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  • More CPU cores may not always lead to better performance – MAXDOP and query memory distribution in spotlight

    - by sqlworkshops
    More hardware normally delivers better performance, but there are exceptions where it can hinder performance. Understanding these exceptions and working around it is a major part of SQL Server performance tuning.   When a memory allocating query executes in parallel, SQL Server distributes memory to each task that is executing part of the query in parallel. In our example the sort operator that executes in parallel divides the memory across all tasks assuming even distribution of rows. Common memory allocating queries are that perform Sort and do Hash Match operations like Hash Join or Hash Aggregation or Hash Union.   In reality, how often are column values evenly distributed, think about an example; are employees working for your company distributed evenly across all the Zip codes or mainly concentrated in the headquarters? What happens when you sort result set based on Zip codes? Do all products in the catalog sell equally or are few products hot selling items?   One of my customers tested the below example on a 24 core server with various MAXDOP settings and here are the results:MAXDOP 1: CPU time = 1185 ms, elapsed time = 1188 msMAXDOP 4: CPU time = 1981 ms, elapsed time = 1568 msMAXDOP 8: CPU time = 1918 ms, elapsed time = 1619 msMAXDOP 12: CPU time = 2367 ms, elapsed time = 2258 msMAXDOP 16: CPU time = 2540 ms, elapsed time = 2579 msMAXDOP 20: CPU time = 2470 ms, elapsed time = 2534 msMAXDOP 0: CPU time = 2809 ms, elapsed time = 2721 ms - all 24 cores.In the above test, when the data was evenly distributed, the elapsed time of parallel query was always lower than serial query.   Why does the query get slower and slower with more CPU cores / higher MAXDOP? Maybe you can answer this question after reading the article; let me know: [email protected].   Well you get the point, let’s see an example.   The best way to learn is to practice. To create the below tables and reproduce the behavior, join the mailing list by using this link: www.sqlworkshops.com/ml and I will send you the table creation script.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go   Let’s create the temporary table #FireDrill with all possible Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip from Employees update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --First serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) goThe query took 1011 ms to complete.   The execution plan shows the 77816 KB of memory was granted while the estimated rows were 799624.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1912 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 799624.  The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead. Sort properties shows the rows are unevenly distributed over the 4 threads.   Sort Warnings in SQL Server Profiler.   Intermediate Summary: The reason for the higher duration with parallel plan was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001. Now let’s update the Employees table and distribute employees evenly across all Zip codes.   update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go   The query took 751 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.   Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 661 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 784707.  Sort properties shows the rows are evenly distributed over the 4 threads. No Sort Warnings in SQL Server Profiler.    Intermediate Summary: When employees were distributed unevenly, concentrated on 1 Zip code, parallel sort spilled while serial sort performed well without spilling to tempdb. When the employees were distributed evenly across all Zip codes, parallel sort and serial sort did not spill to tempdb. This shows uneven data distribution may affect the performance of some parallel queries negatively. For detailed discussion of memory allocation, refer to webcasts available at www.sqlworkshops.com/webcasts.     Some of you might conclude from the above execution times that parallel query is not faster even when there is no spill. Below you can see when we are joining limited amount of Zip codes, parallel query will be fasted since it can use Bitmap Filtering.   Let’s update the Employees table with 49 out of 50 employees located in Zip code 2001. update Employees set Zip = EmployeeID / 400 + 1 where EmployeeID % 50 = 1 update Employees set Zip = 2001 where EmployeeID % 50 != 1 go update statistics Employees with fullscan go  Let’s create the temporary table #FireDrill with limited Zip codes. drop table #FireDrill go create table #FireDrill (Zip int primary key) insert into #FireDrill select distinct Zip       from Employees where Zip between 1800 and 2001 update statistics #FireDrill with fullscan go  Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 989 ms to complete.  The execution plan shows the 77816 KB of memory was granted while the estimated rows were 785594. No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 1799 ms to complete.  The execution plan shows the 79360 KB of memory was granted while the estimated rows were 785594.  Sort Warnings in SQL Server Profiler.    The estimated number of rows between serial and parallel plan are the same. The parallel plan has slightly more memory granted due to additional overhead.  Intermediate Summary: The reason for the higher duration with parallel plan even with limited amount of Zip codes was sort spill. This is due to uneven distribution of employees over Zip codes, especially concentration of 49 out of 50 employees in Zip code 2001.   Now let’s update the Employees table and distribute employees evenly across all Zip codes. update Employees set Zip = EmployeeID / 400 + 1 go update statistics Employees with fullscan go Let’s execute the query serially with MAXDOP 1. --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --Serially with MAXDOP 1 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 1) go The query took 250  ms to complete.  The execution plan shows the 9016 KB of memory was granted while the estimated rows were 79973.8.  No Sort Warnings in SQL Server Profiler.  Now let’s execute the query in parallel with MAXDOP 0.  --Example provided by www.sqlworkshops.com --Execute query with uneven Zip code distribution --In parallel with MAXDOP 0 set statistics time on go declare @EmployeeID int, @EmployeeName varchar(48),@zip int select @EmployeeName = e.EmployeeName, @zip = e.Zip from Employees e       inner join #FireDrill fd on (e.Zip = fd.Zip)       order by e.Zip option (maxdop 0) go The query took 85 ms to complete.  The execution plan shows the 13152 KB of memory was granted while the estimated rows were 784707.  No Sort Warnings in SQL Server Profiler.    Here you see, parallel query is much faster than serial query since SQL Server is using Bitmap Filtering to eliminate rows before the hash join.   Parallel queries are very good for performance, but in some cases it can hinder performance. If one identifies the reason for these hindrances, then it is possible to get the best out of parallelism. I covered many aspects of monitoring and tuning parallel queries in webcasts (www.sqlworkshops.com/webcasts) and articles (www.sqlworkshops.com/articles). I suggest you to watch the webcasts and read the articles to better understand how to identify and tune parallel query performance issues.   Summary: One has to avoid sort spill over tempdb and the chances of spills are higher when a query executes in parallel with uneven data distribution. Parallel query brings its own advantage, reduced elapsed time and reduced work with Bitmap Filtering. So it is important to understand how to avoid spills over tempdb and when to execute a query in parallel.   I explain these concepts with detailed examples in my webcasts (www.sqlworkshops.com/webcasts), I recommend you to watch them. The best way to learn is to practice. To create the above tables and reproduce the behavior, join the mailing list at www.sqlworkshops.com/ml and I will send you the relevant SQL Scripts.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   Disclaimer and copyright information:This article refers to organizations and products that may be the trademarks or registered trademarks of their various owners. Copyright of this article belongs to R Meyyappan / www.sqlworkshops.com. You may freely use the ideas and concepts discussed in this article with acknowledgement (www.sqlworkshops.com), but you may not claim any of it as your own work. This article is for informational purposes only; you use any of the suggestions given here entirely at your own risk.   Register for the upcoming 3 Day Level 400 Microsoft SQL Server 2008 and SQL Server 2005 Performance Monitoring & Tuning Hands-on Workshop in London, United Kingdom during March 15-17, 2011, click here to register / Microsoft UK TechNet.These are hands-on workshops with a maximum of 12 participants and not lectures. For consulting engagements click here.   R Meyyappan [email protected] LinkedIn: http://at.linkedin.com/in/rmeyyappan  

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  • SQL University: Parallelism Week - Part 3, Settings and Options

    - by Adam Machanic
    Congratulations! You've made it back for the the third and final installment of Parallelism Week here at SQL University . So far we've covered the fundamentals of multitasking vs. parallel processing and delved into how parallel query plans actually work . Today we'll take a look at the settings and options that influence intra-query parallelism and discuss how best to set things up in various situations. Instance-Level Configuration Your database server probably has more than one logical processor....(read more)

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  • Performance problems loading XML with SSIS, an alternative way!

    - by AtulThakor
    I recently needed to load several thousand XML files into a SQL database, I created an SSIS package which was created as followed: Using a foreach container to loop through a directory and load each file path into a variable, the “Import XML” dataflow would then load each XML file into a SQL table.       Running this, it took approximately 1 second to load each file which seemed a massive amount of time to parse the XML and load the data, speaking to my colleague Martin Croft, he suggested the use of T-SQL Bulk Insert and OpenRowset, so we adjusted the package as followed:     The same foreach container was used but instead the following SQL command was executed (this is an expression):     "INSERT INTO MyTable(FileDate) SELECT   CAST(bulkcolumn AS XML)     FROM OPENROWSET(         BULK         '" + @[User::CurrentFile]  + "',         SINGLE_BLOB ) AS x"     Using this method we managed to load approximately 20 records per second, much faster…for data loading! For what we wanted to achieve this was perfect but I’ll leave you with the following points when making your own decision on which solution you decide to choose!      Openrowset Method Much faster to get the data into SQL You’ll need to parse or create a view over the XML data to allow the data to be more usable(another post on this!) Not able to apply validation/transformation against the data when loading it The SQL Server service account will need permission to the file No schema validation when loading files SSIS Slower (in our case) Schema validation Allows you to apply transformations/joins to the data Permissions should be less of a problem Data can be loaded into the final form through the package When using a schema validation errors can fail the package (I’ll do another post on this)

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  • Protect foreach loop when empty list

    - by SteB
    Using Powershell v2.0 I want to delete any files older than X days: $backups = Get-ChildItem -Path $Backuppath | Where-Object {($_.lastwritetime -lt (Get-Date).addDays(-$DaysKeep)) -and (-not $_.PSIsContainer) -and ($_.Name -like "backup*")} foreach ($file in $backups) { Remove-Item $file.FullName; } However, when $backups is empty I get: Remove-Item : Cannot bind argument to parameter 'Path' because it is null. I've tried: Protecting the foreach with if (!$backups) Protecting the Remove-Item with if (Test-Path $file -PathType Leaf) Protecting the Remove-Item with if ([IO.File]::Exists($file.FullName) -ne $true) None of these seem to work, what if the recommended way of preventing a foreach loop from being entered if the list is empty?

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  • Any big difference between using contains or loop through a list?

    - by Nazgulled
    Hi, Performance wise, is there really a big difference between using: ArrayList.contains(o) vs foreach|iterator LinkedList.contains(o) vs foreach|iterator HashMap.(containsKey|containsValue) vs foreach|iterator TreeMap.(containsKey|containsValue) vs foreach|iterator Of course, for the foreach|iterator loops, I'll have to explicitly compare the methods and return true or false accordingly. The object I'm comparing is an object where equals() and hashcode() are both properly overridden.

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  • Parallel programming, are we not learning from history again?

    - by mezmo
    I started programming because I was a hardware guy that got bored, I thought the problems being solved in the software side of things were much more interesting than those in hardware. At that time, most of the electrical buses I dealt with were serial, some moving data as fast as 1.5 megabit!! ;) Over the years these evolved into parallel buses in order to speed communication up, after all, transferring 8/16/32/64, whatever bits at a time incredibly speeds up the transfer. Well, our ability to create and detect state changes got faster and faster, to the point where we could push data so fast that interference between parallel traces or cable wires made cleaning the signal too expensive to continue, and we still got reasonable performance from serial interfaces, heck some graphics interfaces are even happening over USB for a while now. I think I'm seeing a like trend in software now, our processors were getting faster and faster, so we got good at building "serial" software. Now we've hit a speed bump in raw processor speed, so we're adding cores, or "traces" to the mix, and spending a lot of time and effort on learning how to properly use those. But I'm also seeing what I feel are advances in things like optical switching and even quantum computing that could take us far more quickly that I was expecting back to the point where "serial programming" again makes the most sense. What are your thoughts?

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  • Will Parallel-port dongle work on USB-to-Parallel Adapter?

    - by Gary M. Mugford
    We have a niche program running on a Win2K laptop that uses a security dongle connected to a parallel port for authentication. The laptop is getting creaky and I spent a frustrating night last night shopping various websites for a new laptop that had a parallel port. Seems I'm about three years late [G]. The question I have, is, if I buy a new(ish) laptop and use a USB-to-Parallel Port adapter, will the security dongle work? I know I'm not being specific about the app, but it's one most people wouldn't have heard of anyways. I've been guessing the answer to my question is no, since the app won't know to send a request out to the non-existent port. But, if the process actually is that the dongle sends a message INTO the computer every now and then, then it might work. And, I'm not sure whether the dongle is only needed at program startup time or randomly. The dongle is a 'permanent' addition to the old laptop. This is all about the money. We can have a newly-updated version of the program (which won't add any features we need) for the princely sum of $2700. Or we can spend $500 on a refurbed laptop still running WinXP, add a 30 buck adapter and keep the same solid, stolid performance we've come to appreciate. But it all comes down to the dongle behaviour. Oh, and a dock won't work. The whole laptop issue is about moving about the various nooks and crannies of the building with laptop in hand. Thanks for any suggestions/guidance. GM

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  • Does the for foreach construct in java create a hard or soft copy?

    - by Doug
    Say I have the following piece of java code ArrayList<Double> myList = new Double[100]; for (Double x : myList) x = randomDouble(); Does this actually modify myList or just the dummy variable? I realize I should just try this code segment out, but I think this is the sort of thing I should be able to google or search for on this site, and several queries so far have turned up nothing useful.

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  • How to simply a foreach iteration using reflection

    - by Priya
    Consider that I have to get the overall performance of X. X has Y elements and Y in turn has Z elements which inturn has some N elements. To get the performance of X I do this: List<double> XQ = new List<double>(); foreach (Elem Y in X.Y){ List<double> YQ = new List<double>(); foreach (Elem Z in Y.Z){ List<double> ZQ = new List<double>(); foreach (Elem N in Z.N){ ZQ.Add(GetPerformance(N)); } YQ.Add(AVG(ZQ)); } XQ.Add(AVG(YQ)); } AVG of XQ list gives the performance of X. The performance can be calculated for either X or Y or for Z. X, Y and Z share the same base class. So depending on the item given the foreach loop has to be executed. Currently I have a switch case to determine each item (X or Y or Z) and the foreach loop is repeated in the code pertaining to the item (eg. If Y foreach starts from Y.Z). Is is possible to convert this whole code generic using reflection instead of having to repeat it in each switch case? Thanks

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  • Can't add/remove items from a collection while foreach is iterating over it

    - by flockofcode
    If I make my own implementation of IEnumerator interface, then I am able ( inside foreach statement )to add or remove items from a albumsList without generating an exception.But if foreach statement uses IEnumerator supplied by albumsList, then trying to add/delete ( inside the foreach )items from albumsList will result in exception: class Program { static void Main(string[] args) { string[] rockAlbums = { "rock", "roll", "rain dogs" }; ArrayList albumsList = new ArrayList(rockAlbums); AlbumsCollection ac = new AlbumsCollection(albumsList); foreach (string item in ac) { Console.WriteLine(item); albumsList.Remove(item); //works } foreach (string item in albumsList) { albumsList.Remove(item); //exception } } class MyEnumerator : IEnumerator { ArrayList table; int _current = -1; public Object Current { get { return table[_current]; } } public bool MoveNext() { if (_current + 1 < table.Count) { _current++; return true; } else return false; } public void Reset() { _current = -1; } public MyEnumerator(ArrayList albums) { this.table = albums; } } class AlbumsCollection : IEnumerable { public ArrayList albums; public IEnumerator GetEnumerator() { return new MyEnumerator(this.albums); } public AlbumsCollection(ArrayList albums) { this.albums = albums; } } } a) I assume code that throws exception ( when using IEnumerator implementation A supplied by albumsList ) is located inside A? b) If I want to be able to add/remove items from a collection ( while foreach is iterating over it), will I always need to provide my own implementation of IEnumerator interface, or can albumsList be set to allow adding/removing items? thank you

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  • ForEach loop in Mathematica.

    - by dreeves
    I'd like something like this: ForEach[i_, {1,2,3}, Print[i] ] Or, more generally, to destructure arbitrary stuff in the list you're looping over, like: ForEach[{i_, j_}, {{1,10}, {2,20}, {3,30}}, Print[i*j] ] (Meta-question: is that a good way to call a ForEach loop, with the first argument a pattern like that?) ADDED: Some answerers have rightly pointed out that usually you want to use Map or other purely functional constructs and eschew a non-functional programming style where you use side effects. I agree! But here's an example where I think this ForEach construct is supremely useful: Say I have a list of options (rules) that pair symbols with expressions, like attrVals = {a -> 7, b -> 8, c -> 9} Now I want to make a hash table where I do the obvious mapping of those symbols to those numbers. I don't think there's a cleaner way to do that than ForEach[a_ -> v_, attrVals, h[a] = v] ADDED: I just realized that to do ForEach properly, it should support Break[] and Continue[]. I'm not sure how to implement that. Perhaps it will need to somehow be implemented in terms of For, While, or Do since those are the only loop constructs that support Break[] and Continue[]. If anyone interested in this wants to ask about that as a separate question, please do!

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  • What Parallel computing APIs make good use of sockets?

    - by Ole Jak
    My program uses sockets, what Parallel computing APIs could I use that would help me without obligating me to go from sockets to anything else? When we are on a cluster with a special, non-socket infrastructure system this API would emulate something like sockets but using that infrastructure (so programs perform much faster than on sockets, but still use the sockets API).

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  • How to use parallel execution in a shell script?

    - by eSKay
    I have a C shell script that does something like this: #!/bin/csh gcc example.c -o ex gcc combine.c -o combine ex file1 r1 <-- 1 ex file2 r2 <-- 2 ex file3 r3 <-- 3 #... many more like the above combine r1 r2 r3 final \rm r1 r2 r3 Is there some way I can make lines 1, 2 and 3 run in parallel instead of one after the another?

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  • Possible to distribute or parallel process a sequential program?

    - by damigu
    In C++, I've written a mathematical program (for diffusion limited aggregation) where each new point calculated is dependent on all of the preceding points. Is it possible to have such a program work in a parallel or distributed manner to increase computing speed? If so, what type of modifications to the code would I need to look into?

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  • Serial plans: Threshold / Parallel_degree_limit = 1

    - by jean-pierre.dijcks
    As a very short follow up on the previous post. So here is some more on getting a serial plan and why that happens Another reason - compared to the auto DOP is not on as we looked at in the earlier post - and often more prevalent to get a serial plan is if the plan simply does not take long enough to consider a parallel path. The resulting plan and note looks like this (note that this is a serial plan!): explain plan for select count(1) from sales; SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY()); PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------- Plan hash value: 672559287 -------------------------------------------------------------------------------------- | Id  | Operation            | Name  | Rows  | Cost (%CPU)| Time     | Pstart| Pstop | -------------------------------------------------------------------------------------- PLAN_TABLE_OUTPUT -------------------------------------------------------------------------------- |   0 | SELECT STATEMENT     |       |     1 |     5   (0)| 00:00:01 |       |     | |   1 |  SORT AGGREGATE      |       |     1 |            |          |       |     | |   2 |   PARTITION RANGE ALL|       |   960 |     5   (0)| 00:00:01 |     1 |  16 | |   3 |    TABLE ACCESS FULL | SALES |   960 |     5   (0)| 00:00:01 |     1 |  16 | Note -----    - automatic DOP: Computed Degree of Parallelism is 1 because of parallel threshold 14 rows selected. The parallel threshold is referring to parallel_min_time_threshold and since I did not change the default (10s) the plan is not being considered for a parallel degree computation and is therefore staying with the serial execution. Now we go into the land of crazy: Assume I do want this DOP=1 to happen, I could set the parameter in the init.ora, but to highlight it in this case I changed it on the session: alter session set parallel_degree_limit = 1; The result I get is: ERROR: ORA-02097: parameter cannot be modified because specified value is invalid ORA-00096: invalid value 1 for parameter parallel_degree_limit, must be from among CPU IO AUTO INTEGER>=2 Which of course makes perfect sense...

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  • Parallel curve like algorithm for graphs

    - by skrat
    Is there a well know algorithm for calculating "parallel graph"? where by parallel graph I mean the same as parallel curve, vaguely called "offset curve", but with a graph instead of a curve. Given this picture how can I calculate points of black outlined polygons?

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  • What is IE's Maximum Parallel Connection Accross All Hosts

    - by timeitquery
    Based on the IE documentation on MSDN IE 8 supports up to 6 parallel connections per server and IE 6,7 support 2. What is the upper limit of parallel connections accross all the hosts? So if I have 60 hosts, 8 requests per host, so 360 requests in the HTML page - does it mean that IE 8 will have 360 connection in parallel and IE 6 or 7 would have 120? (ignoring the html rendering time, and if call is blocking or not)

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  • Draw a parallel line

    - by VOX
    I have x1,y1 and x2,y2 which forms a line segment. How can I get another line x3,y3 - x4,y4 which is parallel to the first line as in the picture. I can simply add n to x1 and x2 to get a parallel line but it is not what i wanted. I want the lines to be as parallel in the picture.

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  • Parallel Assignment operator in Ruby

    - by Bragaadeesh
    Hi, I was going through an example from Programming in Ruby book. This is that example def fib_up_to(max) i1, i2 = 1, 1 # parallel assignment (i1 = 1 and i2 = 1) while i1 <= max yield i1 i1, i2 = i2, i1+i2 end end fib_up_to(100) {|f| print f, " " } The above program simply prints the fibonacci numbers upto 100. Thats fine. My question here is when i replace the parallel assignment with something like this, i1 = i2 i2 = i1+i2 I am not getting the desired output. My question here is, is it advisable to use parallel assignments? (I come from Java background and it feels really wierd to see this type of assignment) One more doubt is : Is parallel assignment an operator?? Thanks

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  • How to approach parallel processing of messages?

    - by Dan
    I am redesigning the messaging system for my app to use intel threading building blocks and am stumped trying to decide between two possible approaches. Basically, I have a sequence of message objects and for each message type, a sequence of handlers. For each message object, I apply each handler registered for that message objects type. The sequential version would be something like this (pseudocode): for each message in message_sequence <- SEQUENTIAL for each handler in (handler_table for message.type) apply handler to message <- SEQUENTIAL The first approach which I am considering processes the message objects in turn (sequentially) and applies the handlers concurrently. Pros: predictable ordering of messages (ie, we are guaranteed a FIFO processing order) (potentially) lower latency of processing each message Cons: more processing resources available than handlers for a single message type (bad parallelization) bad use of processor cache since message objects need to be copied for each handler to use large overhead for small handlers The pseudocode of this approach would be as follows: for each message in message_sequence <- SEQUENTIAL parallel_for each handler in (handler_table for message.type) apply handler to message <- PARALLEL The second approach is to process the messages in parallel and apply the handlers to each message sequentially. Pros: better use of processor cache (keeps the message object local to all handlers which will use it) small handlers don't impose as much overhead (as long as there are other handlers also to be run) more messages are expected than there are handlers, so the potential for parallelism is greater Cons: Unpredictable ordering - if message A is sent before message B, they may both be processed at the same time, or B may finish processing before all of A's handlers are finished (order is non-deterministic) The pseudocode is as follows: parallel_for each message in message_sequence <- PARALLEL for each handler in (handler_table for message.type) apply handler to message <- SEQUENTIAL The second approach has more advantages than the first, but non-deterministic ordering is a big disadvantage.. Which approach would you choose and why? Are there any other approaches I should consider (besides the obvious third approach: parallel messages and parallel handlers, which has the disadvantages of both and no real redeeming factors as far as I can tell)? Thanks!

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