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  • Here's your chance: MOS Feedback Sessions @OOW

    - by cwarticki
    Bring your questions, comments, concerns, opinions, recommendations, enhancement requests and any emotional outbursts!   As I travel the world and speak to thousands of customers, I receive plenty of feedback about My Oracle support.  Come hear directly from the source. Meet Dennis Reno, VP of Customer Portal Experience. The Customer Portal Experience team will host a My Oracle Support Tips and Techniques session and three roundtable feedback sessions at this year’s Oracle OpenWorld. The sessions will include a Hardware Support component, as well as best practices that are sure to benefit all My Oracle Support users. The events planned will give our users the opportunity to learn more about how the My Oracle Support customer portal adds value to the support process and to their business needs. The roundtable feedback sessions will allow customers to meet, give feedback, and share their experiences directly with the team responsible for the customer portal experience. Date Time (PT) Session Name Mon, Oct 1 01:45 PM My Oracle Support: Tips and Techniques for Getting the Best Hardware Support Possible (Session #CON9745) Tue, Oct 2 11:00 AM Roundtable - My Oracle Support General Feedback Wed, Oct 3 11:00 AM Roundtable - My Oracle Support Community Feedback Thr, Oct 4 11:00 AM Roundtable - My Oracle Support General Feedback Customers can find more information, including specific details about how to attend, by accessing My Oracle Support at OpenWorld (Article ID 1484508.1). Enjoy OpenWorld everyone! -Chris Warticki Global Customer Management

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  • Adding complexity by generalising: how far should you go?

    - by marcog
    Reference question: http://stackoverflow.com/questions/4303813/help-with-interview-question The above question asked to solve a problem for an NxN matrix. While there was an easy solution, I gave a more general solution to solve the more general problem for an NxM matrix. A handful of people commented that this generalisation was bad because it made the solution more complex. One such comment is voted +8. Putting aside the hard-to-explain voting effects on SO, there are two types of complexity to be considered here: Runtime complexity, i.e. how fast does the code run Code complexity, i.e. how difficult is the code to read and understand The question of runtime complexity is something that requires a better understanding of the input data today and what it might look like in the future, taking the various growth factors into account where necessary. The question of code complexity is the one I'm interested in here. By generalising the solution, we avoid having to rewrite it in the event that the constraints change. However, at the same time it can often result in complicating the code. In the reference question, the code for NxN is easy to understand for any competent programmer, but the NxM case (unless documented well) could easily confuse someone coming across the code for the first time. So, my question is this: Where should you draw the line between generalising and keeping the code easy to understand?

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  • Adding complexity by generalising: how far should you go?

    - by marcog
    Reference question: http://stackoverflow.com/questions/4303813/help-with-interview-question The above question asked to solve a problem for an NxN matrix. While there was an easy solution, I gave a more general solution to solve the more general problem for an NxM matrix. A handful of people commented that this generalisation was bad because it made the solution more complex. One such comment is voted +8. Putting aside the hard-to-explain voting effects on SO, there are two types of complexity to be considered here: Runtime complexity, i.e. how fast does the code run Code complexity, i.e. how difficult is the code to read and understand The question of runtime complexity is something that requires a better understanding of the input data today and what it might look like in the future, taking the various growth factors into account where necessary. The question of code complexity is the one I'm interested in here. By generalising the solution, we avoid having to rewrite it in the event that the constraints change. However, at the same time it can often result in complicating the code. In the reference question, the code for NxN is easy to understand for any competent programmer, but the NxM case (unless documented well) could easily confuse someone coming across the code for the first time. So, my question is this: Where should you draw the line between generalising and keeping the code easy to understand?

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  • Code Metrics: Number of IL Instructions

    - by DigiMortal
    In my previous posting about code metrics I introduced how to measure LoC (Lines of Code) in .NET applications. Now let’s take a step further and let’s take a look how to measure compiled code. This way we can somehow have a picture about what compiler produces. In this posting I will introduce you code metric called number of IL instructions. NB! Number of IL instructions is not something you can use to measure productivity of your team. If you want to get better idea about the context of this metric and LoC then please read my first posting about LoC. What are IL instructions? When code written in some .NET Framework language is compiled then compiler produces assemblies that contain byte code. These assemblies are executed later by Common Language Runtime (CLR) that is code execution engine of .NET Framework. The byte code is called Intermediate Language (IL) – this is more common language than C# and VB.NET by example. You can use ILDasm tool to convert assemblies to IL assembler so you can read them. As IL instructions are building blocks of all .NET Framework binary code these instructions are smaller and highly general – we don’t want very rich low level language because it executes slower than more general language. For every method or property call in some .NET Framework language corresponds set of IL instructions. There is no 1:1 relationship between line in high level language and line in IL assembler. There are more IL instructions than lines in C# code by example. How much instructions there are? I have no common answer because it really depends on your code. Here you can see some metrics from my current community project that is developed on SharePoint Server 2007. As average I have about 7 IL instructions per line of code. This is not metric you should use, it is just illustrative example so you can see the differences between numbers of lines and IL instructions. Why should I measure the number of IL instructions? Just take a look at chart above. Compiler does something that you cannot see – it compiles your code to IL. This is not intuitive process because you usually cannot say what is exactly the end result. You know it at greater plain but you don’t know it exactly. Therefore we can expect some surprises and that’s why we should measure the number of IL instructions. By example, you may find better solution for some method in your source code. It looks nice, it works nice and everything seems to be okay. But on server under load your fix may be way slower than previous code. Although you minimized the number of lines of code it ended up with increasing the number of IL instructions. How to measure the number of IL instructions? My choice is NDepend because Visual Studio is not able to measure this metric. Steps to make are easy. Open your NDepend project or create new and add all your application assemblies to project (you can also add Visual Studio solution to project). Run project analysis and wait until it is done. You can see over-all stats form global summary window. This is the same window I used to read the LoC and the number of IL instructions metrics for my chart. Meanwhile I made some changes to my code (enabled advanced caching for events and event registrations module) and then I ran code analysis again to get results for this section of this posting. NDepend is also able to tell you exactly what parts of code have problematically much IL instructions. The code quality section of CQL Query Explorer shows you how much problems there are with members in analyzed code. If you click on the line Methods too big (NbILInstructions) you can see all the problematic members of classes in CQL Explorer shown in image on right. In my case if have 10 methods that are too big and two of them have horrible number of IL instructions – just take a look at first two methods in this TOP10. Also note the query box. NDepend has easy and SQL-like query language to query code analysis results. You can modify these queries if you like and also you can define your own ones if default set is not enough for you. What is good result? As you can see from query window then the number of IL instructions per member should have maximally 200 IL instructions. Of course, like always, the less instructions you have, the better performing code you have. I don’t mean here little differences but big ones. By example, take a look at my first method in warnings list. The number of IL instructions it has is huge. And believe me – this method looks awful. Conclusion The number of IL instructions is useful metric when optimizing your code. For analyzing code at general level to find out too long methods you can use the number of LoC metric because it is more intuitive for you and you can therefore handle the situation more easily. Also you can use NDepend as code metrics tool because it has a lot of metrics to offer.

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  • How to refactor use of the general Exception?

    - by Colin
    Our code catches the general exception everywhere. Usually it writes the error to a log table in the database and shows a MessageBox to the user to say that the operation requested failed. If there is database interaction, the transaction is rolled back. I have introduced a business logic layer and a data access layer to unravel some of the logic. In the data access layer, I have chosen not to catch anything and I also throw ArgumentNullExceptions and ArgumentOutOfRangeExceptions so that the message passed up the stack does not come straight from the database. In the business logic layer I put a try catch. In the catch I rollback the transaction, do the logging and rethrow. In the presentation layer there is another try catch that displays a MessageBox. I am now thinking about catching a DataException and an ArgumentException instead of an Exception where I know the code only accesses a database. Where the code accesses a web service, then I thought I would create my own "WebServiceException", which would be created in the data access layer whenever an HttpException, WebException or SoapException is thrown. So now, generally I will be catching 2 or 3 exceptions where currently I catch just the general Exception, and I think that seems OK to me. Does anyone wrap exceptions up again to carry the message up to the presentation layer? I think I should probably add a try catch to Main() that catches Exception, attempts to log it, displays an "Application has encountered an error" message and exits the application. So, my question is, does anyone see any holes in my plan? Are there any obvious exceptions that I should be catching or do these ones pretty much cover it (other than file access - I think there is only 1 place where we read-write to a config file).

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  • Why are there no package management systems for C and C++?

    - by m0nhawk
    There are some programming languages for which exist their own package management systems: CTAN for TeX CPAN for Perl Pip & Eggs for Python Maven for Java cabal for Haskell Gems for Ruby Is there any other languages with such systems? What about C and C++? (that's the main question!) Why there are no such systems for them? And isn't creating packages for yum, apt-get or other general package management systems better?

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  • General purpose physics engine

    - by Lucas
    Is there any general purpose physics engine that allows huge simulations of rigid bodies? I'm using PhysX from Nvidia, but the focus of this engine is game development, soft bodies. I want to know if exists physics engine that runs on top of PS3 cell processors or CUDA cores allowing massive scientific physics simulations.

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  • General Purpose Language to build a compiler for

    - by Brownie
    Inspired by Eric Sink's interview on the stackoverflow podcast I would like to build a full compiler in my spare time for the learning experience. My initial thought was to build a C compiler but I'm not sure whether it would take too much time. I am wondering if there is a smaller general purpose language that would be more appropriate to implement as a first compiler effort? Or is a C implementation doable on a reasonable timescale (200 hrs)? It is my intention to target the CLR.

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  • Podcast Focusing on XNA or Game/Graphics Programming in General

    - by Daniel Brotherston
    I realize there are a number of podcast related questions, but I'm specifically looking for XNA oriented podcasts. I've Googled around a little bit, but I have been unable to find any current podcasts. I'm just wondering if anyone else knows of any interesting ones. I'd guess if Google can't find them, they don't exist but I thought I'd ask anyways. Also, failing that, podcasts about game development in general would be interesting as well. Thanks!

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  • How do games move around objects (in general)

    - by user146780
    I'm sure there's not just 1 answer to this but, do game engines actually change the vectors in memory, or use gltransformations? Because pushing and popping the matrix all the time seems inefficient, but if you keep modifying the verticies you cant make use of display lists. So I'm wondering how it's done in general. Thanks

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  • How do games move around objects (in general) (OGL)

    - by user146780
    I'm sure there's not just 1 answer to this but, do game engines actually change the vectors in memory, or use gltransformations? Because pushing and popping the matrix all the time seems inefficient, but if you keep modifying the verticies you cant make use of display lists. So I'm wondering how it's done in general. Thanks

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  • The dislikes of TDD

    - by andrewstopford
    I enjoy debates about TDD and Brian Harrys blog post is no exception. Brian sounds out what he likes and dislikes about TDD and it's the dislikes I'll focus on. The idea of having unit tests that cover virtually every line of code that I’ve written that I have to refactor every time I refactor my code makes me shudder.  Doing this way makes me take nearly twice as long as it would otherwise take and I don’t feel like I get sufficient benefits from it. Refactoring your tests to match your refactored code sounds like the tests are suffering. Too many hard dependencies with no SOLID concerns are a sure fire reason you would do this. Maybe at the start of a TDD cycle you would need to do this as your design evolves and you remove these dependencies but this should quickly be resolved as you refactor. If you find your self still doing it then stop and look back at your design. Don’t get me wrong, I’m a big fan of unit tests.  I just prefer to write them after the code has stopped shaking a bit.  In fact most of my early testing is “manual”.  Either I write a small UI on top of my service that allows me to plug in values and try it or write some quick API tests that I throw away as soon as I have validated them. The problem with this is that a UI can make assumptions on your code that then just unit test around and very quickly the design becomes bad and you technical debt sweeps in. If you want to blackbox test your code with a UI then do so after your TDD cycles not before. This is probably by biggest issue with a literal TDD interpretation.  TDD says you never write a line of code without a failing test to show you need it.  I find it leads developers down a dangerous path.  Without any help from a methodology, I have met way too many developers in my life that “back into a solution”.  By this, I mean they write something, it mostly works and they discover a new requirement so they tack it on, and another and another and when they are done, they’ve got a monstrosity of special cases each designed to handle one specific scenario.  There’s way more code than there should be and it’s way too complicated to understand. I believe in finding general solutions to problems from which all the special cases naturally derive rather than building a solution of special cases.  In my mind, to do this, you have to start by conceptualizing and coding the framework of the general algorithm.  For me, that’s a relatively monolithic exercise. TDD is an development pratice not a methodology, the danger is that the solution becomes a mass of different things that violate DRY. TDD won't solve these problems, only good communication and practices like pairing will help. Above all else an assumption that TDD replaces a methodology is a mistake, combine it with what ever works for your team\business but only good communication will help. A good naming scheme\structure for folders, files and tests can help you and your team isolate what tests are for what.

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  • Windows Phone 7 review

    - by Jeff
    I finally got around to composing some thoughts on what I think about Windows Phone 7, and I posted those impressions on my personal blog. I'll save a few bytes and not repost it here.It should be obvious that my general impression is overwhelmingly positive. What I don't go into very deeply is how much I enjoy developing stuff for it. Baby Stopwatch was not even remotely hard to build, because it wasn't complex, but also because the platform itself is so easy to deal with. I've been messing around and building something a little more involved, and it too has been fun to work with. Sure, you have the quirks of Silverlight to work out, and then the phone-specific quirks after that, but it really is a lot of fun. If you haven't come up with a science project for the phone, I would encourage you to do so.Now if only I could find a gig here at Microsoft where people just build phone apps all day! (But not games... I know we already do that quite a bit.)

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  • Difference between $ and # in ADF/JSF/JSP

    - by pavan.pvj
    Found this one interesting. So, picked it from one of the books and posting here.JSP 2.1 and JSF 1.2 - both of them use a unified Expression language. One major and the most obvious difference is between $ and #. JSP 2.1 uses $ and JSF 1.2 uses # in an EL. $ - immediate evaluation# - deferred evaluation$ - $ syntax executes expressions eagerly/immediately, which means that the result is returned immediately when the page renders.# - # syntax defers the expression evaluation to a point defined by the implementing technology. In general, JSF uses deferred EL evaluation because of its multiple lifecycle phases in which events are handled. To ensure the model is prepared before the values are accessed by EL, it must defer EL evaluation until the appropriate point in the life cycle.Note: This is picked up from Oracle Fusion Developer Guide (ISBN: 9780071622547). There is also a very good article here:http://java.sun.com/products/jsp/reference/techart/unifiedEL.html

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  • PTLQueue : a scalable bounded-capacity MPMC queue

    - by Dave
    Title: Fast concurrent MPMC queue -- I've used the following concurrent queue algorithm enough that it warrants a blog entry. I'll sketch out the design of a fast and scalable multiple-producer multiple-consumer (MPSC) concurrent queue called PTLQueue. The queue has bounded capacity and is implemented via a circular array. Bounded capacity can be a useful property if there's a mismatch between producer rates and consumer rates where an unbounded queue might otherwise result in excessive memory consumption by virtue of the container nodes that -- in some queue implementations -- are used to hold values. A bounded-capacity queue can provide flow control between components. Beware, however, that bounded collections can also result in resource deadlock if abused. The put() and take() operators are partial and wait for the collection to become non-full or non-empty, respectively. Put() and take() do not allocate memory, and are not vulnerable to the ABA pathologies. The PTLQueue algorithm can be implemented equally well in C/C++ and Java. Partial operators are often more convenient than total methods. In many use cases if the preconditions aren't met, there's nothing else useful the thread can do, so it may as well wait via a partial method. An exception is in the case of work-stealing queues where a thief might scan a set of queues from which it could potentially steal. Total methods return ASAP with a success-failure indication. (It's tempting to describe a queue or API as blocking or non-blocking instead of partial or total, but non-blocking is already an overloaded concurrency term. Perhaps waiting/non-waiting or patient/impatient might be better terms). It's also trivial to construct partial operators by busy-waiting via total operators, but such constructs may be less efficient than an operator explicitly and intentionally designed to wait. A PTLQueue instance contains an array of slots, where each slot has volatile Turn and MailBox fields. The array has power-of-two length allowing mod/div operations to be replaced by masking. We assume sensible padding and alignment to reduce the impact of false sharing. (On x86 I recommend 128-byte alignment and padding because of the adjacent-sector prefetch facility). Each queue also has PutCursor and TakeCursor cursor variables, each of which should be sequestered as the sole occupant of a cache line or sector. You can opt to use 64-bit integers if concerned about wrap-around aliasing in the cursor variables. Put(null) is considered illegal, but the caller or implementation can easily check for and convert null to a distinguished non-null proxy value if null happens to be a value you'd like to pass. Take() will accordingly convert the proxy value back to null. An advantage of PTLQueue is that you can use atomic fetch-and-increment for the partial methods. We initialize each slot at index I with (Turn=I, MailBox=null). Both cursors are initially 0. All shared variables are considered "volatile" and atomics such as CAS and AtomicFetchAndIncrement are presumed to have bidirectional fence semantics. Finally T is the templated type. I've sketched out a total tryTake() method below that allows the caller to poll the queue. tryPut() has an analogous construction. Zebra stripping : alternating row colors for nice-looking code listings. See also google code "prettify" : https://code.google.com/p/google-code-prettify/ Prettify is a javascript module that yields the HTML/CSS/JS equivalent of pretty-print. -- pre:nth-child(odd) { background-color:#ff0000; } pre:nth-child(even) { background-color:#0000ff; } border-left: 11px solid #ccc; margin: 1.7em 0 1.7em 0.3em; background-color:#BFB; font-size:12px; line-height:65%; " // PTLQueue : Put(v) : // producer : partial method - waits as necessary assert v != null assert Mask = 1 && (Mask & (Mask+1)) == 0 // Document invariants // doorway step // Obtain a sequence number -- ticket // As a practical concern the ticket value is temporally unique // The ticket also identifies and selects a slot auto tkt = AtomicFetchIncrement (&PutCursor, 1) slot * s = &Slots[tkt & Mask] // waiting phase : // wait for slot's generation to match the tkt value assigned to this put() invocation. // The "generation" is implicitly encoded as the upper bits in the cursor // above those used to specify the index : tkt div (Mask+1) // The generation serves as an epoch number to identify a cohort of threads // accessing disjoint slots while s-Turn != tkt : Pause assert s-MailBox == null s-MailBox = v // deposit and pass message Take() : // consumer : partial method - waits as necessary auto tkt = AtomicFetchIncrement (&TakeCursor,1) slot * s = &Slots[tkt & Mask] // 2-stage waiting : // First wait for turn for our generation // Acquire exclusive "take" access to slot's MailBox field // Then wait for the slot to become occupied while s-Turn != tkt : Pause // Concurrency in this section of code is now reduced to just 1 producer thread // vs 1 consumer thread. // For a given queue and slot, there will be most one Take() operation running // in this section. // Consumer waits for producer to arrive and make slot non-empty // Extract message; clear mailbox; advance Turn indicator // We have an obvious happens-before relation : // Put(m) happens-before corresponding Take() that returns that same "m" for T v = s-MailBox if v != null : s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 // unlock slot to admit next producer and consumer return v Pause tryTake() : // total method - returns ASAP with failure indication for auto tkt = TakeCursor slot * s = &Slots[tkt & Mask] if s-Turn != tkt : return null T v = s-MailBox // presumptive return value if v == null : return null // ratify tkt and v values and commit by advancing cursor if CAS (&TakeCursor, tkt, tkt+1) != tkt : continue s-MailBox = null ST-ST barrier s-Turn = tkt + Mask + 1 return v The basic idea derives from the Partitioned Ticket Lock "PTL" (US20120240126-A1) and the MultiLane Concurrent Bag (US8689237). The latter is essentially a circular ring-buffer where the elements themselves are queues or concurrent collections. You can think of the PTLQueue as a partitioned ticket lock "PTL" augmented to pass values from lock to unlock via the slots. Alternatively, you could conceptualize of PTLQueue as a degenerate MultiLane bag where each slot or "lane" consists of a simple single-word MailBox instead of a general queue. Each lane in PTLQueue also has a private Turn field which acts like the Turn (Grant) variables found in PTL. Turn enforces strict FIFO ordering and restricts concurrency on the slot mailbox field to at most one simultaneous put() and take() operation. PTL uses a single "ticket" variable and per-slot Turn (grant) fields while MultiLane has distinct PutCursor and TakeCursor cursors and abstract per-slot sub-queues. Both PTL and MultiLane advance their cursor and ticket variables with atomic fetch-and-increment. PTLQueue borrows from both PTL and MultiLane and has distinct put and take cursors and per-slot Turn fields. Instead of a per-slot queues, PTLQueue uses a simple single-word MailBox field. PutCursor and TakeCursor act like a pair of ticket locks, conferring "put" and "take" access to a given slot. PutCursor, for instance, assigns an incoming put() request to a slot and serves as a PTL "Ticket" to acquire "put" permission to that slot's MailBox field. To better explain the operation of PTLQueue we deconstruct the operation of put() and take() as follows. Put() first increments PutCursor obtaining a new unique ticket. That ticket value also identifies a slot. Put() next waits for that slot's Turn field to match that ticket value. This is tantamount to using a PTL to acquire "put" permission on the slot's MailBox field. Finally, having obtained exclusive "put" permission on the slot, put() stores the message value into the slot's MailBox. Take() similarly advances TakeCursor, identifying a slot, and then acquires and secures "take" permission on a slot by waiting for Turn. Take() then waits for the slot's MailBox to become non-empty, extracts the message, and clears MailBox. Finally, take() advances the slot's Turn field, which releases both "put" and "take" access to the slot's MailBox. Note the asymmetry : put() acquires "put" access to the slot, but take() releases that lock. At any given time, for a given slot in a PTLQueue, at most one thread has "put" access and at most one thread has "take" access. This restricts concurrency from general MPMC to 1-vs-1. We have 2 ticket locks -- one for put() and one for take() -- each with its own "ticket" variable in the form of the corresponding cursor, but they share a single "Grant" egress variable in the form of the slot's Turn variable. Advancing the PutCursor, for instance, serves two purposes. First, we obtain a unique ticket which identifies a slot. Second, incrementing the cursor is the doorway protocol step to acquire the per-slot mutual exclusion "put" lock. The cursors and operations to increment those cursors serve double-duty : slot-selection and ticket assignment for locking the slot's MailBox field. At any given time a slot MailBox field can be in one of the following states: empty with no pending operations -- neutral state; empty with one or more waiting take() operations pending -- deficit; occupied with no pending operations; occupied with one or more waiting put() operations -- surplus; empty with a pending put() or pending put() and take() operations -- transitional; or occupied with a pending take() or pending put() and take() operations -- transitional. The partial put() and take() operators can be implemented with an atomic fetch-and-increment operation, which may confer a performance advantage over a CAS-based loop. In addition we have independent PutCursor and TakeCursor cursors. Critically, a put() operation modifies PutCursor but does not access the TakeCursor and a take() operation modifies the TakeCursor cursor but does not access the PutCursor. This acts to reduce coherence traffic relative to some other queue designs. It's worth noting that slow threads or obstruction in one slot (or "lane") does not impede or obstruct operations in other slots -- this gives us some degree of obstruction isolation. PTLQueue is not lock-free, however. The implementation above is expressed with polite busy-waiting (Pause) but it's trivial to implement per-slot parking and unparking to deschedule waiting threads. It's also easy to convert the queue to a more general deque by replacing the PutCursor and TakeCursor cursors with Left/Front and Right/Back cursors that can move either direction. Specifically, to push and pop from the "left" side of the deque we would decrement and increment the Left cursor, respectively, and to push and pop from the "right" side of the deque we would increment and decrement the Right cursor, respectively. We used a variation of PTLQueue for message passing in our recent OPODIS 2013 paper. ul { list-style:none; padding-left:0; padding:0; margin:0; margin-left:0; } ul#myTagID { padding: 0px; margin: 0px; list-style:none; margin-left:0;} -- -- There's quite a bit of related literature in this area. I'll call out a few relevant references: Wilson's NYU Courant Institute UltraComputer dissertation from 1988 is classic and the canonical starting point : Operating System Data Structures for Shared-Memory MIMD Machines with Fetch-and-Add. Regarding provenance and priority, I think PTLQueue or queues effectively equivalent to PTLQueue have been independently rediscovered a number of times. See CB-Queue and BNPBV, below, for instance. But Wilson's dissertation anticipates the basic idea and seems to predate all the others. Gottlieb et al : Basic Techniques for the Efficient Coordination of Very Large Numbers of Cooperating Sequential Processors Orozco et al : CB-Queue in Toward high-throughput algorithms on many-core architectures which appeared in TACO 2012. Meneghin et al : BNPVB family in Performance evaluation of inter-thread communication mechanisms on multicore/multithreaded architecture Dmitry Vyukov : bounded MPMC queue (highly recommended) Alex Otenko : US8607249 (highly related). John Mellor-Crummey : Concurrent queues: Practical fetch-and-phi algorithms. Technical Report 229, Department of Computer Science, University of Rochester Thomasson : FIFO Distributed Bakery Algorithm (very similar to PTLQueue). Scott and Scherer : Dual Data Structures I'll propose an optimization left as an exercise for the reader. Say we wanted to reduce memory usage by eliminating inter-slot padding. Such padding is usually "dark" memory and otherwise unused and wasted. But eliminating the padding leaves us at risk of increased false sharing. Furthermore lets say it was usually the case that the PutCursor and TakeCursor were numerically close to each other. (That's true in some use cases). We might still reduce false sharing by incrementing the cursors by some value other than 1 that is not trivially small and is coprime with the number of slots. Alternatively, we might increment the cursor by one and mask as usual, resulting in a logical index. We then use that logical index value to index into a permutation table, yielding an effective index for use in the slot array. The permutation table would be constructed so that nearby logical indices would map to more distant effective indices. (Open question: what should that permutation look like? Possibly some perversion of a Gray code or De Bruijn sequence might be suitable). As an aside, say we need to busy-wait for some condition as follows : "while C == 0 : Pause". Lets say that C is usually non-zero, so we typically don't wait. But when C happens to be 0 we'll have to spin for some period, possibly brief. We can arrange for the code to be more machine-friendly with respect to the branch predictors by transforming the loop into : "if C == 0 : for { Pause; if C != 0 : break; }". Critically, we want to restructure the loop so there's one branch that controls entry and another that controls loop exit. A concern is that your compiler or JIT might be clever enough to transform this back to "while C == 0 : Pause". You can sometimes avoid this by inserting a call to a some type of very cheap "opaque" method that the compiler can't elide or reorder. On Solaris, for instance, you could use :"if C == 0 : { gethrtime(); for { Pause; if C != 0 : break; }}". It's worth noting the obvious duality between locks and queues. If you have strict FIFO lock implementation with local spinning and succession by direct handoff such as MCS or CLH,then you can usually transform that lock into a queue. Hidden commentary and annotations - invisible : * And of course there's a well-known duality between queues and locks, but I'll leave that topic for another blog post. * Compare and contrast : PTLQ vs PTL and MultiLane * Equivalent : Turn; seq; sequence; pos; position; ticket * Put = Lock; Deposit Take = identify and reserve slot; wait; extract & clear; unlock * conceptualize : Distinct PutLock and TakeLock implemented as ticket lock or PTL Distinct arrival cursors but share per-slot "Turn" variable provides exclusive role-based access to slot's mailbox field put() acquires exclusive access to a slot for purposes of "deposit" assigns slot round-robin and then acquires deposit access rights/perms to that slot take() acquires exclusive access to slot for purposes of "withdrawal" assigns slot round-robin and then acquires withdrawal access rights/perms to that slot At any given time, only one thread can have withdrawal access to a slot at any given time, only one thread can have deposit access to a slot Permissible for T1 to have deposit access and T2 to simultaneously have withdrawal access * round-robin for the purposes of; role-based; access mode; access role mailslot; mailbox; allocate/assign/identify slot rights; permission; license; access permission; * PTL/Ticket hybrid Asymmetric usage ; owner oblivious lock-unlock pairing K-exclusion add Grant cursor pass message m from lock to unlock via Slots[] array Cursor performs 2 functions : + PTL ticket + Assigns request to slot in round-robin fashion Deconstruct protocol : explication put() : allocate slot in round-robin fashion acquire PTL for "put" access store message into slot associated with PTL index take() : Acquire PTL for "take" access // doorway step seq = fetchAdd (&Grant, 1) s = &Slots[seq & Mask] // waiting phase while s-Turn != seq : pause Extract : wait for s-mailbox to be full v = s-mailbox s-mailbox = null Release PTL for both "put" and "take" access s-Turn = seq + Mask + 1 * Slot round-robin assignment and lock "doorway" protocol leverage the same cursor and FetchAdd operation on that cursor FetchAdd (&Cursor,1) + round-robin slot assignment and dispersal + PTL/ticket lock "doorway" step waiting phase is via "Turn" field in slot * PTLQueue uses 2 cursors -- put and take. Acquire "put" access to slot via PTL-like lock Acquire "take" access to slot via PTL-like lock 2 locks : put and take -- at most one thread can access slot's mailbox Both locks use same "turn" field Like multilane : 2 cursors : put and take slot is simple 1-capacity mailbox instead of queue Borrow per-slot turn/grant from PTL Provides strict FIFO Lock slot : put-vs-put take-vs-take at most one put accesses slot at any one time at most one put accesses take at any one time reduction to 1-vs-1 instead of N-vs-M concurrency Per slot locks for put/take Release put/take by advancing turn * is instrumental in ... * P-V Semaphore vs lock vs K-exclusion * See also : FastQueues-excerpt.java dice-etc/queue-mpmc-bounded-blocking-circular-xadd/ * PTLQueue is the same as PTLQB - identical * Expedient return; ASAP; prompt; immediately * Lamport's Bakery algorithm : doorway step then waiting phase Threads arriving at doorway obtain a unique ticket number Threads enter in ticket order * In the terminology of Reed and Kanodia a ticket lock corresponds to the busy-wait implementation of a semaphore using an eventcount and a sequencer It can also be thought of as an optimization of Lamport's bakery lock was designed for fault-tolerance rather than performance Instead of spinning on the release counter, processors using a bakery lock repeatedly examine the tickets of their peers --

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  • SUPINFO International University in Mauritius

    Since a while I'm considering to pick up my activities as a student and I'd like to get a degree in Computer Science. Personal motivation I mean after all this years as a professional software (and database) developer I have the personal urge to complete this part of my education. Having various certifications by Microsoft and being awarded as an Microsoft Most Valuable Professional (MVP) twice looks pretty awesome on a resume but having a "proper" degree would just complete my package. During the last couple of years I already got in touch with C-SAC (local business school with degree courses), the University of Mauritius and BCS, the Chartered Institute for IT to check the options to enroll as an experienced software developer. Quite frankly, it was kind of alienating to receive that feedback: Start from scratch! No seriously? Spending x amount of years to sit for courses that might be outdated and form part of your daily routine? Probably being in an awkward situation in which your professional expertise might exceed the lecturers knowledge? I don't know... but if that's path to walk... Well, then I might have to go for it. SUPINFO International University Some weeks ago I was contacted by the General Manager, Education Recruitment and Development of Medine Education Village, Yamal Matabudul, to have a chat on how the local IT scene, namely the Mauritius Software Craftsmanship Community (MSCC), could assist in their plans to promote their upcoming campus. Medine went into partnership with the French-based SUPINFO International University and Mauritius will be the 36th location world-wide for SUPINFO. Actually, the concept of SUPINFO is very likely to the common understanding of an apprenticeship in Germany. Not only does a student enroll into the programme but will also be placed into various internships as part of the curriculum. It's a big advantage in my opinion as the person stays in touch with the daily procedures and workflows in the real world of IT. Statements like "We just received a 'crash course' of information and learned new technology which is equivalent to 1.5 months of lectures at the university" wouldn't form part of the experience of such an education. Open Day at the Medine Education Village Last Saturday, Medine organised their Open Day and it was the official inauguration of the SUPINFO campus in Mauritius. It's now listed on their website, too - but be warned, the site is mainly in French language although the courses are all done in English. Not only was it a big opportunity to "hang out" on the campus of Medine but it was great to see the first professional partners for their internship programme, too. Oh, just for the records, IOS Indian Ocean Software Ltd. will also be among the future employers for SUPINFO students. More about that in an upcoming blog entry. Open Day at Medine Education Village - SUPINFO International University in Mauritius Mr Alick Mouriesse, President of SUPINFO, arrived the previous day and he gave all attendees a great overview of the roots of SUPINFO, the general development of the educational syllabus and their high emphasis on their partnerships with local IT companies in order to assist their students to get future jobs but also feel the heartbeat of technology live. Something which is completely missing in classic institutions of tertiary education in Computer Science. And since I was on tour with my children, as usual during weekends, he also talked about the outlook of having a SUPINFO campus in Mauritius. Apart from the close connection to IT companies and providing internships to students, SUPINFO clearly works on an international level. Meaning students of SUPINFO can move around the globe and can continue their studies seamlessly. For example, you might enroll for your first year in France, then continue to do 2nd and 3rd year in Canada or any other country with a SUPINFO campus to earn your bachelor degree, and then live and study in Mauritius for the next 2 years to achieve a Master degree. Having a chat with Dale Smith, Expand Technologies, after his interesting session on Technological Entrepreneurship - TechPreneur More questions by other craftsmen of the Mauritius Software Craftsmanship Community And of course, this concept works in any direction, giving Mauritian students a huge (!) opportunity to live, study and work abroad. And thanks to this, Medine already announced that there will be new facilities near Cascavelle to provide dormitories and other facilities to international students coming to our island. Awesome! Okay, but why SUPINFO? Well, coming back to my original statement - I'd like to get a degree in Computer Science - SUPINFO has a process called Validation of Acquired Experience (VAE) which is tailor-made for employees in the field of IT, and allows you to enroll in their course programme. I already got in touch with their online support chat but was only redirected to some FAQs on their website, unfortunately. So, during the Open Day I seized the opportunity to have an one-on-one conversation with Alick Mouriesse, and he clearly encouraged me to gather my certifications and working experience. SUPINFO does an individual evaluation prior to their assignment regarding course level, and hopefully my chances of getting some modules ahead of studies are looking better than compared to the other institutes. Don't get me wrong, I don't want to go down the easy route but why should someone sit for "Database 101" or "Principles of OOP" when applying and preaching database normalisation and practicing Clean Code Developer are like flesh and blood? Anyway, I'll be off to get my transcripts of certificates together with my course assignments from the old days at the university. Yes, I studied Applied Chemistry for a couple of years before intersecting into IT and software development particularly... ;-)

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  • A Method for Reducing Contention and Overhead in Worker Queues for Multithreaded Java Applications

    - by Janice J. Heiss
    A java.net article, rich in practical resources, by IBM India Labs’ Sathiskumar Palaniappan, Kavitha Varadarajan, and Jayashree Viswanathan, explores the challenge of writing code in a way that that effectively makes use of the resources of modern multicore processors and multiprocessor servers.As the article states: “Many server applications, such as Web servers, application servers, database servers, file servers, and mail servers, maintain worker queues and thread pools to handle large numbers of short tasks that arrive from remote sources. In general, a ‘worker queue’ holds all the short tasks that need to be executed, and the threads in the thread pool retrieve the tasks from the worker queue and complete the tasks. Since multiple threads act on the worker queue, adding tasks to and deleting tasks from the worker queue needs to be synchronized, which introduces contention in the worker queue.” The article goes on to explain ways that developers can reduce contention by maintaining one queue per thread. It also demonstrates a work-stealing technique that helps in effectively utilizing the CPU in multicore systems. Read the rest of the article here.

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  • How Does AutoPatch Handle Shared E-Business Suite Products?

    - by Steven Chan
    Space... is big. Really big. You just won't believe how vastly hugely mindbogglingly big it is.~ Douglas AdamsDouglas Adams could have been talking about the E-Business Suite.  Depending upon whom you ask (and how you count them), there are between 200 to 240 products in Oracle E-Business Suite.  The products that make up Oracle E-Business Suite are tightly integrated. Some of these products are known as shared or dependent products. Installed and registered automatically by Rapid Install, such products depend on components from other products for full functionality.For example:General Ledger (GL) depends on Application Object Library (FND) and Oracle Receivables (AR)Inventory (INV) depends on FND and GLReceivables (AR) depends on FND, INV, and GLIt can sometimes be challenging to craft a patching strategy for these types of product dependencies.  To help you with that, our Applications Database (AD) team has recently published a new document that describes the actions AutoPatch takes with shared Oracle E-Business Suite products:Patching Shared Oracle E-Business Suite Products (Note 1069099.1)

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  • System Wide Performance Sanity Check Procedures

    - by user702295
    Do you need to boost your overall implementation performance? Do you need a direction to pinpoint possible performance opportunities? Are you looking for a general performance guide? Try MOS note 69565.1.  This paper describes a holistic methodology that defines a systematic approach to resolve complex Application performance problems.  It has been successfully used on many critical accounts.  The 'end-to-end' tuning approach encompasses the client, network and database and has proven far more effective than isolated tuning exercises.  It has been used to define and measure targets to ensure success.  Even though it was checked for relevance on 13-Oct-2008, the procedure is still very valuable. Regards!  

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  • Performance triage

    - by Dave
    Folks often ask me how to approach a suspected performance issue. My personal strategy is informed by the fact that I work on concurrency issues. (When you have a hammer everything looks like a nail, but I'll try to keep this general). A good starting point is to ask yourself if the observed performance matches your expectations. Expectations might be derived from known system performance limits, prototypes, and other software or environments that are comparable to your particular system-under-test. Some simple comparisons and microbenchmarks can be useful at this stage. It's also useful to write some very simple programs to validate some of the reported or expected system limits. Can that disk controller really tolerate and sustain 500 reads per second? To reduce the number of confounding factors it's better to try to answer that question with a very simple targeted program. And finally, nothing beats having familiarity with the technologies that underlying your particular layer. On the topic of confounding factors, as our technology stacks become deeper and less transparent, we often find our own technology working against us in some unexpected way to choke performance rather than simply running into some fundamental system limit. A good example is the warm-up time needed by just-in-time compilers in Java Virtual Machines. I won't delve too far into that particular hole except to say that it's rare to find good benchmarks and methodology for java code. Another example is power management on x86. Power management is great, but it can take a while for the CPUs to throttle up from low(er) frequencies to full throttle. And while I love "turbo" mode, it makes benchmarking applications with multiple threads a chore as you have to remember to turn it off and then back on otherwise short single-threaded runs may look abnormally fast compared to runs with higher thread counts. In general for performance characterization I disable turbo mode and fix the power governor at "performance" state. Another source of complexity is the scheduler, which I've discussed in prior blog entries. Lets say I have a running application and I want to better understand its behavior and performance. We'll presume it's warmed up, is under load, and is an execution mode representative of what we think the norm would be. It should be in steady-state, if a steady-state mode even exists. On Solaris the very first thing I'll do is take a set of "pstack" samples. Pstack briefly stops the process and walks each of the stacks, reporting symbolic information (if available) for each frame. For Java, pstack has been augmented to understand java frames, and even report inlining. A few pstack samples can provide powerful insight into what's actually going on inside the program. You'll be able to see calling patterns, which threads are blocked on what system calls or synchronization constructs, memory allocation, etc. If your code is CPU-bound then you'll get a good sense where the cycles are being spent. (I should caution that normal C/C++ inlining can diffuse an otherwise "hot" method into other methods. This is a rare instance where pstack sampling might not immediately point to the key problem). At this point you'll need to reconcile what you're seeing with pstack and your mental model of what you think the program should be doing. They're often rather different. And generally if there's a key performance issue, you'll spot it with a moderate number of samples. I'll also use OS-level observability tools to lock for the existence of bottlenecks where threads contend for locks; other situations where threads are blocked; and the distribution of threads over the system. On Solaris some good tools are mpstat and too a lesser degree, vmstat. Try running "mpstat -a 5" in one window while the application program runs concurrently. One key measure is the voluntary context switch rate "vctx" or "csw" which reflects threads descheduling themselves. It's also good to look at the user; system; and idle CPU percentages. This can give a broad but useful understanding if your threads are mostly parked or mostly running. For instance if your program makes heavy use of malloc/free, then it might be the case you're contending on the central malloc lock in the default allocator. In that case you'd see malloc calling lock in the stack traces, observe a high csw/vctx rate as threads block for the malloc lock, and your "usr" time would be less than expected. Solaris dtrace is a wonderful and invaluable performance tool as well, but in a sense you have to frame and articulate a meaningful and specific question to get a useful answer, so I tend not to use it for first-order screening of problems. It's also most effective for OS and software-level performance issues as opposed to HW-level issues. For that reason I recommend mpstat & pstack as my the 1st step in performance triage. If some other OS-level issue is evident then it's good to switch to dtrace to drill more deeply into the problem. Only after I've ruled out OS-level issues do I switch to using hardware performance counters to look for architectural impediments.

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