the WSGIApplication. app.yaml - url: /.* script: main.py main.py from google.appengine.ext import webapp class Page1(webapp.RequestHandler): def get(self): self.response.out.write("Page 1") class Page2(webapp.RequestHandler): def get(self): self.response.out.write("Page 2") application = webapp.WSGIApplication([ ('/page1/', Page1), ('/page2/', Page2), ], debug=True) def main(): wsgiref.handlers.CGIHandler().run(application) if __name__ == '__main__': main() Scenario 2: This involves defining two routes in app.yaml and then two separate scripts for each (page1.py and page2.py). app.yaml - url: /page1/ script: page1.py - url: /page2/ script: page2.py page1.py from google.appengine.ext import webapp class Page1(webapp.RequestHandler): def get(self): self.response.out.write("Page 1") application = webapp.WSGIApplication([ ('/page1/', Page1), ], debug=True) def main(): wsgiref.handlers.CGIHandler().run(application) if __name__ == '__main__': main() page2.py from google.appengine.ext import webapp class Page2(webapp.RequestHandler): def get(self): self.response.out.write("Page 2") application = webapp.WSGIApplication([ ('/page2/', Page2), ], debug=True) def main(): wsgiref.handlers.CGIHandler().run(application) if __name__ == '__main__': main() Question What are the benefits and drawbacks of each pattern? Is one much faster than the other?

    Read the article

  • Can I improve performance by refactoring SQL commands into classes?

    - by Matthew Jones
    Currently, my entire website does updating from SQL parameterized queries. It works, we've had no problems with it, but it can occasionally be very slow. I was wondering if it makes sense to refactor some of these SQL commands into classes so that we would not have to hit the database so often. I understand hitting the database is generally the slowest part of any web application For example, say we have a class structure like this: Project (comprised of) Tasks (comprised of) Assignments Where Project, Task, and Assignment are classes. At certain points in the site you are only working on one project at a time, and so creating a Project class and passing it among pages (using Session, Profile, something else) might make sense. I imagine this class would have a Save() method to save value changes. Does it make sense to invest the time into doing this? Under what conditions might it be worth it?

    Read the article

  • Drawing performance in Java 6 updates 19,20 versus Java 6 update 3 ?

    - by Pesho
    I'm getting twice the frame rate with the earlier Java 6 u 3, than with the new ones. Very weird. Can anyone give some explanation? On Core 2 Duo 1.83ghz, integrated video (only one core is used) - 1500 (older java) vs 700 fps On Athlon 64 3500+, discrete video - 120 (older java) vs 55 fps The app is a simple game with a moving rectangle. I'm using Graphics2D to draw from a loop.

    Read the article

  • Can I improve performance by refactoring SQL commands into C# classes?

    - by Matthew Jones
    Currently, my entire website does updating from SQL parameterized queries. It works, we've had no problems with it, but it can occasionally be very slow. I was wondering if it makes sense to refactor some of these SQL commands into classes so that we would not have to hit the database so often. I understand hitting the database is generally the slowest part of any web application For example, say we have a class structure like this: Project (comprised of) Tasks (comprised of) Assignments Where Project, Task, and Assignment are classes. At certain points in the site you are only working on one project at a time, and so creating a Project class and passing it among pages (using Session, Profile, something else) might make sense. I imagine this class would have a Save() method to save value changes. Does it make sense to invest the time into doing this? Under what conditions might it be worth it?

    Read the article

  • Why one loop is performing better than other memory wise as well as performance wise?

    - by Mohit
    I have following two loops in C#, and I am running these loops for a collection with 10,000 records being downloaded with paging using "yield return" First foreach(var k in collection) { repo.Save(k); } Second var collectionEnum = collection.GetEnumerator(); while (collectionEnum.MoveNext()) { var k = collectionEnum.Current; repo.Save(k); k = null; } Seems like that the second loop consumes less memory and it faster than the first loop. Memory I understand may be because of k being set to null(Even though I am not sure). But how come it is faster than for each. Following is the actual code [Test] public void BechmarkForEach_Test() { bool isFirstTimeSync = true; Func<Contact, bool> afterProcessing = contactItem => { return true; }; var contactService = CreateSerivce("/administrator/components/com_civicrm"); var contactRepo = new ContactRepository(new Mock<ILogger>().Object); contactRepo.Drop(); contactRepo = new ContactRepository(new Mock<ILogger>().Object); Profile("For Each Profiling",1,()=>{ var localenumertaor=contactService.Download(); foreach (var item in localenumertaor) { if (isFirstTimeSync) item.StateFlag = 1; item.ClientTimeStamp = DateTime.UtcNow; if (item.StateFlag == 1) contactRepo.Insert(item); else contactRepo.Update(item); afterProcessing(item); } contactRepo.DeleteAll(); }); } [Test] public void BechmarkWhile_Test() { bool isFirstTimeSync = true; Func<Contact, bool> afterProcessing = contactItem => { return true; }; var contactService = CreateSerivce("/administrator/components/com_civicrm"); var contactRepo = new ContactRepository(new Mock<ILogger>().Object); contactRepo.Drop(); contactRepo = new ContactRepository(new Mock<ILogger>().Object); var itemsCollection = contactService.Download().GetEnumerator(); Profile("While Profiling", 1, () => { while (itemsCollection.MoveNext()) { var item = itemsCollection.Current; //if First time sync then ignore and overwrite the stateflag if (isFirstTimeSync) item.StateFlag = 1; item.ClientTimeStamp = DateTime.UtcNow; if (item.StateFlag == 1) contactRepo.Insert(item); else contactRepo.Update(item); afterProcessing(item); item = null; } contactRepo.DeleteAll(); }); } static void Profile(string description, int iterations, Action func) { // clean up GC.Collect(); GC.WaitForPendingFinalizers(); GC.Collect(); // warm up func(); var watch = Stopwatch.StartNew(); for (int i = 0; i < iterations; i++) { func(); } watch.Stop(); Console.Write(description); Console.WriteLine(" Time Elapsed {0} ms", watch.ElapsedMilliseconds); } I m using the micro bench marking, from a stackoverflow question itself benchmarking-small-code The time taken is For Each Profiling Time Elapsed 5249 ms While Profiling Time Elapsed 116 ms

    Read the article

  • In Java, is there a performance gain in using interfaces for complex models?

    - by Gnoupi
    The title is hardly understandable, but I'm not sure how to summarize that another way. Any edit to clarify is welcome. I have been told, and recommended to use interfaces to improve performances, even in a case which doesn't especially call for the regular "interface" role. In this case, the objects are big models (in a MVC meaning), with many methods and fields. The "good use" that has been recommended to me is to create an interface, with its unique implementation. There won't be any other class implementing this interface, for sure. I have been told that this is better to do so, because it "exposes less" (or something close) to the other classes which will use methods from this class, as these objects are referring to the object from its interface (all public methods from the implementation being reproduced in the interface). This seems quite strange to me, as it seems like a C++ use to me (with header files). There I see the point, but in Java? Is there really a point in making an interface for such unique implementation? I would really appreciate some clarifications on the topic, so I could justify not following such kind of behavior, and the hassle it creates from duplicating all declarations. Edit: Plenty of valid points in most answers, I'm wondering if I won't switch this question for a community wiki, so we can regroup these points in more structured answers.

    Read the article

  • Which one has a faster runtime performance: WPF or Winforms?

    - by Joan Venge
    I know WPF is more complex an flexible so could be thought to do more calculations. But since the rendering is done on the GPU, wouldn't it be faster than Winforms for the same application (functionally and visually)? I mean when you are not running any games or heavy 3d rendering, the GPU isn't doing heavy work, right? Whereas the CPU is always busy. Is this a valid assumption or is the GPU utilization of WPF a very minor operation in its pipeline?

    Read the article

  • When does code bloat start having a noticeable effect on performance?

    - by Kyle
    I am looking to make a hefty shift towards templates in one of my OpenGL projects, mainly for fun and the learning experience. I plan on watching the size of the executable carefully as I do this, to see just how much of the notorious bloat happens. Currently, the size of my Release build is around 580 KB when I favor speed and 440 KB when I favor size. Yes, it's a tiny project, and in fact even if my executable bloats 10 x its size, it's still going to be 5 MB or so, which hardly seems large by today's standards... or is it? This brings me to my question. Is speed proportional to size, or are there leaps and plateaus at certain thresholds, thresholds which I should be aiming to stay below? (And if so, what are the thresholds specifically?)

    Read the article

  • How can I compare the performance of log() and fp division in C++?

    - by Ventzi Zhechev
    Hi, I’m using a log-based class in C++ to store very small floating-point values (as the values otherwise go beyond the scope of double). As I’m performing a large number of multiplications, this has the added benefit of converting the multiplications to sums. However, at a certain point in my algorithm, I need to divide a standard double value by an integer value and than do a *= to a log-based value. I have overloaded the *= operator for my log-based class and the right-hand side value is first converted to a log-based value by running log() and than added to the left-hand side value. Thus the operations actually performed are floating-point division, log() and floating-point summation. My question whether it would be faster to first convert the denominator to a log-based value, which would replace the floating-point division with floating-point subtraction, yielding the following chain of operations: twice log(), floating-point subtraction, floating-point summation. In the end, this boils down to whether floating-point division is faster or slower than log(). I suspect that a common answer would be that this is compiler and architecture dependent, so I’ll say that I use gcc 4.2 from Apple on darwin 10.3.0. Still, I hope to get an answer with a general remark on the speed of these two operators and/or an idea on how to measure the difference myself, as there might be more going on here, e.g. executing the constructors that do the type conversion etc. Cheers!

    Read the article

  • What's best performance way to constantly change image on WP7?

    - by AlRodriguez
    I'm trying to make my own type of remote desktop for WP7. I have a WCF service that returns an image on what's on the target machine's screen. Here's the WCF Server Code: // Method to load desktop image Bitmap image = new Bitmap( ViewSize.Width, ViewSize.Height ); Graphics g = Graphics.FromImage( image ); g.CopyFromScreen( Position.X, Position.Y, 0, 0, ViewSize ); g.Dispose( ); return image; // Convert image to byte[] which is returned to client using ( MemoryStream ms = new MemoryStream( ) ) { Bitmap image = screenGrabber.LoadScreenImage( ); image.Save( ms, ImageFormat.Jpeg ); imageArray = ms.ToArray( ); } Here's the code for the WP7 client: MemoryStream stream = new MemoryStream( data ); BitmapImage image = new BitmapImage( ); image.SetSource( stream ); BackgroundImage.Source = image; The BackgroundImage variable is an Image control. I'm noticing this freeze on the emulator after a short while, and will eventually crash from an OutOfMemoryException. This is already pretty slow ( images show up a good half second later than what's on the screen ), and I'm wondering if there's a better/faster way of doing this? Any help would be great. Thanks in advance.

    Read the article

  • Why do dicts of defaultdict(int)'s use so much memory? (and other simple python performance question

    - by dukhat
    import numpy as num from collections import defaultdict topKeys = range(16384) keys = range(8192) table = dict((k,defaultdict(int)) for k in topKeys) dat = num.zeros((16384,8192), dtype="int32") print "looping begins" #how much memory should this use? I think it shouldn't use more that a few #times the memory required to hold (16384*8192) int32's (512 mb), but #it uses 11 GB! for k in topKeys: for j in keys: dat[k,j] = table[k][j] print "done" What is going on here? Furthermore, this similar script takes eons to run compared to the first one, and also uses an absurd quantity of memory. topKeys = range(16384) keys = range(8192) table = [(j,0) for k in topKeys for j in keys] I guess python ints might be 64 bit ints, which would account for some of this, but do these relatively natural and simple constructions really produce such a massive overhead? I guess these scripts show that they do, so my question is: what exactly is causing the high memory usage in the first script and the long runtime and high memory usage of the second script and is there any way to avoid these costs?

    Read the article

  • Basics of Join Factorization

    - by Hong Su
    We continue our series on optimizer transformations with a post that describes the Join Factorization transformation. The Join Factorization transformation was introduced in Oracle 11g Release 2 and applies to UNION ALL queries. Union all queries are commonly used in database applications, especially in data integration applications. In many scenarios the branches in a UNION All query share a common processing, i.e, refer to the same tables. In the current Oracle execution strategy, each branch of a UNION ALL query is evaluated independently, which leads to repetitive processing, including data access and join. The join factorization transformation offers an opportunity to share the common computations across the UNION ALL branches. Currently, join factorization only factorizes common references to base tables only, i.e, not views. Consider a simple example of query Q1. Q1:    select t1.c1, t2.c2    from t1, t2, t3    where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c2 = 2 and t2.c2 = t3.c2   union all    select t1.c1, t2.c2    from t1, t2, t4    where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c3 = t4.c3; Table t1 appears in both the branches. As does the filter predicates on t1 (t1.c1 > 1) and the join predicates involving t1 (t1.c1 = t2.c1). Nevertheless, without any transformation, the scan (and the filtering) on t1 has to be done twice, once per branch. Such a query may benefit from join factorization which can transform Q1 into Q2 as follows: Q2:    select t1.c1, VW_JF_1.item_2    from t1, (select t2.c1 item_1, t2.c2 item_2                   from t2, t3                    where t2.c2 = t3.c2 and t2.c2 = 2                                  union all                   select t2.c1 item_1, t2.c2 item_2                   from t2, t4                    where t2.c3 = t4.c3) VW_JF_1    where t1.c1 = VW_JF_1.item_1 and t1.c1 > 1; In Q2, t1 is "factorized" and thus the table scan and the filtering on t1 is done only once (it's shared). If t1 is large, then avoiding one extra scan of t1 can lead to a huge performance improvement. Another benefit of join factorization is that it can open up more join orders. Let's look at query Q3. Q3:    select *    from t5, (select t1.c1, t2.c2                  from t1, t2, t3                  where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c2 = 2 and t2.c2 = t3.c2                 union all                  select t1.c1, t2.c2                  from t1, t2, t4                  where t1.c1 = t2.c1 and t1.c1 > 1 and t2.c3 = t4.c3) V;   where t5.c1 = V.c1 In Q3, view V is same as Q1. Before join factorization, t1, t2 and t3 must be joined first before they can be joined with t5. But if join factorization factorizes t1 from view V, t1 can then be joined with t5. This opens up new join orders. That being said, join factorization imposes certain join orders. For example, in Q2, t2 and t3 appear in the first branch of the UNION ALL query in view VW_JF_1. T2 must be joined with t3 before it can be joined with t1 which is outside of the VW_JF_1 view. The imposed join order may not necessarily be the best join order. For this reason, join factorization is performed under cost-based transformation framework; this means that we cost the plans with and without join factorization and choose the cheapest plan. Note that if the branches in UNION ALL have DISTINCT clauses, join factorization is not valid. For example, Q4 is NOT semantically equivalent to Q5.   Q4:     select distinct t1.*      from t1, t2      where t1.c1 = t2.c1  union all      select distinct t1.*      from t1, t2      where t1.c1 = t2.c1 Q5:    select distinct t1.*     from t1, (select t2.c1 item_1                   from t2                union all                   select t2.c1 item_1                  from t2) VW_JF_1     where t1.c1 = VW_JF_1.item_1 Q4 might return more rows than Q5. Q5's results are guaranteed to be duplicate free because of the DISTINCT key word at the top level while Q4's results might contain duplicates.   The examples given so far involve inner joins only. Join factorization is also supported in outer join, anti join and semi join. But only the right tables of outer join, anti join and semi joins can be factorized. It is not semantically correct to factorize the left table of outer join, anti join or semi join. For example, Q6 is NOT semantically equivalent to Q7. Q6:     select t1.c1, t2.c2    from t1, t2    where t1.c1 = t2.c1(+) and t2.c2 (+) = 2  union all    select t1.c1, t2.c2    from t1, t2      where t1.c1 = t2.c1(+) and t2.c2 (+) = 3 Q7:     select t1.c1, VW_JF_1.item_2    from t1, (select t2.c1 item_1, t2.c2 item_2                  from t2                  where t2.c2 = 2                union all                  select t2.c1 item_1, t2.c2 item_2                  from t2                                                                                                    where t2.c2 = 3) VW_JF_1       where t1.c1 = VW_JF_1.item_1(+)                                                                  However, the right side of an outer join can be factorized. For example, join factorization can transform Q8 to Q9 by factorizing t2, which is the right table of an outer join. Q8:    select t1.c2, t2.c2    from t1, t2      where t1.c1 = t2.c1 (+) and t1.c1 = 1 union all    select t1.c2, t2.c2    from t1, t2    where t1.c1 = t2.c1(+) and t1.c1 = 2 Q9:   select VW_JF_1.item_2, t2.c2   from t2,             (select t1.c1 item_1, t1.c2 item_2            from t1            where t1.c1 = 1           union all            select t1.c1 item_1, t1.c2 item_2            from t1            where t1.c1 = 2) VW_JF_1   where VW_JF_1.item_1 = t2.c1(+) All of the examples in this blog show factorizing a single table from two branches. This is just for ease of illustration. Join factorization can factorize multiple tables and from more than two UNION ALL branches.  SummaryJoin factorization is a cost-based transformation. It can factorize common computations from branches in a UNION ALL query which can lead to huge performance improvement. 

    Read the article

  • howto mount usb disk on esxi

    - by maruti
    have a USB drive NTFS attached to ESXi4 host. fdisk -l shows the device as /dev/mpx.... but when i try to mount that using mount /dev/xxx /mnt/usbdisk....it fails with message "no such file or dir" could anyone help with correct entry in etc/fstab? all that i am trying to do is backup the vms on esxi host to usb disk...thanks in advance

    Read the article

facebook twitter digg google delicious technorati stumbleupon myspace wordpress linkedin gmail igoogle windows live tumblr viadeo yahoo buzz yahoo mail yahoo bookmarks favorites email print

< Previous Page | 91 92 93 94 95 96 97 98 99 100 101 102  | Next Page >