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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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  • Oracle auf der CeBIT 2011 in Hannover

    - by franziska.schneider(at)oracle.com
    Cloud Computing als Organisationsstrategie in heterogenen Umgebungen 02.03.2011, 15:40 - 16:00 Halle 4, Stand A 58 Veranstalter: BITKOM Veranstaltungsreihe: Cloud Computing World Referent: Helene Lengler, Vice President, ORACLE Deutschland B.V. & Co. KG   Weiterhin können Sie viele Oracle Partner auf der CeBIT treffen. Schreiben Sie uns einfach mit Ihrem Themenwunsch an und wir organisieren einen Termin.

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  • Pay in the future should make you think in the present

    - by BuckWoody
    Distributed Computing - and more importantly “-as-a-Service” models of computing have a different cost model. This is something that sounds obvious on the surface but it’s often forgotten during the design and coding phase of a project. In on-premises computing, we’re used to purchasing a server and all of the hardware infrastructure and software licenses needed not only for one project, but several. This is an up-front or “sunk” cost that we consume by running code the organization needs to perform its function. Using a direct connection over wires you’ve already paid for, we don’t often have to think about bandwidth, hits on the data store or the amount of compute we use - we just know more is better. In a pay-as-you-go model, however, each of these architecture decisions has a potential cost impact. The amount of data you store, the number of times you access it, and the amount you send back all come with a charge. The offset is that you don’t buy anything at all up-front, so that sunk cost is freed up. And financial professionals know that money now is worth more than money later. Saving that up-front cost allows you to invest it in other things. It’s not just that you’re using things that now cost money - it’s that the design itself in distributed computing has a cost impact. That can be a really good thing, such as when you dynamically add capacity for paying customers. If you can tie back the cost of a series of clicks to what a user will pay to do so, you can set a profit margin that is easy to track. Here’s a case in point: Assume you are using a large instance in Windows Azure to compute some data that you retrieve from a SQL Azure database. If you don’t monitor the path of the application, you may not know what you are really using. Since you’re paying by the size of the instance, it’s best to maximize it all the time. Recently I evaluated just this situation, and found that downsizing the instance and adding another one where needed, adding a caching function to the application, moving part of the data into Windows Azure tables not only increased the speed of the application, but reduced the cost and more closely tied the cost to the profit. The key is this: from the very outset - the design - make sure you include metrics to measure for the cost/performance (sometimes these are the same) for your application. Windows Azure opens up awesome new ways of doing things, so make sure you study distributed systems architecture before you try and force in the application design you have on premises into your new application structure.

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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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  • 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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  • Terminology for mobile computing with a tablet?

    - by Idrise_Coulombe
    This is more of a terminology question... I'm developing an occasionally connected application that will run on a tablet for clinicians or field service workers but I'm struggling with what this type of computing is referred to. Mobile computing as connotations of a phone app. Whereas our clients may be occasionally at their desk. Microsoft uses Smart Client a lot, but I'm not sure if that best describes this scenario or is the common term for this kind of computing.

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  • Networked Parallel Port in Linux / KVM / QEMU

    - by korkman
    What I want to use is the "-parallel" tcp or udp option from KVM / QEMU, but I don't seem to find any server for this client. I don't serve a printer but a hardware dongle. I checked ser2net, which does provide "/dev/lp0" sharing, but it doesn't seem to work for KVM / QEMU. I suspect KVM / QEMU requires "/dev/parport0". I did rmmod lp, modprobe ppdev, linked ser2net to parport0, but it didn't work out. Perhaps ser2net is not suited for this. I tried socat as well, and I tried netcat. No success. Does anyone know any KVM / QEMU compatible parallel port server? Or did any of netcat, socat or ser2net work for you?

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  • Parallel prologue and epilogue in Grid Engine

    - by ajdecon
    We have a cluster being used to run MPI jobs for a customer. Previously this cluster used Torque as the scheduler, but we are transitioning to Grid Engine 6.2u5 (for some other features). Unfortunately, we are having trouble duplicating some of our maintenance scripts in the Grid Engine environment. In Torque, we have a prologue.parallel script which is used to carry out an automated health-check on the node. If this script returns a fail condition, Torque will helpfully offline the node and re-queue the job to use a different group of nodes. In Grid Engine, however, the queue "prolog" only runs on the head node of the job. We can manually run our prologue script from the startmpi.sh initialization script, for the mpi parallel environment; but I can't figure out how to detect a fail condition and carry out the same "mark offline and requeue" procedure. Any suggestions?

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