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  • How do you increase the number of processes in parallel with Powershell 3?

    - by Mark Shay
    I am trying to run 20 processes in parallel. I changed the session as below, but having no luck. I am getting only up to 5 parallel processes per session. $wo=New-PSWorkflowExecutionOption -MaxSessionsPerWorkflow 50 -MaxDisconnectedSessions 200 -MaxSessionsPerRemoteNode 50 -MaxActivityProcesses 50 Register-PSSessionConfiguration -Name ITWorkflows -SessionTypeOption $wo -Force Get-PSSessionConfiguration ITWorkflows | Format-List -Property * Is there a switch parameter to increase the number of processes? This is what I am running: Workflow MyWorkflow1 { Parallel { InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 2 and 2975416"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 2975417 and 5950831"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 5950832 and 8926246"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 8926247 and 11901661"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 11901662 and 14877076"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns"where OrderId between 14877077 and 17852491"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 17852492 and 20827906"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 20827907 and 23803321"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 23803322 and 26778736"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 26778737 and 29754151"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 29754152 and 32729566"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 32729567 and 35704981"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 35704982 and 38680396"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 38680397 and 432472144"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 432472145 and 435447559"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 435447560 and 438422974"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 864944289 and 867919703"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 867919704 and 870895118"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 870895119 and 1291465602"} InlineScript { import-module \\PS_Scripts\bulkins.ps1; BulkIns "where OrderId between 1291465603 and 1717986945"} }

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  • Is it too early to start designing for Task Parallel Library?

    - by Joe Erickson
    I have been following the development of the .NET Task Parallel Library (TPL) with great interest since Microsoft first announced it. There is no doubt in my mind that we will eventually take advantage of TPL. What I am questioning is whether it makes sense to start taking advantage of TPL when Visual Studio 2010 and .NET 4.0 are released, or whether it makes sense to wait a while longer. Why Start Now? The .NET 4.0 Task Parallel Library appears to be well designed and some relatively simple tests demonstrate that it works well on today's multi-core CPUs. I have been very interested in the potential advantages of using multiple lightweight threads to speed up our software since buying my first quad processor Dell Poweredge 6400 about seven years ago. Experiments at that time indicated that it was not worth the effort, which I attributed largely to the overhead of moving data between each CPU's cache (there was no shared cache back then) and RAM. Competitive advantage - some of our customers can never get enough performance and there is no doubt that we can build a faster product using TPL today. It sounds fun. Yes, I realize that some developers would rather poke themselves in the eye with a sharp stick, but we really enjoy maximizing performance. Why Wait? Are today's Intel Nehalem CPUs representative of where we are going as multi-core support matures? You can purchase a Nehalem CPU with 4 cores which share a single level 3 cache today, and most likely a 6 core CPU sharing a single level 3 cache by the time Visual Studio 2010 / .NET 4.0 are released. Obviously, the number of cores will go up over time, but what about the architecture? As the number of cores goes up, will they still share a cache? One issue with Nehalem is the fact that, even though there is a very fast interconnect between the cores, they have non-uniform memory access (NUMA) which can lead to lower performance and less predictable results. Will future multi-core architectures be able to do away with NUMA? Similarly, will the .NET Task Parallel Library change as it matures, requiring modifications to code to fully take advantage of it? Limitations Our core engine is 100% C# and has to run without full trust, so we are limited to using .NET APIs.

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  • Why is my computer not showing a speedup when I use parallel code?

    - by Jared P
    So I realize this question sounds stupid (and yes I am using a dual core), but I have tried two different libraries (Grand Central Dispatch and OpenMP), and when using clock() to time the code with and without the lines that make it parallel, the speed is the same. (for the record they were both using their own form of parallel for). They report being run on different threads, but perhaps they are running on the same core? Is there any way to check? (Both libraries are for C, I'm uncomfortable at lower layers.) This is super weird. Any ideas?

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  • Duplication in parallel inheritance hierarchies

    - by flamingpenguin
    Using an OO language with static typing (like Java), what are good ways to represent the following model invariant without large amounts of duplication. I have two (actually multiple) flavours of the same structure. Each flavour requires its own (unique to that flavour data) on each of the objects within that structure as well as some shared data. But within each instance of the aggregation only objects of one (the same) flavour are allowed. FooContainer can contain FooSources and FooDestinations and associations between the "Foo" objects BarContainer can contain BarSources and BarDestinations and associations between the "Bar" objects interface Container() { List<? extends Source> sources(); List<? extends Destination> destinations(); List<? extends Associations> associations(); } interface FooContainer() extends Container { List<? extends FooSource> sources(); List<? extends FooDestination> destinations(); List<? extends FooAssociations> associations(); } interface BarContainer() extends Container { List<? extends BarSource> sources(); List<? extends BarDestination> destinations(); List<? extends BarAssociations> associations(); } interface Source { String getSourceDetail1(); } interface FooSource extends Source { String getSourceDetail2(); } interface BarSource extends Source { String getSourceDetail3(); } interface Destination { String getDestinationDetail1(); } interface FooDestination extends Destination { String getDestinationDetail2(); } interface BarDestination extends Destination { String getDestinationDetail3(); } interface Association { Source getSource(); Destination getDestination(); } interface FooAssociation extends Association { FooSource getSource(); FooDestination getDestination(); String getFooAssociationDetail(); } interface BarAssociation extends Association { BarSource getSource(); BarDestination getDestination(); String getBarAssociationDetail(); }

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  • Parallel Installation of Ubuntu, Wubi and Windows 7?

    - by Aufwind
    I have Windows 7 installed on a Notebook. Because I wanted to experiment with Ubutnu without loosing my Windows 7 configuration I installed the Wubi Version of Ubuntu inside of Windows 7. Since things seam to work out well with Ubuntu and my harddisk space is very limited, I want to make Ubuntu my only operating system on this machine. So my question is: Is it possible, to install Ubuntu 11.04 additionally to my actual configuration (Wubi and Windows 7)? And if things turned out well with the native installation of Ubuntu, too, to delete Wubi and Windows 7 from my system afterwards? Why I am asking this is simply, that I am afraid, that if I just format my machine and made the whole harddisk available for the fresh Ubuntu installation, it could be possible, that it turns out, that one thing or an other would not work properly, like it did in Wubi. (Possible Network, VPN, and Firewall@work issues) In this case I'd like the possibility to switch back.

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  • Parallel downloading of JavaScript files on page load

    - by user359650
    Below is a quote from one of the Yahoo performance pages: While a script is downloading, however, the browser won't start any other downloads, even on different hostnames. When I look at page load of our website, I can see that many scripts are being downloaded at the same time: Am I mistaken, or should the quote should instead read like this? While scripts are downloading (there can be several scripts downloading at the same time), the browser won't start any other downloads, even on different hostnames.

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  • Load Balance and Parallel Performance

    Load balancing an application workload among threads is critical to performance. However, achieving perfect load balance is non-trivial, and it depends on the parallelism within the application, workload, the number of threads, load balancing policy, and the threading implementation.

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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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  • 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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  • 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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