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• Use jQuery and ASP.NET to Build a News Ticker

  Many websites display a news ticker of one sort or another. A news ticker is a user interface element that displays a subset of a list of items, cycling through them one at a time after a set interval. For example, on Cisco's website there is a news ticker that shows the company's latest news items. Each news item is a one sentence link, such as "Desktop Virtualization Gathers Steam," or "Cisco Reports First Quarter Earnings." Clicking a headline whisks you to a page that shows the full story. Cisco's news ticker shows one headline at a time; every few seconds the currently displayed headline fades out and the next one appears. In total, Cisco has five different headlines - the ticker displays each of the five and then starts back from the beginning. This article is the first in a series that explores how to create your own news ticker widget using jQuery and ASP.NET. jQuery is a free, popular, open-source JavaScript library that simplifies many common client-side tasks, like event handling, DOM manipulation, and Ajax. This article kicks off the series and shows how to build a fairly simple news ticker whose contents can be specified statically in HTML markup or created dynamically from server-side code. Future installments will explore adding bells and whistles, such as: stopping the news ticker rotation when the mouse is hovered over it; adding controls to start, stop and pause the headlines; loading new headlines dynamically using Ajax; and packaging the JavaScript used by the ticker into a jQuery plugin. Read on to learn more! Read More >

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• Pausing and Resuming the jQuery / ASP.NET News Ticker

  Many websites display a news ticker of one sort or another. A news ticker is a user interface element that displays a subset of a list of items, cycling through them one at a time after a set interval. In December 2010 I wrote an article titled Use jQuery and ASP.NET to Build a News Ticker that explored how to create your own news ticker widget using jQuery and ASP.NET. The news ticker's content is defined as an unordered list (<ul>) where each list item (<li>) represents a news headline. Once the ticker's content is defined, having it cycle through the head lines is as simple as calling the JavaScript function startTicker(id, numberToShow, duration), which begins cycling the headlines in the unordered list with the specified id, showing numberToShow headlines at a time and cycling to the next headline every duration number of milliseconds. This installment shows how to enhance the news ticker to enable pausing and resuming. With these enhancements, the ticker can be configured to automatically pause rotating its headlines when the user mouses over it, and to resume rotating them once the user mouses out. Similarly, with a bit of additional markup and script you can add pause and play buttons to a ticker, allowing a user to start and stop the ticker by clicking an image or button. Read on to learn more! Read More >

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• Price Drop for Processor based License on Exalytics

  - by Mike.Hallett(at)Oracle-BI&EPM

  ·       33% reduction in the list `per processor` license pricing for the Oracle BI Foundation Suite ·       New capacity-based licensing which allows customers to think big & start small, significantly lowering the entry price point for an Exalytics. Oracle BI Software List Price changes In response to new powerful platforms like the in-memory Oracle Exalytics with 40 cpu cores (counted under Oracle pricing policy as 20 “processors”), the list price of “Oracle BI Foundation Suite” (BIFS) is reduced by 33% from $450K per processor to $300K per processor. Capacity-based licensing on Exalytics (Trusted Partitions) “Capacity-based pricing” for the BIFS, Endeca, Essbase and Times Ten for Exalytics software is now available for Exalytics systems. This is delivered using “Oracle VM” (OVM).  We still ship a full Exalytics machine to all customers, but they may choose to only use and license a subset of the processors installed in the machine.   Customers can license Exalytics software in units of 5 “processors”: 5, 10, 15 or the full capacity 20.   As the customer’s implementation and workload increases, it is a simple matter to license additional processors and, using OVM, make them available to the BI or EPM application. Endeca Information Discovery now available on Exalytics Oracle has also announced the certification of “Oracle Endeca Information Discovery” (EID) on the Exalytics machine.    EID can be licensed alone or in combination with the BIFS & Times Ten for an Exalytics stack, and also participates in the capacity based pricing outlined above.   The Exalytics hardware is the perfect platform for EID, and provides superb power and performance for this in-memory hybrid text-search-analytics.   For more information : Oracle Price lists Oracle Partitioning Policy Discussion by Mark Rittman (Rittman Mead Consulting ltd.) on Oracle Trusted Partitions for Oracle Engineered Systems, Oracle Exalytics and Updated BI Foundation Pricing.

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• Microsoft Public License Question

  - by ryanzec

  Let preface this by saying that I understand that any advice I may receive is not to be taken as 100% correct, I am just looking for what people's understand of what this license is. I have been looking for a library that allow be to deal with archived compressed files (like zip files) and so far the best one I have found is DotNetZip. The only concern I have is that I am not familiar with the Microsoft Public License. While I plan to release a portion of my project (a web application platform) freely (MIT/BSD style) there are a few things. One is that I don't plan on actually releasing the source code, just the compiled project. Another thing is that I don't plan on releasing everything freely, only a subset of the application. Those are reason why I stay away form (L)GPL code. Is this something allowed while using 3rd party libraries that are licensed under the Microsoft Public License? EDIT The part about the Microsoft license that concerns me is Section 3 (D) which says (full license here): If you distribute any portion of the software in source code form, you may do so only under this license by including a complete copy of this license with your distribution. If you distribute any portion of the software in compiled or object code form, you may only do so under a license that complies with this license. I don't know what is meant by 'software'. My assumption would be that 'software' only refers to the library included under the license (being DotNetZip) and that is doesn't extends over to my code which includes the DotNetZip library. If that is the case then everything is fine as I have no issues keeping the license for DotNetZip when release this project in compiled form while having my code under its own license. If 'software' also include my code that include the DotNetZip library then that would be an issue (as it would basically act like GPL with the copyleft sense).

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• Messages do not always appear in [catalog].[event_messages] in the order that they occur [SSIS]

  - by jamiet

  This is a simple heads up for anyone doing SQL Server Integration Services (SSIS) development using SSIS 2012. Be aware that messages do not always appear in [catalog].[event_messages] in the order that they occur, observe… In the following query I am looking at a subset of messages in [catalog].[event_messages] and ordering them by [event_message_id]: SELECT [event_message_id],[event_name],[message_time],[message_source_name]FROM   [catalog].[event_messages] emWHERE  [event_message_id] BETWEEN 290972 AND 290982ORDER  BY [event_message_id] ASC--ORDER BY [message_time] ASC Take a look at the two rows that I have highlighted, note how the OnPostExecute event for “Utility GetTargetLoadDatesPerETLIfcName” appears after the OnPreExecute event for “FELC Loop over TargetLoadDates”, I happen to know that this is incorrect because “Utility GetTargetLoadDatesPerETLIfcName” is a package that gets executed by an Execute Package Task prior to the For Each Loop “FELC Loop over TargetLoadDates”: If we order instead by [message_time] then we see something that makes more sense: SELECT [event_message_id],[event_name],[message_time],[message_source_name]FROM   [catalog].[event_messages] emWHERE  [event_message_id] BETWEEN 290972 AND 290982--ORDER BY [event_message_id] ASCORDER  BY [message_time] ASC We can see that the OnPostExecute for “Utility GetTargetLoadDatesPerETLIfcName” did indeed occur before the OnPreExecute event for “FELC Loop over TargetLoadDates”, they just did not get assigned an [event_message_id] in chronological order. We can speculate as to why that might be (I suspect the explanation is something to do with the two executables appearing in different packages) but the reason is not the important thing here, just be aware that you should be ordering by [message_time] rather than [event_message_id] if you want to get 100% accurate insights into your executions. @Jamiet

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• Is there an alternative to SDL 1.3 for a C++ game that should run on iOS and Android?

  - by futlib

  I've used SDL for many desktop games, always as the cross-platform glue for: Creating a window Processing input Rendering images Rendering fonts Playing sounds/music It has never disappointed me at those tasks. But when it comes to graphics, I prefer to work with the OpenGL API directly, even though all of our games are 2D. In the project I'm currently working on, I've made sure to only use the API subset supported by both OpenGL 1.3 and OpenGL 1.0, so making the thing run on Android should be easy, I thought. Turns out there is no official Android or iOS port of SDL yet. However, there's one in SDL 1.3, which is still in development. SDL 1.3 doesn't seem very appealing to me for three reasons: It's been in development for at least 4 years, and I have no idea when it will be done, not to mention stable. It's not ported to as many platforms as SDL 1.2. From what I've seen, it uses OpenGL for drawing, so I suppose the community will move away from directly using OpenGL. So I'm wondering if I should use a different library for our current project - it doesn't matter much if I need to port my existing code from SDL 1.2 to SDL 1.3 or to some other library. We're planning to release on Windows, Mac OS X, Linux, iOS and Android, so good support for these platforms is essential. Is there anything stable that does what I want?

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• Setting up Clojure Project And Sub Projects

  - by octopusgrabbus

  This is primarily a lein question about setting up a major project and its sub-projects, and is not intended to be a discussion question. Instead, I am interested in either a pointer to documentation or to a Clojure/lein best practices link. I have a municipal property assessments application that splits two master flies into different subset files, depending on whether a billing transfer is taking place or we want to batch update new accounts, rather than making our assessment department enter new accounts once in their system and then again in the tax collection system. My application is going to be large enough, that I can see a common library lein project with support functions, like splitting apart the files, and then individual lein projects that use the common library. Should the lein projects be set up at the same level and support included through the project.clj/core.clj files? Is there an advantage to creating lein new projects underneath a major project? Is there a problem with combing all functions in one project? I can probably make my one core.clj contain all flavors of the program, but coming from a C/C++ and Python background, I would prefer to have a lot of little projects.

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• ASP.NET design not SOLID

  - by w0051977

  SOLID principles are described here: http://en.wikipedia.org/wiki/SOLID_%28object-oriented_design%29 I am developing a large ASP.NET app. The previous developer created a few very large classes each with lots of different purposes. It is very difficult to maintain and extend. The classes are deployed to the web server along with the code behind files etc. I want to share a small amount of the app with another application. I am considering moving all of the classes of the ASP.NET web app to a DLL, so the small subset of functionality can be shared. I realise it would be better to only share the classes which contain code to be shared but because of the dependencies this is proving to be very difficult e.g. class A contains code that should be shared, however class A contains references to classes B, C, D, E, F, G etc, so class A cannot be shared on its own. I am planning to refactor the code in the future. As a temporary solution I am planning to convert all the classes into a single class library. Is this a bad idea and if so, is there an alternative? as I don't have time to refactor at the moment.

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• Are DDD Aggregates really a good idea in a Web Application?

  - by Mystere Man

  I'm diving in to Domain Driven Design and some of the concepts i'm coming across make a lot of sense on the surface, but when I think about them more I have to wonder if that's really a good idea. The concept of Aggregates, for instance makes sense. You create small domains of ownership so that you don't have to deal with the entire domain model. However, when I think about this in the context of a web app, we're frequently hitting the database to pull back small subsets of data. For instance, a page may only list the number of orders, with links to click on to open the order and see its order id's. If i'm understanding Aggregates right, I would typically use the repository pattern to return an OrderAggregate that would contain the members GetAll, GetByID, Delete, and Save. Ok, that sounds good. But... If I call GetAll to list all my order's, it would seem to me that this pattern would require the entire list of aggregate information to be returned, complete orders, order lines, etc... When I only need a small subset of that information (just header information). Am I missing something? Or is there some level of optimization you would use here? I can't imagine that anyone would advocate returning entire aggregates of information when you don't need it. Certainly, one could create methods on your repository like GetOrderHeaders, but that seems to defeat the purpose of using a pattern like repository in the first place. Can anyone clarify this for me?

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• How to handle fine grained field-based ACL permissions in a RESTful service?

  - by Jason McClellan

  I've been trying to design a RESTful API and have had most of my questions answered, but there is one aspect of permissions that I'm struggling with. Different roles may have different permissions and different representations of a resource. For example, an Admin or the user himself may see more fields in his own User representation vs another less-privileged user. This is achieved simply by changing the representation on the backend, ie: deciding whether or not to include those fields. Additionally, some actions may be taken on a resource by some users and not by others. This is achieved by deciding whether or not to include those action items as links, eg: edit and delete links. A user who does not have edit permissions will not have an edit link. That covers nearly all of my permission use cases, but there is one that I've not quite figured out. There are some scenarios whereby for a given representation of an object, all fields are visible for two or more roles, but only a subset of those roles my edit certain fields. An example: { "person": { "id": 1, "name": "Bob", "age": 25, "occupation": "software developer", "phone": "555-555-5555", "description": "Could use some sunlight.." } } Given 3 users: an Admin, a regular User, and Bob himself (also a regular User), I need to be able to convey to the front end that: Admins may edit all fields, Bob himself may edit all fields, but a regular User, while they can view all fields, can only edit the description field. I certainly don't want the client to have to make the determination (or even, for that matter, to have any notion of the roles involved) but I do need a way for the backend to convey to the client which fields are editable. I can't simply use a combination of representation (the fields returned for viewing) and links (whether or not an edit link is availble) in this scenario since it's more finely grained. Has anyone solved this elegantly without adding the logic directly to the client?

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• ArchBeat Link-o-Rama for December 12, 2012

  - by Bob Rhubart

  “Cloud Integration in Minutes” – True or False? | Bruce Tierney The answer is 'True, but..." according to Bruce Tierney. "Connecting on-premise and cloud applications “in minutes” is true…provided you only consider the connectivity subset of integration and have a small number of cloud integration touch points." Get the rest of the story in Bruce's detailed post. Tech World Discovers New Species: The Cloud Architect | Wired Enterprise | Wired.com This Wired article by Cade Metz boils down to one essential conclusion: Cloud computing is a significant departure from "data center designs of the past," and the demand for the specialized skills of the cloud architect will only increase. But you already knew that, right? Oracle B2B - Synchronous Request Reply | A-Team - SOA "Beginning with Oracle SOA Suite PS5 (11.1.1.6), B2B supports synchronous request reply over http using the b2b/syncreceiver servlet," says C. D. Wright of the Fusion Middleware A-Team. His post includes a demo and everything you need to run it. Thought for the Day "Don't worry about what anybody else is going to do… The best way to predict the future is to invent it." — Alan Kay (Month Day, Year - Month Day, Year) Source: SoftwareQuotes.com

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• Should business services cross bounded contexts?

  - by Paul T Davies

  Firstly, I am following the convention that a bounded context is synonymous to a department, or possibly one department has 1 to many bounded contexts. We have a client consultancy department that has a Documentation Service. Documents are stored in the Document Store Service (which is where all documents in the company are stored - it is a utility service), and the Documentation Service stores information about that document (a business service). As it was designed for the client consultancy, it is information relevant to them. Now health and safety need somewhere to store information about a document. This is different information to client consultancy, but I have been instructed to extend the existing service to account for this extra information. I feel this service is now crossing a bounded context. My worry is that all departments will eventually store there information in here and the service will become bloated, trying to be all things to all departments. Each document record will only store a subset of the information because it will only belong to one department. It will get worse when different departments want to store the same information but refer to it in a diferent ways, or when two departments want to store different information that they refer to in the same way. In my understanding, this is exactly the reason for bounded contexts. I feel each department should have it's own business service for information about a document, but use the same utility service to actually store the document. What would be the correct approach?

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• IoT: Wearables!

  - by Tori Wieldt

  Wearables are a subset of the Internet of Things that has gained a lot of attention. Wearables can monitor your infant's heartrate, open your front door, or warn you when someone's trying to hack your enterprise network. From Devoxx UK to Oracle OpenWorld to Devoxx4kids, everyone seems to be doing something with wearables.  In this video, John McLear introduces the NFC Ring. It can be used to unlock doors, mobile phones, transfer information and link people. The software for developers is open source, so get coding! If you are coming to JavaOne or Oracle OpenWorld, join us for Dress Code 2.0, a wearables meetup. Put on your best wearables gear and come hang out with the Oracle Applications User Experience team and friends at the OTN Lounge. We'll discuss the finer points of use cases, APIs, integrations, UX design, and fashion and style considerations for wearable tech development. There will be gifts for attendees sporting wearable tech, while supplies last. What: Dress Code 2.0: A Wearables Meetup When: Tuesday, 30-September-2014, 4-6 PM Where: OTN Lounge at Oracle OpenWorld IoT - Wearable Resources The IoT Community on Java.net Wearables in the World of Enterprise Applications? Yep. The Paradox of Wearable Technologies Conference: Wearable Sensors and Electronics (Santa Clara, USA) Devoxx4Kids Workshop for Youth: Wearable tech! (Mountain View, USA)

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• Cloning from a given point in the snapshot tree

  - by Fat Bloke

  Although we have just released VirtualBox 4.3, this quick blog entry is about a longer standing ability of VirtualBox when it comes to Snapshots and Cloning, and was prompted by a question posed internally, here in Oracle: "Is there a way I can create a new VM from a point in my snapshot tree?". Here's the scenario: Let's say you have your favourite work VM which is Oracle Linux based and as you installed different packages, such as database, middleware, and the apps, you took snapshots at each point like this: But you then need to create a new VM for some other testing or to share with a colleague who will be using the same Linux and Database layers but may want to reconfigure the Middleware tier, and may want to install his own Apps. All you have to do is right click on the snapshot that you're happy with and clone: Give the VM that you are about to create a name, and if you plan to use it on the same host machine as the original VM, it's a good idea to "Reinitialize the MAC address" so there's no clash on the same network: Now choose the Clone type. If you plan to use this new VM on the same host as the original, you can use Linked Cloning else choose Full.  At this point you now have a choice about what to do about your snapshot tree. In our example, we're happy with the Linux and Database layers, but we may want to allow our colleague to change the upper tiers, with the option of reverting back to our known-good state, so we'll retain the snapshot data in the new VM from this point on: The cloning process then chugs along and may take a while if you chose a Full Clone: Finally, the newly cloned VM is ready with the subset of the Snapshot tree that we wanted to retain: Pretty powerful, and very useful.  Cheers, -FB 

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• REST API wrapper - class design for 'lite' object responses

  - by sasfrog

  I am writing a class library to serve as a managed .NET wrapper over a REST API. I'm very new to OOP, and this task is an ideal opportunity for me to learn some OOP concepts in a real-life situation that makes sense to me. Some of the key resources/objects that the API returns are returned with different levels of detail depending on whether the request is for a single instance, a list, or part of a "search all resources" response. This is obviously a good design for the REST API itself, so that full objects aren't returned (thus increasing the size of the response and therefore the time taken to respond) unless they're needed. So, to be clear: .../car/1234.json returns the full Car object for 1234, all its properties like colour, make, model, year, engine_size, etc. Let's call this full. .../cars.json returns a list of Car objects, but only with a subset of the properties returned by .../car/1234.json. Let's call this lite. ...search.json returns, among other things, a list of car objects, but with minimal properties (only ID, make and model). Let's call this lite-lite. I want to know what the pros and cons of each of the following possible designs are, and whether there is a better design that I haven't covered: Create a Car class that models the lite-lite properties, and then have each of the more detailed responses inherit and extend this class. Create separate CarFull, CarLite and CarLiteLite classes corresponding to each of the responses. Create a single Car class that contains (nullable?) properties for the full response, and create constructors for each of the responses which populate it to the extent possible (and maybe include a property that returns the response type from which the instance was created). I expect among other things there will be use cases for consumers of the wrapper where they will want to iterate through lists of Cars, regardless of which response type they were created from, such that the three response types can contribute to the same list. Happy to be pointed to good resources on this sort of thing, and/or even told the name of the concept I'm describing so I can better target my research.

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• Using foldr to append two lists together (Haskell)

  - by Luke Murphy

  I have been given the following question as part of a college assignment. Due to the module being very short, we are using only a subset of Haskell, without any of the syntactic sugar or idiomatic shortcuts....I must write: append xs ys : The list formed by joining the lists xs and ys, in that order append (5:8:3:[]) (4:7:[]) => 5:8:3:4:7:[] I understand the concept of how foldr works, but I am only starting off in Functional programming. I managed to write the following working solution (hidden for the benefit of others in my class...) : However, I just can't for the life of me, explain what the hell is going on!? I wrote it by just fiddling around in the interpreter, for example, the following line : foldr (\x -> \y -> x:y) [] (2:3:4:[]) which returned [2:3:4] , which led me to try, foldr (\x -> \y -> x:y) (2:3:4:[]) (5:6:7:[]) which returned [5,6,7,2,3,4] so I worked it out from there. I came to the correct solution through guess work and a bit of luck... I am working from the following definition of foldr: foldr = \f -> \s -> \xs -> if null xs then s else f (head xs) (foldr f s (tail xs) ) Can someone baby step me through my correct solution? I can't seem to get it....I already have scoured the web, and also read a bunch of SE threads, such as How foldr works

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• Combinatorial explosion of interfaces: How many is too many?

  - by mga

  I'm a relative newcomer to OOP, and I'm having a bit of trouble creating good designs when it comes to interfaces. Consider a class A with N public methods. There are a number of other classes, B, C, ..., each of which interacts with A in a different way, that is, accesses some subset (<= N) of A's methods. The maximum degree of encapsulation is achieved by implementing an interface of A for each other class, i.e. AInterfaceForB, AInterfaceForC, etc. However, if B, C, ... etc. also interact with A and with each other, then there will be a combinatorial explosion of interfaces (a maximum of n(n-1), to be precise), and the benefit of encapsulation becomes outweighed by a code-bloat. What is the best practice in this scenario? Is the whole idea of restricting access to a class's public functions in different ways for other different classes just silly altogether? One could imagine a language that explicitly allows for this sort of encapsulation (e.g. instead of declaring a function public, one could specify exactly which classes it is visible to); Since this is not a feature of C++, maybe it's misguided to try to do it through the back door with interaces?

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• Writing generic code when your target is a C compiler

  - by enobayram

  I need to write some algorithms for a PIC micro controller. AFAIK, the official tools support either assembler or a subset of C. My goal is to write the algorithms in a generic and reusable way without losing any runtime or memory performance. And if possible, I would like to do this without increasing the development time much and compromising the readability and maintainability much either. What I mean by generic and reusable is that I don't want to commit to types, array sizes, number of bits in a bit field etc. All these specifications, IMHO, point to C++ templates, but there's no compiler for it for my target. C macro metaprogramming is another option, but, again my opinion, that greatly reduces readability and increases development time. I believe what I'm looking for is a decent C++ to C translator, but I'd like to hear anything else that satisfies the above requirements. Maybe a translator from another high-level language to C that produces very efficient code, maybe something else. Please note that I have nothing against C, I just wish templates were available in it.

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• How to encourage domain experts familiar only with C into a C++ opensource project [closed]

  - by paperjam

  Possible Duplicate: How to persuade C fanatics to work on my C++ open source project? I am launching an open-source project into a space where a lot of development is done Linux-kernel-style, i.e. C-language with a low-level mindset. My project is broad and complex and uses aspects of the C++ language and libraries, including the Boost library to best effect for simple, slightly syntactically sweetened, elegant and well structured high level code. We are using C++ templates too to avoid duplication of code and for static polymorphism in code specialisation for performance. Many of the experts in this field are well used to pure C-language projects. How can I persuade them to contribute to my idiomatic C++ based project? I have no objection to C-language subcomponents or the use of a C-like subset for parts of the project so that might be part of the answer. This is a rewritten and retagged rehash of my previous question that was closed. Apologies to those who read and answered for it not being constructive. I hope this new question is viewed as constructive. Please note that this is not a language advocacy question and please keep answers in that spirit.

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• cookie not being sent when requesting JS

  - by Mala

  I host a webservice, and provide my members with a Javascript bookmarklet, which loads a JS sript from my server. However, clients must be logged in, in order to receive the JS script. This works for almost everybody. However, some users on setups (i.e. browser/OS) that are known to work for other people have the following problem: when they request the script via the javascript bookmarklet from my server, their cookie from my server does not get included with the request, and as such they are always "not authenticated". I'm making the request in the following way: var myScript = eltCreate('script'); myScript.setAttribute('src','http://myserver.com/script'); document.body.appendChild(myScript); In a fit of confused desperation, I changed the script page to simply output "My cookie has [x] elements" where [x] is count($_COOKIE). If this extremely small subset of users requests the script via the normal method, the message reads "My cookie has 0 elements". When they access the URL directly in their browser, the message reads "My cookie has 7 elements". What on earth could be going on?!

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• Release Notes for 7/6/2012

  Happy belated 4th of July, everyone! Here are the notes for this week’s release on CodePlex: Implemented performance improvements to Git repositories. Fixed an issue that caused the final “click here” download link to fail in projects that display ads. Fixed an issue for certain projects that made it impossible to edit releases. Fixed an issue where the URL for a diff of a file would not take users to the diff in question. Fixed a rare issue that prevented a small subset of projects from modifying their project details. Fixed an issue where scrollbars were missing in our side-by-side diff viewer. Super- and sub-scripts now work properly in documentation. Addressed several usability issues around the diff viewer. Fixed an issue where the scrollbar could disappear in the advanced issue tracker if a user opens a modal dialog. Have ideas on how to improve CodePlex? Visit our ideas page! Vote for your favorite ideas or submit a new one. Got Twitter? Follow us and keep apprised of the latest releases and service status at @codeplex.

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• opengl problem works on droid but not droid eris and others.

  - by nathan

  This GlRenderer works fine on the moto droid, but does not work well at all on droid eris or other android phones does anyone know why? package com.ntu.way2fungames.spacehockeybase; import java.io.DataInputStream; import java.io.IOException; import java.nio.Buffer; import java.nio.FloatBuffer; import javax.microedition.khronos.egl.EGLConfig; import javax.microedition.khronos.opengles.GL10; import com.ntu.way2fungames.LoadFloatArray; import com.ntu.way2fungames.OGLTriReader; import android.content.res.AssetManager; import android.content.res.Resources; import android.opengl.GLU; import android.opengl.GLSurfaceView.Renderer; import android.os.Handler; import android.os.Message; public class GlRenderer extends Thread implements Renderer { private float drawArray[]; private float yoff; private float yoff2; private long lastRenderTime; private float[] yoffs= new float[10]; int Width; int Height; private float[] pixelVerts = new float[] { +.0f,+.0f,2, +.5f,+.5f,0, +.5f,-.5f,0, +.0f,+.0f,2, +.5f,-.5f,0, -.5f,-.5f,0, +.0f,+.0f,2, -.5f,-.5f,0, -.5f,+.5f,0, +.0f,+.0f,2, -.5f,+.5f,0, +.5f,+.5f,0, }; @Override public void run() { } private float[] arenaWalls = new float[] { 8.00f,2.00f,1f,2f,2f,1f,2.00f,8.00f,1f,8.00f,2.00f,1f,2.00f,8.00f,1f,8.00f,8.00f,1f, 2.00f,8.00f,1f,2f,2f,1f,0.00f,0.00f,0f,2.00f,8.00f,1f,0.00f,0.00f,0f,0.00f,10.00f,0f, 8.00f,8.00f,1f,2.00f,8.00f,1f,0.00f,10.00f,0f,8.00f,8.00f,1f,0.00f,10.00f,0f,10.00f,10.00f,0f, 2f,2f,1f,8.00f,2.00f,1f,10.00f,0.00f,0f,2f,2f,1f,10.00f,0.00f,0f,0.00f,0.00f,0f, 8.00f,2.00f,1f,8.00f,8.00f,1f,10.00f,10.00f,0f,8.00f,2.00f,1f,10.00f,10.00f,0f,10.00f,0.00f,0f, 10.00f,10.00f,0f,0.00f,10.00f,0f,0.00f,0.00f,0f,10.00f,10.00f,0f,0.00f,0.00f,0f,10.00f,0.00f,0f, 8.00f,6.00f,1f,8.00f,4.00f,1f,122f,4.00f,1f,8.00f,6.00f,1f,122f,4.00f,1f,122f,6.00f,1f, 8.00f,6.00f,1f,122f,6.00f,1f,120f,7.00f,0f,8.00f,6.00f,1f,120f,7.00f,0f,10.00f,7.00f,0f, 122f,4.00f,1f,8.00f,4.00f,1f,10.00f,3.00f,0f,122f,4.00f,1f,10.00f,3.00f,0f,120f,3.00f,0f, 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10.00f,854f,0f,0.00f,854f,0f,0.00f,844f,0f,10.00f,854f,0f,0.00f,844f,0f,10.00f,844f,0f, 8.00f,846f,1f,8.00f,852f,1f,10.00f,854f,0f,8.00f,846f,1f,10.00f,854f,0f,10.00f,844f,0f, 2f,846f,1f,8.00f,846f,1f,10.00f,844f,0f,2f,846f,1f,10.00f,844f,0f,0.00f,844f,0f, 8.00f,852f,1f,2.00f,852f,1f,0.00f,854f,0f,8.00f,852f,1f,0.00f,854f,0f,10.00f,854f,0f, 2.00f,852f,1f,2f,846f,1f,0.00f,844f,0f,2.00f,852f,1f,0.00f,844f,0f,0.00f,854f,0f, 8.00f,846f,1f,2f,846f,1f,2.00f,852f,1f,8.00f,846f,1f,2.00f,852f,1f,8.00f,852f,1f, 6f,846f,1f,4f,846f,1f,4f,8f,1f,6f,846f,1f,4f,8f,1f,6f,8f,1f, 6f,846f,1f,6f,8f,1f,7f,10f,0f,6f,846f,1f,7f,10f,0f,7f,844f,0f, 4f,8f,1f,4f,846f,1f,3f,844f,0f,4f,8f,1f,3f,844f,0f,3f,10f,0f, 474f,8f,1f,474f,846f,1f,473f,844f,0f,474f,8f,1f,473f,844f,0f,473f,10f,0f, 476f,846f,1f,476f,8f,1f,477f,10f,0f,476f,846f,1f,477f,10f,0f,477f,844f,0f, 476f,846f,1f,474f,846f,1f,474f,8f,1f,476f,846f,1f,474f,8f,1f,476f,8f,1f, 130f,10.00f,0f,120f,10.00f,0f,120f,0.00f,0f,130f,10.00f,0f,120f,0.00f,0f,130f,0.00f,0f, 128f,2.00f,1f,128f,8.00f,1f,130f,10.00f,0f,128f,2.00f,1f,130f,10.00f,0f,130f,0.00f,0f, 122f,2f,1f,128f,2.00f,1f,130f,0.00f,0f,122f,2f,1f,130f,0.00f,0f,120f,0.00f,0f, 128f,8.00f,1f,122f,8.00f,1f,120f,10.00f,0f,128f,8.00f,1f,120f,10.00f,0f,130f,10.00f,0f, 122f,8.00f,1f,122f,2f,1f,120f,0.00f,0f,122f,8.00f,1f,120f,0.00f,0f,120f,10.00f,0f, 128f,2.00f,1f,122f,2f,1f,122f,8.00f,1f,128f,2.00f,1f,122f,8.00f,1f,128f,8.00f,1f, 352f,8.00f,1f,358f,8.00f,1f,358f,2.00f,1f,352f,8.00f,1f,358f,2.00f,1f,352f,2.00f,1f, 358f,2.00f,1f,358f,8.00f,1f,360f,10.00f,0f,358f,2.00f,1f,360f,10.00f,0f,360f,0.00f,0f, 352f,2.00f,1f,358f,2.00f,1f,360f,0.00f,0f,352f,2.00f,1f,360f,0.00f,0f,350f,0.00f,0f, 358f,8.00f,1f,352f,8.00f,1f,350f,10.00f,0f,358f,8.00f,1f,350f,10.00f,0f,360f,10.00f,0f, 352f,8.00f,1f,352f,2.00f,1f,350f,0.00f,0f,352f,8.00f,1f,350f,0.00f,0f,350f,10.00f,0f, 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10.00f,7.00f,0f,7f,10f,0f,16.64f,27.24f,-0.50f,10.00f,7.00f,0f,16.64f,27.24f,-0.50f,19.51f,24.37f,-0.50f, 350f,10.00f,0f,360f,7.00f,0f,355.04f,24.37f,-0.50f,350f,10.00f,0f,355.04f,24.37f,-0.50f,345.45f,27.24f,-0.50f, 473f,10f,0f,470f,7.00f,0f,460.49f,24.37f,-0.50f,473f,10f,0f,460.49f,24.37f,-0.50f,463.36f,27.24f,-0.50f, 473f,844f,0f,470f,847f,0f,460.49f,829.63f,-0.50f,473f,844f,0f,460.49f,829.63f,-0.50f,463.36f,826.76f,-0.50f, 360f,847f,0f,350f,844f,0f,345.45f,826.76f,-0.50f,360f,847f,0f,345.45f,826.76f,-0.50f,355.04f,829.63f,-0.50f, 130f,844f,0f,120f,847f,0f,124.96f,829.63f,-0.50f,130f,844f,0f,124.96f,829.63f,-0.50f,134.55f,826.76f,-0.50f, 7f,844f,0f,10f,847f,0f,19.51f,829.63f,-0.50f,7f,844f,0f,19.51f,829.63f,-0.50f,16.64f,826.76f,-0.50f, 19.51f,829.63f,-0.50f,124.96f,829.63f,-0.50f,136.47f,789.37f,-2f,19.51f,829.63f,-0.50f,136.47f,789.37f,-2f,41.56f,789.37f,-2f, 134.55f,826.76f,-0.50f,134.55f,836.34f,-0.50f,145.09f,795.41f,-2f,134.55f,826.76f,-0.50f,145.09f,795.41f,-2f,145.09f,786.78f,-2f, 345.45f,826.76f,-0.50f,345.45f,836.34f,-0.50f,334.91f,795.41f,-2f,345.45f,826.76f,-0.50f,334.91f,795.41f,-2f,334.91f,786.78f,-2f, 355.04f,829.63f,-0.50f,460.49f,829.63f,-0.50f,438.44f,789.37f,-2f,355.04f,829.63f,-0.50f,438.44f,789.37f,-2f,343.53f,789.37f,-2f, 460.49f,24.37f,-0.50f,355.04f,24.37f,-0.50f,343.53f,64.63f,-2f,460.49f,24.37f,-0.50f,343.53f,64.63f,-2f,438.44f,64.63f,-2f, 345.45f,27.24f,-0.50f,345.45f,17.66f,-0.50f,334.91f,58.59f,-2f,345.45f,27.24f,-0.50f,334.91f,58.59f,-2f,334.91f,67.22f,-2f, 134.55f,27.24f,-0.50f,134.55f,17.66f,-0.50f,145.09f,58.59f,-2f,134.55f,27.24f,-0.50f,145.09f,58.59f,-2f,145.09f,67.22f,-2f, 463.36f,826.76f,-0.50f,463.36f,27.24f,-0.50f,441.03f,67.22f,-2f,463.36f,826.76f,-0.50f,441.03f,67.22f,-2f,441.03f,786.78f,-2f, 16.64f,27.24f,-0.50f,16.64f,826.76f,-0.50f,38.97f,786.78f,-2f,16.64f,27.24f,-0.50f,38.97f,786.78f,-2f,38.97f,67.22f,-2f, 124.96f,24.37f,-0.50f,19.51f,24.37f,-0.50f,41.56f,64.63f,-2f,124.96f,24.37f,-0.50f,41.56f,64.63f,-2f,136.47f,64.63f,-2f, 124.96f,24.37f,-0.50f,134.55f,27.24f,-0.50f,145.09f,67.22f,-2f,124.96f,24.37f,-0.50f,145.09f,67.22f,-2f,136.47f,64.63f,-2f, 19.51f,24.37f,-0.50f,16.64f,27.24f,-0.50f,38.97f,67.22f,-2f,19.51f,24.37f,-0.50f,38.97f,67.22f,-2f,41.56f,64.63f,-2f, 345.45f,27.24f,-0.50f,355.04f,24.37f,-0.50f,343.53f,64.63f,-2f,345.45f,27.24f,-0.50f,343.53f,64.63f,-2f,334.91f,67.22f,-2f, 463.36f,27.24f,-0.50f,460.49f,24.37f,-0.50f,438.44f,64.63f,-2f,463.36f,27.24f,-0.50f,438.44f,64.63f,-2f,441.03f,67.22f,-2f, 463.36f,826.76f,-0.50f,460.49f,829.63f,-0.50f,438.44f,789.37f,-2f,463.36f,826.76f,-0.50f,438.44f,789.37f,-2f,441.03f,786.78f,-2f, 355.04f,829.63f,-0.50f,345.45f,826.76f,-0.50f,334.91f,786.78f,-2f,355.04f,829.63f,-0.50f,334.91f,786.78f,-2f,343.53f,789.37f,-2f, 134.55f,826.76f,-0.50f,124.96f,829.63f,-0.50f,136.47f,789.37f,-2f,134.55f,826.76f,-0.50f,136.47f,789.37f,-2f,145.09f,786.78f,-2f, 16.64f,826.76f,-0.50f,19.51f,829.63f,-0.50f,41.56f,789.37f,-2f,16.64f,826.76f,-0.50f,41.56f,789.37f,-2f,38.97f,786.78f,-2f, }; private float[] backgroundData = new float[] { // # ,Scale, Speed, 300 , 1.05f, .001f, 150 , 1.07f, .002f, 075 , 1.10f, .003f, 040 , 1.12f, .006f, 20 , 1.15f, .012f, 10 , 1.25f, .025f, 05 , 1.50f, .050f, 3 , 2.00f, .100f, 2 , 3.00f, .200f, }; private float[] triangleCoords = new float[] { 0, -25, 0, -.75f, -1, 0, +.75f, -1, 0, 0, +2, 0, -.99f, -1, 0, .99f, -1, 0, }; private float[] triangleColors = new float[] { 1.0f, 1.0f, 1.0f, 0.05f, 1.0f, 1.0f, 1.0f, 0.5f, 1.0f, 1.0f, 1.0f, 0.5f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 0.5f, 1.0f, 1.0f, 1.0f, 0.5f, }; private float[] drawArray2; private FloatBuffer drawBuffer2; private float[] colorArray2; private static FloatBuffer colorBuffer; private static FloatBuffer triangleBuffer; private static FloatBuffer quadBuffer; private static FloatBuffer drawBuffer; private float[] backgroundVerts; private FloatBuffer backgroundVertsWrapped; private float[] backgroundColors; private Buffer backgroundColorsWraped; private FloatBuffer backgroundColorsWrapped; private FloatBuffer arenaWallsWrapped; private FloatBuffer arenaColorsWrapped; private FloatBuffer arena2VertsWrapped; private FloatBuffer arena2ColorsWrapped; private long wallHitStartTime; private int wallHitDrawTime; private FloatBuffer pixelVertsWrapped; private float[] wallHit; private FloatBuffer pixelColorsWrapped; //private float[] pitVerts; private Resources lResources; private FloatBuffer pitVertsWrapped; private FloatBuffer pitColorsWrapped; private boolean arena2; private long lastStartTime; private long startTime; private int state=1; private long introEndTime; protected long introTotalTime =8000; protected long introStartTime; private boolean initDone= false; private static int stateIntro = 0; private static int stateGame = 1; public GlRenderer(spacehockey nspacehockey) { lResources = nspacehockey.getResources(); nspacehockey.SetHandlerToGLRenderer(new Handler() { @Override public void handleMessage(Message m) { if (m.what ==0){ wallHit = m.getData().getFloatArray("wall hit"); wallHitStartTime =System.currentTimeMillis(); wallHitDrawTime = 1000; }else if (m.what ==1){ //state = stateIntro; introEndTime= System.currentTimeMillis()+introTotalTime ; introStartTime = System.currentTimeMillis(); } }}); } public void onSurfaceCreated(GL10 gl, EGLConfig config) { gl.glShadeModel(GL10.GL_SMOOTH); gl.glClearColor(.01f, .01f, .01f, .1f); gl.glClearDepthf(1.0f); gl.glEnable(GL10.GL_DEPTH_TEST); gl.glDepthFunc(GL10.GL_LEQUAL); gl.glHint(GL10.GL_PERSPECTIVE_CORRECTION_HINT, GL10.GL_NICEST); } private float SumOfStrideI(float[] data, int offset, int stride) { int sum= 0; for (int i=offset;i<data.length-1;i=i+stride){ sum = (int) (data[i]+sum); } return sum; } public void onDrawFrame(GL10 gl) { if (state== stateIntro){DrawIntro(gl);} if (state== stateGame){DrawGame(gl);} } private void DrawIntro(GL10 gl) { startTime = System.currentTimeMillis(); if (startTime< introEndTime){ float ptd = (float)(startTime- introStartTime)/(float)introTotalTime; float ptl = 1-ptd; gl.glClear(GL10.GL_COLOR_BUFFER_BIT);//dont move gl.glMatrixMode(GL10.GL_MODELVIEW); int setVertOff = 0; gl.glEnableClientState(GL10.GL_VERTEX_ARRAY); gl.glEnableClientState(GL10.GL_COLOR_ARRAY); gl.glColorPointer(4, GL10.GL_FLOAT, 0, backgroundColorsWrapped); for (int i = 0; i < backgroundData.length / 3; i = i + 1) { int setoff = i * 3; int setVertLen = (int) backgroundData[setoff]; yoffs[i] = (backgroundData[setoff + 2]*(90+(ptl*250))) + yoffs[i]; if (yoffs[i] > Height) {yoffs[i] = 0;} gl.glPushMatrix(); //gl.glTranslatef(0, -(Height/2), 0); //gl.glScalef(1f, 1f+(ptl*2), 1f); //gl.glTranslatef(0, +(Height/2), 0); gl.glTranslatef(0, yoffs[i], i+60); gl.glVertexPointer(3, GL10.GL_FLOAT, 0, backgroundVertsWrapped); gl.glDrawArrays(GL10.GL_TRIANGLES, (setVertOff * 2 * 3) - 0, (setVertLen * 2 * 3) - 1); gl.glTranslatef(0, -Height, 0); gl.glDrawArrays(GL10.GL_TRIANGLES, (setVertOff * 2 * 3) - 0, (setVertLen * 2 * 3) - 1); setVertOff = (int) (setVertOff + setVertLen); gl.glPopMatrix(); } gl.glDisableClientState(GL10.GL_VERTEX_ARRAY); gl.glDisableClientState(GL10.GL_COLOR_ARRAY); }else{state = stateGame;} } private void DrawGame(GL10 gl) { lastStartTime = startTime; startTime = System.currentTimeMillis(); long moveTime = startTime-lastStartTime; gl.glClear(GL10.GL_COLOR_BUFFER_BIT);//dont move gl.glMatrixMode(GL10.GL_MODELVIEW); int setVertOff = 0; gl.glEnableClientState(GL10.GL_VERTEX_ARRAY); gl.glEnableClientState(GL10.GL_COLOR_ARRAY); gl.glColorPointer(4, GL10.GL_FLOAT, 0, backgroundColorsWrapped); for (int i = 0; i < backgroundData.length / 3; i = i + 1) { int setoff = i * 3; int setVertLen = (int) backgroundData[setoff]; yoffs[i] = (backgroundData[setoff + 2]*moveTime) + yoffs[i]; if (yoffs[i] > Height) {yoffs[i] = 0;} gl.glPushMatrix(); gl.glTranslatef(0, yoffs[i], i+60); gl.glVertexPointer(3, GL10.GL_FLOAT, 0, backgroundVertsWrapped); gl.glDrawArrays(GL10.GL_TRIANGLES, (setVertOff * 6) - 0, (setVertLen *6) - 1); gl.glTranslatef(0, -Height, 0); gl.glDrawArrays(GL10.GL_TRIANGLES, (setVertOff * 6) - 0, (setVertLen *6) - 1); setVertOff = (int) (setVertOff + setVertLen); gl.glPopMatrix(); } //arena frame gl.glPushMatrix(); gl.glVertexPointer(3, GL10.GL_FLOAT, 0, arenaWallsWrapped); gl.glColorPointer(4, GL10.GL_FLOAT, 0, arenaColorsWrapped); gl.glColor4f(.1f, .5f, 1f, 1f); gl.glTranslatef(0, 0, 50); gl.glDrawArrays(GL10.GL_TRIANGLES, 0, (int)(arenaWalls.length / 3)); gl.glPopMatrix(); //arena2 frame if (arena2 == true){ gl.glLoadIdentity(); gl.glVertexPointer(3, GL10.GL_FLOAT, 0, pitVertsWrapped); gl.glColorPointer(4, GL10.GL_FLOAT, 0, pitColorsWrapped); gl.glTranslatef(0, -Height, 40); gl.glDrawArrays(GL10.GL_TRIANGLES, 0, (int)(pitVertsWrapped.capacity() / 3)); } if (wallHitStartTime != 0) { float timeRemaining = (wallHitStartTime + wallHitDrawTime)-System.currentTimeMillis(); if (timeRemaining>0) { gl.glPushMatrix(); float percentDone = 1-(timeRemaining/wallHitDrawTime); gl.glLoadIdentity(); gl.glVertexPointer(3, GL10.GL_FLOAT, 0, pixelVertsWrapped); gl.glColorPointer(4, GL10.GL_FLOAT, 0, pixelColorsWrapped); gl.glTranslatef(wallHit[0], wallHit[1], 0); gl.glScalef(8, Height*percentDone, 0); gl.glDrawArrays(GL10.GL_TRIANGLES, 0, 12); gl.glPopMatrix(); } else { wallHitStartTime = 0; } } gl.glDisableClientState(GL10.GL_VERTEX_ARRAY); gl.glDisableClientState(GL10.GL_COLOR_ARRAY); } public void init(GL10 gl) { if (arena2 == true) { AssetManager assetManager = lResources.getAssets(); try { // byte[] ba = {111,111}; DataInputStream Dis = new DataInputStream(assetManager .open("arena2.ogl")); pitVertsWrapped = LoadFloatArray.FromDataInputStream(Dis); pitColorsWrapped = MakeFakeLighting(pitVertsWrapped.array(), .25f, .50f, 1f, 200, .5f); } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } } if ((Height != 854) || (Width != 480)) { arenaWalls = ScaleFloats(arenaWalls, Width / 480f, Height / 854f); } arenaWallsWrapped = FloatBuffer.wrap(arenaWalls); arenaColorsWrapped = MakeFakeLighting(arenaWalls, .03f, .16f, .33f, .33f, 3); pixelVertsWrapped = FloatBuffer.wrap(pixelVerts); pixelColorsWrapped = MakeFakeLighting(pixelVerts, .03f, .16f, .33f, .10f, 20); initDone=true; } public void onSurfaceChanged(GL10 gl, int nwidth, int nheight) { Width= nwidth; Height = nheight; // avoid division by zero if (Height == 0) Height = 1; // draw on the entire screen gl.glViewport(0, 0, Width, Height); // setup projection matrix gl.glMatrixMode(GL10.GL_PROJECTION); gl.glLoadIdentity(); gl.glOrthof(0, Width, Height, 0, 100, -100); // gl.glOrthof(-nwidth*2, nwidth*2, nheight*2,-nheight*2, 100, -100); // GLU.gluPerspective(gl, 180.0f, (float)nwidth / (float)nheight, // 1000.0f, -1000.0f); gl.glEnable(GL10.GL_BLEND); gl.glBlendFunc(GL10.GL_SRC_ALPHA, GL10.GL_ONE_MINUS_SRC_ALPHA); System.gc(); if (initDone == false){ SetupStars(); init(gl); } } public void SetupStars(){ backgroundVerts = new float[(int) SumOfStrideI(backgroundData,0,3)*triangleCoords.length]; backgroundColors = new float[(int) SumOfStrideI(backgroundData,0,3)*triangleColors.length]; int iii=0; int vc=0; float ascale=1; for (int i=0;i<backgroundColors.length-1;i=i+1){ if (iii==0){ascale = (float) Math.random();} if (vc==3){ backgroundColors[i]= (float) (triangleColors[iii]*(ascale)); }else if(vc==2){ backgroundColors[i]= (float) (triangleColors[iii]-(Math.random()*.2)); }else{ backgroundColors[i]= (float) (triangleColors[iii]-(Math.random()*.3)); } iii=iii+1;if (iii> triangleColors.length-1){iii=0;} vc=vc+1; if (vc>3){vc=0;} } int ii=0; int i =0; int set =0; while(ii<backgroundVerts.length-1){ float scale = (float) backgroundData[(set*3)+1]; int length= (int) backgroundData[(set*3)]; for (i=0;i<length;i=i+1){ if (set ==0){ AddVertsToArray(ScaleFloats(triangleCoords, scale,scale*.25f), backgroundVerts, (float)(Math.random()*Width),(float) (Math.random()*Height), ii); }else{ AddVertsToArray(ScaleFloats(triangleCoords, scale), backgroundVerts, (float)(Math.random()*Width),(float) (Math.random()*Height), ii);} ii=ii+triangleCoords.length; } set=set+1; } backgroundVertsWrapped = FloatBuffer.wrap(backgroundVerts); backgroundColorsWrapped = FloatBuffer.wrap(backgroundColors); } public void AddVertsToArray(float[] sva,float[]dva,float ox,float oy,int start){ //x for (int i=0;i<sva.length;i=i+3){ if((start+i)<dva.length){dva[start+i]= sva[i]+ox;} } //y for (int i=1;i<sva.length;i=i+3){ if((start+i)<dva.length){dva[start+i]= sva[i]+oy;} } //z for (int i=2;i<sva.length;i=i+3){ if((start+i)<dva.length){dva[start+i]= sva[i];} } } public FloatBuffer MakeFakeLighting(float[] sa,float r, float g,float b,float a,float multby){ float[] da = new float[((sa.length/3)*4)]; int vertex=0; for (int i=0;i<sa.length;i=i+3){ if (sa[i+2]>=1){ da[(vertex*4)+0]= r*multby*sa[i+2]; da[(vertex*4)+1]= g*multby*sa[i+2]; da[(vertex*4)+2]= b*multby*sa[i+2]; da[(vertex*4)+3]= a*multby*sa[i+2]; }else if (sa[i+2]<=-1){ float divisor = (multby*(-sa[i+2])); da[(vertex*4)+0]= r / divisor; da[(vertex*4)+1]= g / divisor; da[(vertex*4)+2]= b / divisor; da[(vertex*4)+3]= a / divisor; }else{ da[(vertex*4)+0]= r; da[(vertex*4)+1]= g; da[(vertex*4)+2]= b; da[(vertex*4)+3]= a; } vertex = vertex+1; } return FloatBuffer.wrap(da); } public float[] ScaleFloats(float[] va,float s){ float[] reta= new float[va.length]; for (int i=0;i<va.length;i=i+1){ reta[i]=va[i]*s; } return reta; } public float[] ScaleFloats(float[] va,float sx,float sy){ float[] reta= new float[va.length]; int cnt = 0; for (int i=0;i<va.length;i=i+1){ if (cnt==0){reta[i]=va[i]*sx;} else if (cnt==1){reta[i]=va[i]*sy;} else if (cnt==2){reta[i]=va[i];} cnt = cnt +1;if (cnt>2){cnt=0;} } return reta; } }

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• How to reduce RAM consumption when my server is idle

  - by Julien Genestoux

  We use Slicehost, with 512MB instances. We run Ubuntu 9.10 on them. I installed a few packages, and I'm now trying to optimize RAM consumption before running anything on there. A simple ps gives me the list of running processes : # ps faux USER PID %CPU %MEM VSZ RSS TTY STAT START TIME COMMAND root 2 0.0 0.0 0 0 ? S< Jan04 0:00 [kthreadd] root 3 0.0 0.0 0 0 ? S< Jan04 0:15 \_ [migration/0] root 4 0.0 0.0 0 0 ? S< Jan04 0:01 \_ [ksoftirqd/0] root 5 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [watchdog/0] root 6 0.0 0.0 0 0 ? S< Jan04 0:04 \_ [events/0] root 7 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [cpuset] root 8 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [khelper] root 9 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [async/mgr] root 10 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xenwatch] root 11 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xenbus] root 13 0.0 0.0 0 0 ? S< Jan04 0:02 \_ [migration/1] root 14 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [ksoftirqd/1] root 15 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [watchdog/1] root 16 0.0 0.0 0 0 ? S< Jan04 0:07 \_ [events/1] root 17 0.0 0.0 0 0 ? S< Jan04 0:02 \_ [migration/2] root 18 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [ksoftirqd/2] root 19 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [watchdog/2] root 20 0.0 0.0 0 0 ? R< Jan04 0:07 \_ [events/2] root 21 0.0 0.0 0 0 ? S< Jan04 0:04 \_ [migration/3] root 22 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [ksoftirqd/3] root 23 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [watchdog/3] root 24 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [events/3] root 25 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kintegrityd/0] root 26 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kintegrityd/1] root 27 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kintegrityd/2] root 28 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kintegrityd/3] root 29 0.0 0.0 0 0 ? S< Jan04 0:01 \_ [kblockd/0] root 30 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kblockd/1] root 31 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kblockd/2] root 32 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kblockd/3] root 33 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kseriod] root 34 0.0 0.0 0 0 ? S Jan04 0:00 \_ [khungtaskd] root 35 0.0 0.0 0 0 ? S Jan04 0:05 \_ [pdflush] root 36 0.0 0.0 0 0 ? S Jan04 0:06 \_ [pdflush] root 37 0.0 0.0 0 0 ? S< Jan04 1:02 \_ [kswapd0] root 38 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [aio/0] root 39 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [aio/1] root 40 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [aio/2] root 41 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [aio/3] root 42 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [jfsIO] root 43 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [jfsCommit] root 44 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [jfsCommit] root 45 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [jfsCommit] root 46 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [jfsCommit] root 47 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [jfsSync] root 48 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfs_mru_cache] root 49 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfslogd/0] root 50 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfslogd/1] root 51 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfslogd/2] root 52 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfslogd/3] root 53 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfsdatad/0] root 54 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfsdatad/1] root 55 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfsdatad/2] root 56 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfsdatad/3] root 57 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfsconvertd/0] root 58 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfsconvertd/1] root 59 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfsconvertd/2] root 60 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [xfsconvertd/3] root 61 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [glock_workqueue] root 62 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [glock_workqueue] root 63 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [glock_workqueue] root 64 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [glock_workqueue] root 65 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [delete_workqueu] root 66 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [delete_workqueu] root 67 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [delete_workqueu] root 68 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [delete_workqueu] root 69 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kslowd] root 70 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kslowd] root 71 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [crypto/0] root 72 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [crypto/1] root 73 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [crypto/2] root 74 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [crypto/3] root 77 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [net_accel/0] root 78 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [net_accel/1] root 79 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [net_accel/2] root 80 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [net_accel/3] root 81 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [sfc_netfront/0] root 82 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [sfc_netfront/1] root 83 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [sfc_netfront/2] root 84 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [sfc_netfront/3] root 310 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [kstriped] root 315 0.0 0.0 0 0 ? S< Jan04 0:00 \_ [ksnapd] root 1452 0.0 0.0 0 0 ? S< Jan04 4:31 \_ [kjournald] root 1 0.0 0.1 19292 948 ? Ss Jan04 0:15 /sbin/init root 1545 0.0 0.1 13164 1064 ? S Jan04 0:00 upstart-udev-bridge --daemon root 1547 0.0 0.1 17196 996 ? S<s Jan04 0:00 udevd --daemon root 1728 0.0 0.2 20284 1468 ? S< Jan04 0:00 \_ udevd --daemon root 1729 0.0 0.1 17192 792 ? S< Jan04 0:00 \_ udevd --daemon root 1881 0.0 0.0 8192 152 ? Ss Jan04 0:00 dd bs=1 if=/proc/kmsg of=/var/run/rsyslog/kmsg syslog 1884 0.0 0.2 185252 1200 ? Sl Jan04 1:00 rsyslogd -c4 103 1894 0.0 0.1 23328 700 ? Ss Jan04 1:08 dbus-daemon --system --fork root 2046 0.0 0.0 136 32 ? Ss Jan04 4:05 runsvdir -P /etc/service log: gems/custom_require.rb:31:in `require'??from /mnt/app/superfeedr-firehoser/current/script/component:52?/opt/ruby-enterprise/lib/ruby/si root 2055 0.0 0.0 112 32 ? Ss Jan04 0:00 \_ runsv chef-client root 2060 0.0 0.0 132 40 ? S Jan04 0:02 | \_ svlogd -tt ./main root 2056 0.0 0.0 112 28 ? Ss Jan04 0:20 \_ runsv superfeedr-firehoser_2 root 2059 0.0 0.0 132 40 ? S Jan04 0:29 | \_ svlogd /var/log/superfeedr-firehoser_2 root 2057 0.0 0.0 112 28 ? Ss Jan04 0:20 \_ runsv superfeedr-firehoser_1 root 2062 0.0 0.0 132 44 ? S Jan04 0:26 \_ svlogd /var/log/superfeedr-firehoser_1 root 2058 0.0 0.0 18708 316 ? Ss Jan04 0:01 cron root 2095 0.0 0.1 49072 764 ? Ss Jan04 0:06 /usr/sbin/sshd root 9832 0.0 0.5 78916 3500 ? Ss 00:37 0:00 \_ sshd: root@pts/0 root 9846 0.0 0.3 17900 2036 pts/0 Ss 00:37 0:00 \_ -bash root 10132 0.0 0.1 15020 1064 pts/0 R+ 09:51 0:00 \_ ps faux root 2180 0.0 0.0 5988 140 tty1 Ss+ Jan04 0:00 /sbin/getty -8 38400 tty1 root 27610 0.0 1.4 47060 8436 ? S Apr04 2:21 python /usr/sbin/denyhosts --daemon --purge --config=/etc/denyhosts.conf --config=/etc/denyhosts.conf root 22640 0.0 0.7 119244 4164 ? Ssl Apr05 0:05 /usr/sbin/console-kit-daemon root 10113 0.0 0.0 3904 316 ? Ss 09:46 0:00 /usr/sbin/collectdmon -P /var/run/collectdmon.pid -- -C /etc/collectd/collectd.conf root 10114 0.0 0.2 201084 1464 ? Sl 09:46 0:00 \_ collectd -C /etc/collectd/collectd.conf -f As you can see there is nothing serious here. If I sum up the RSS line on all this, I get the following : # ps -aeo rss | awk '{sum+=$1} END {print sum}' 30096 Which makes sense. However, I have a pretty big surprise when I do a free: # free total used free shared buffers cached Mem: 591180 343684 247496 0 25432 161256 -/+ buffers/cache: 156996 434184 Swap: 1048568 0 1048568 As you can see 60% of the available memory is already consumed... which leaves me with only 40% to run my own applications if I want to avoid swapping. Quite disapointing! 2 questions arise : Where is all this memory? How to take some of it back for my own apps?

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• How John Got 15x Improvement Without Really Trying

  - by rchrd

  The following article was published on a Sun Microsystems website a number of years ago by John Feo. It is still useful and worth preserving. So I'm republishing it here.  How I Got 15x Improvement Without Really Trying John Feo, Sun Microsystems Taking ten "personal" program codes used in scientific and engineering research, the author was able to get from 2 to 15 times performance improvement easily by applying some simple general optimization techniques. Introduction Scientific research based on computer simulation depends on the simulation for advancement. The research can advance only as fast as the computational codes can execute. The codes' efficiency determines both the rate and quality of results. In the same amount of time, a faster program can generate more results and can carry out a more detailed simulation of physical phenomena than a slower program. Highly optimized programs help science advance quickly and insure that monies supporting scientific research are used as effectively as possible. Scientific computer codes divide into three broad categories: ISV, community, and personal. ISV codes are large, mature production codes developed and sold commercially. The codes improve slowly over time both in methods and capabilities, and they are well tuned for most vendor platforms. Since the codes are mature and complex, there are few opportunities to improve their performance solely through code optimization. Improvements of 10% to 15% are typical. Examples of ISV codes are DYNA3D, Gaussian, and Nastran. Community codes are non-commercial production codes used by a particular research field. Generally, they are developed and distributed by a single academic or research institution with assistance from the community. Most users just run the codes, but some develop new methods and extensions that feed back into the general release. The codes are available on most vendor platforms. Since these codes are younger than ISV codes, there are more opportunities to optimize the source code. Improvements of 50% are not unusual. Examples of community codes are AMBER, CHARM, BLAST, and FASTA. Personal codes are those written by single users or small research groups for their own use. These codes are not distributed, but may be passed from professor-to-student or student-to-student over several years. They form the primordial ocean of applications from which community and ISV codes emerge. Government research grants pay for the development of most personal codes. This paper reports on the nature and performance of this class of codes. Over the last year, I have looked at over two dozen personal codes from more than a dozen research institutions. The codes cover a variety of scientific fields, including astronomy, atmospheric sciences, bioinformatics, biology, chemistry, geology, and physics. The sources range from a few hundred lines to more than ten thousand lines, and are written in Fortran, Fortran 90, C, and C++. For the most part, the codes are modular, documented, and written in a clear, straightforward manner. They do not use complex language features, advanced data structures, programming tricks, or libraries. I had little trouble understanding what the codes did or how data structures were used. Most came with a makefile. Surprisingly, only one of the applications is parallel. All developers have access to parallel machines, so availability is not an issue. Several tried to parallelize their applications, but stopped after encountering difficulties. Lack of education and a perception that parallelism is difficult prevented most from trying. I parallelized several of the codes using OpenMP, and did not judge any of the codes as difficult to parallelize. Even more surprising than the lack of parallelism is the inefficiency of the codes. I was able to get large improvements in performance in a matter of a few days applying simple optimization techniques. Table 1 lists ten representative codes [names and affiliation are omitted to preserve anonymity]. Improvements on one processor range from 2x to 15.5x with a simple average of 4.75x. I did not use sophisticated performance tools or drill deep into the program's execution character as one would do when tuning ISV or community codes. Using only a profiler and source line timers, I identified inefficient sections of code and improved their performance by inspection. The changes were at a high level. I am sure there is another factor of 2 or 3 in each code, and more if the codes are parallelized. The study’s results show that personal scientific codes are running many times slower than they should and that the problem is pervasive. Computational scientists are not sloppy programmers; however, few are trained in the art of computer programming or code optimization. I found that most have a working knowledge of some programming language and standard software engineering practices; but they do not know, or think about, how to make their programs run faster. They simply do not know the standard techniques used to make codes run faster. In fact, they do not even perceive that such techniques exist. The case studies described in this paper show that applying simple, well known techniques can significantly increase the performance of personal codes. It is important that the scientific community and the Government agencies that support scientific research find ways to better educate academic scientific programmers. The inefficiency of their codes is so bad that it is retarding both the quality and progress of scientific research. # cacheperformance redundantoperations loopstructures performanceimprovement 1 x x 15.5 2 x 2.8 3 x x 2.5 4 x 2.1 5 x x 2.0 6 x 5.0 7 x 5.8 8 x 6.3 9 2.2 10 x x 3.3 Table 1 — Area of improvement and performance gains of 10 codes The remainder of the paper is organized as follows: sections 2, 3, and 4 discuss the three most common sources of inefficiencies in the codes studied. These are cache performance, redundant operations, and loop structures. Each section includes several examples. The last section summaries the work and suggests a possible solution to the issues raised. Optimizing cache performance Commodity microprocessor systems use caches to increase memory bandwidth and reduce memory latencies. Typical latencies from processor to L1, L2, local, and remote memory are 3, 10, 50, and 200 cycles, respectively. Moreover, bandwidth falls off dramatically as memory distances increase. Programs that do not use cache effectively run many times slower than programs that do. When optimizing for cache, the biggest performance gains are achieved by accessing data in cache order and reusing data to amortize the overhead of cache misses. Secondary considerations are prefetching, associativity, and replacement; however, the understanding and analysis required to optimize for the latter are probably beyond the capabilities of the non-expert. Much can be gained simply by accessing data in the correct order and maximizing data reuse. 6 out of the 10 codes studied here benefited from such high level optimizations. Array Accesses The most important cache optimization is the most basic: accessing Fortran array elements in column order and C array elements in row order. Four of the ten codes—1, 2, 4, and 10—got it wrong. Compilers will restructure nested loops to optimize cache performance, but may not do so if the loop structure is too complex, or the loop body includes conditionals, complex addressing, or function calls. In code 1, the compiler failed to invert a key loop because of complex addressing do I = 0, 1010, delta_x IM = I - delta_x IP = I + delta_x do J = 5, 995, delta_x JM = J - delta_x JP = J + delta_x T1 = CA1(IP, J) + CA1(I, JP) T2 = CA1(IM, J) + CA1(I, JM) S1 = T1 + T2 - 4 * CA1(I, J) CA(I, J) = CA1(I, J) + D * S1 end do end do In code 2, the culprit is conditionals do I = 1, N do J = 1, N If (IFLAG(I,J) .EQ. 0) then T1 = Value(I, J-1) T2 = Value(I-1, J) T3 = Value(I, J) T4 = Value(I+1, J) T5 = Value(I, J+1) Value(I,J) = 0.25 * (T1 + T2 + T5 + T4) Delta = ABS(T3 - Value(I,J)) If (Delta .GT. MaxDelta) MaxDelta = Delta endif enddo enddo I fixed both programs by inverting the loops by hand. Code 10 has three-dimensional arrays and triply nested loops. The structure of the most computationally intensive loops is too complex to invert automatically or by hand. The only practical solution is to transpose the arrays so that the dimension accessed by the innermost loop is in cache order. The arrays can be transposed at construction or prior to entering a computationally intensive section of code. The former requires all array references to be modified, while the latter is cost effective only if the cost of the transpose is amortized over many accesses. I used the second approach to optimize code 10. Code 5 has four-dimensional arrays and loops are nested four deep. For all of the reasons cited above the compiler is not able to restructure three key loops. Assume C arrays and let the four dimensions of the arrays be i, j, k, and l. In the original code, the index structure of the three loops is L1: for i L2: for i L3: for i for l for l for j for k for j for k for j for k for l So only L3 accesses array elements in cache order. L1 is a very complex loop—much too complex to invert. I brought the loop into cache alignment by transposing the second and fourth dimensions of the arrays. Since the code uses a macro to compute all array indexes, I effected the transpose at construction and changed the macro appropriately. The dimensions of the new arrays are now: i, l, k, and j. L3 is a simple loop and easily inverted. L2 has a loop-carried scalar dependence in k. By promoting the scalar name that carries the dependence to an array, I was able to invert the third and fourth subloops aligning the loop with cache. Code 5 is by far the most difficult of the four codes to optimize for array accesses; but the knowledge required to fix the problems is no more than that required for the other codes. I would judge this code at the limits of, but not beyond, the capabilities of appropriately trained computational scientists. Array Strides When a cache miss occurs, a line (64 bytes) rather than just one word is loaded into the cache. If data is accessed stride 1, than the cost of the miss is amortized over 8 words. Any stride other than one reduces the cost savings. Two of the ten codes studied suffered from non-unit strides. The codes represent two important classes of "strided" codes. Code 1 employs a multi-grid algorithm to reduce time to convergence. The grids are every tenth, fifth, second, and unit element. Since time to convergence is inversely proportional to the distance between elements, coarse grids converge quickly providing good starting values for finer grids. The better starting values further reduce the time to convergence. The downside is that grids of every nth element, n > 1, introduce non-unit strides into the computation. In the original code, much of the savings of the multi-grid algorithm were lost due to this problem. I eliminated the problem by compressing (copying) coarse grids into continuous memory, and rewriting the computation as a function of the compressed grid. On convergence, I copied the final values of the compressed grid back to the original grid. The savings gained from unit stride access of the compressed grid more than paid for the cost of copying. Using compressed grids, the loop from code 1 included in the previous section becomes do j = 1, GZ do i = 1, GZ T1 = CA(i+0, j-1) + CA(i-1, j+0) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) S1 = T1 + T4 - 4 * CA1(i+0, j+0) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 enddo enddo where CA and CA1 are compressed arrays of size GZ. Code 7 traverses a list of objects selecting objects for later processing. The labels of the selected objects are stored in an array. The selection step has unit stride, but the processing steps have irregular stride. A fix is to save the parameters of the selected objects in temporary arrays as they are selected, and pass the temporary arrays to the processing functions. The fix is practical if the same parameters are used in selection as in processing, or if processing comprises a series of distinct steps which use overlapping subsets of the parameters. Both conditions are true for code 7, so I achieved significant improvement by copying parameters to temporary arrays during selection. Data reuse In the previous sections, we optimized for spatial locality. It is also important to optimize for temporal locality. Once read, a datum should be used as much as possible before it is forced from cache. Loop fusion and loop unrolling are two techniques that increase temporal locality. Unfortunately, both techniques increase register pressure—as loop bodies become larger, the number of registers required to hold temporary values grows. Once register spilling occurs, any gains evaporate quickly. For multiprocessors with small register sets or small caches, the sweet spot can be very small. In the ten codes presented here, I found no opportunities for loop fusion and only two opportunities for loop unrolling (codes 1 and 3). In code 1, unrolling the outer and inner loop one iteration increases the number of result values computed by the loop body from 1 to 4, do J = 1, GZ-2, 2 do I = 1, GZ-2, 2 T1 = CA1(i+0, j-1) + CA1(i-1, j+0) T2 = CA1(i+1, j-1) + CA1(i+0, j+0) T3 = CA1(i+0, j+0) + CA1(i-1, j+1) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) T5 = CA1(i+2, j+0) + CA1(i+1, j+1) T6 = CA1(i+1, j+1) + CA1(i+0, j+2) T7 = CA1(i+2, j+1) + CA1(i+1, j+2) S1 = T1 + T4 - 4 * CA1(i+0, j+0) S2 = T2 + T5 - 4 * CA1(i+1, j+0) S3 = T3 + T6 - 4 * CA1(i+0, j+1) S4 = T4 + T7 - 4 * CA1(i+1, j+1) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 CA(i+1, j+0) = CA1(i+1, j+0) + DD * S2 CA(i+0, j+1) = CA1(i+0, j+1) + DD * S3 CA(i+1, j+1) = CA1(i+1, j+1) + DD * S4 enddo enddo The loop body executes 12 reads, whereas as the rolled loop shown in the previous section executes 20 reads to compute the same four values. In code 3, two loops are unrolled 8 times and one loop is unrolled 4 times. Here is the before for (k = 0; k < NK[u]; k++) { sum = 0.0; for (y = 0; y < NY; y++) { sum += W[y][u][k] * delta[y]; } backprop[i++]=sum; } and after code for (k = 0; k < KK - 8; k+=8) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (y = 0; y < NY; y++) { sum0 += W[y][0][k+0] * delta[y]; sum1 += W[y][0][k+1] * delta[y]; sum2 += W[y][0][k+2] * delta[y]; sum3 += W[y][0][k+3] * delta[y]; sum4 += W[y][0][k+4] * delta[y]; sum5 += W[y][0][k+5] * delta[y]; sum6 += W[y][0][k+6] * delta[y]; sum7 += W[y][0][k+7] * delta[y]; } backprop[k+0] = sum0; backprop[k+1] = sum1; backprop[k+2] = sum2; backprop[k+3] = sum3; backprop[k+4] = sum4; backprop[k+5] = sum5; backprop[k+6] = sum6; backprop[k+7] = sum7; } for one of the loops unrolled 8 times. Optimizing for temporal locality is the most difficult optimization considered in this paper. The concepts are not difficult, but the sweet spot is small. Identifying where the program can benefit from loop unrolling or loop fusion is not trivial. Moreover, it takes some effort to get it right. Still, educating scientific programmers about temporal locality and teaching them how to optimize for it will pay dividends. Reducing instruction count Execution time is a function of instruction count. Reduce the count and you usually reduce the time. The best solution is to use a more efficient algorithm; that is, an algorithm whose order of complexity is smaller, that converges quicker, or is more accurate. Optimizing source code without changing the algorithm yields smaller, but still significant, gains. This paper considers only the latter because the intent is to study how much better codes can run if written by programmers schooled in basic code optimization techniques. The ten codes studied benefited from three types of "instruction reducing" optimizations. The two most prevalent were hoisting invariant memory and data operations out of inner loops. The third was eliminating unnecessary data copying. The nature of these inefficiencies is language dependent. Memory operations The semantics of C make it difficult for the compiler to determine all the invariant memory operations in a loop. The problem is particularly acute for loops in functions since the compiler may not know the values of the function's parameters at every call site when compiling the function. Most compilers support pragmas to help resolve ambiguities; however, these pragmas are not comprehensive and there is no standard syntax. To guarantee that invariant memory operations are not executed repetitively, the user has little choice but to hoist the operations by hand. The problem is not as severe in Fortran programs because in the absence of equivalence statements, it is a violation of the language's semantics for two names to share memory. Codes 3 and 5 are C programs. In both cases, the compiler did not hoist all invariant memory operations from inner loops. Consider the following loop from code 3 for (y = 0; y < NY; y++) { i = 0; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += delta[y] * I1[i++]; } } } Since dW[y][u] can point to the same memory space as delta for one or more values of y and u, assignment to dW[y][u][k] may change the value of delta[y]. In reality, dW and delta do not overlap in memory, so I rewrote the loop as for (y = 0; y < NY; y++) { i = 0; Dy = delta[y]; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += Dy * I1[i++]; } } } Failure to hoist invariant memory operations may be due to complex address calculations. If the compiler can not determine that the address calculation is invariant, then it can hoist neither the calculation nor the associated memory operations. As noted above, code 5 uses a macro to address four-dimensional arrays #define MAT4D(a,q,i,j,k) (double *)((a)->data + (q)*(a)->strides[0] + (i)*(a)->strides[3] + (j)*(a)->strides[2] + (k)*(a)->strides[1]) The macro is too complex for the compiler to understand and so, it does not identify any subexpressions as loop invariant. The simplest way to eliminate the address calculation from the innermost loop (over i) is to define a0 = MAT4D(a,q,0,j,k) before the loop and then replace all instances of *MAT4D(a,q,i,j,k) in the loop with a0[i] A similar problem appears in code 6, a Fortran program. The key loop in this program is do n1 = 1, nh nx1 = (n1 - 1) / nz + 1 nz1 = n1 - nz * (nx1 - 1) do n2 = 1, nh nx2 = (n2 - 1) / nz + 1 nz2 = n2 - nz * (nx2 - 1) ndx = nx2 - nx1 ndy = nz2 - nz1 gxx = grn(1,ndx,ndy) gyy = grn(2,ndx,ndy) gxy = grn(3,ndx,ndy) balance(n1,1) = balance(n1,1) + (force(n2,1) * gxx + force(n2,2) * gxy) * h1 balance(n1,2) = balance(n1,2) + (force(n2,1) * gxy + force(n2,2) * gyy)*h1 end do end do The programmer has written this loop well—there are no loop invariant operations with respect to n1 and n2. However, the loop resides within an iterative loop over time and the index calculations are independent with respect to time. Trading space for time, I precomputed the index values prior to the entering the time loop and stored the values in two arrays. I then replaced the index calculations with reads of the arrays. Data operations Ways to reduce data operations can appear in many forms. Implementing a more efficient algorithm produces the biggest gains. The closest I came to an algorithm change was in code 4. This code computes the inner product of K-vectors A(i) and B(j), 0 = i < N, 0 = j < M, for most values of i and j. Since the program computes most of the NM possible inner products, it is more efficient to compute all the inner products in one triply-nested loop rather than one at a time when needed. The savings accrue from reading A(i) once for all B(j) vectors and from loop unrolling. for (i = 0; i < N; i+=8) { for (j = 0; j < M; j++) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (k = 0; k < K; k++) { sum0 += A[i+0][k] * B[j][k]; sum1 += A[i+1][k] * B[j][k]; sum2 += A[i+2][k] * B[j][k]; sum3 += A[i+3][k] * B[j][k]; sum4 += A[i+4][k] * B[j][k]; sum5 += A[i+5][k] * B[j][k]; sum6 += A[i+6][k] * B[j][k]; sum7 += A[i+7][k] * B[j][k]; } C[i+0][j] = sum0; C[i+1][j] = sum1; C[i+2][j] = sum2; C[i+3][j] = sum3; C[i+4][j] = sum4; C[i+5][j] = sum5; C[i+6][j] = sum6; C[i+7][j] = sum7; }} This change requires knowledge of a typical run; i.e., that most inner products are computed. The reasons for the change, however, derive from basic optimization concepts. It is the type of change easily made at development time by a knowledgeable programmer. In code 5, we have the data version of the index optimization in code 6. Here a very expensive computation is a function of the loop indices and so cannot be hoisted out of the loop; however, the computation is invariant with respect to an outer iterative loop over time. We can compute its value for each iteration of the computation loop prior to entering the time loop and save the values in an array. The increase in memory required to store the values is small in comparison to the large savings in time. The main loop in Code 8 is doubly nested. The inner loop includes a series of guarded computations; some are a function of the inner loop index but not the outer loop index while others are a function of the outer loop index but not the inner loop index for (j = 0; j < N; j++) { for (i = 0; i < M; i++) { r = i * hrmax; R = A[j]; temp = (PRM[3] == 0.0) ? 1.0 : pow(r, PRM[3]); high = temp * kcoeff * B[j] * PRM[2] * PRM[4]; low = high * PRM[6] * PRM[6] / (1.0 + pow(PRM[4] * PRM[6], 2.0)); kap = (R > PRM[6]) ? high * R * R / (1.0 + pow(PRM[4]*r, 2.0) : low * pow(R/PRM[6], PRM[5]); < rest of loop omitted > }} Note that the value of temp is invariant to j. Thus, we can hoist the computation for temp out of the loop and save its values in an array. for (i = 0; i < M; i++) { r = i * hrmax; TEMP[i] = pow(r, PRM[3]); } [N.B. – the case for PRM[3] = 0 is omitted and will be reintroduced later.] We now hoist out of the inner loop the computations invariant to i. Since the conditional guarding the value of kap is invariant to i, it behooves us to hoist the computation out of the inner loop, thereby executing the guard once rather than M times. The final version of the code is for (j = 0; j < N; j++) { R = rig[j] / 1000.; tmp1 = kcoeff * par[2] * beta[j] * par[4]; tmp2 = 1.0 + (par[4] * par[4] * par[6] * par[6]); tmp3 = 1.0 + (par[4] * par[4] * R * R); tmp4 = par[6] * par[6] / tmp2; tmp5 = R * R / tmp3; tmp6 = pow(R / par[6], par[5]); if ((par[3] == 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp5; } else if ((par[3] == 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp4 * tmp6; } else if ((par[3] != 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp5; } else if ((par[3] != 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp4 * tmp6; } for (i = 0; i < M; i++) { kap = KAP[i]; r = i * hrmax; < rest of loop omitted > } } Maybe not the prettiest piece of code, but certainly much more efficient than the original loop, Copy operations Several programs unnecessarily copy data from one data structure to another. This problem occurs in both Fortran and C programs, although it manifests itself differently in the two languages. Code 1 declares two arrays—one for old values and one for new values. At the end of each iteration, the array of new values is copied to the array of old values to reset the data structures for the next iteration. This problem occurs in Fortran programs not included in this study and in both Fortran 77 and Fortran 90 code. Introducing pointers to the arrays and swapping pointer values is an obvious way to eliminate the copying; but pointers is not a feature that many Fortran programmers know well or are comfortable using. An easy solution not involving pointers is to extend the dimension of the value array by 1 and use the last dimension to differentiate between arrays at different times. For example, if the data space is N x N, declare the array (N, N, 2). Then store the problem’s initial values in (_, _, 2) and define the scalar names new = 2 and old = 1. At the start of each iteration, swap old and new to reset the arrays. The old–new copy problem did not appear in any C program. In programs that had new and old values, the code swapped pointers to reset data structures. Where unnecessary coping did occur is in structure assignment and parameter passing. Structures in C are handled much like scalars. Assignment causes the data space of the right-hand name to be copied to the data space of the left-hand name. Similarly, when a structure is passed to a function, the data space of the actual parameter is copied to the data space of the formal parameter. If the structure is large and the assignment or function call is in an inner loop, then copying costs can grow quite large. While none of the ten programs considered here manifested this problem, it did occur in programs not included in the study. A simple fix is always to refer to structures via pointers. Optimizing loop structures Since scientific programs spend almost all their time in loops, efficient loops are the key to good performance. Conditionals, function calls, little instruction level parallelism, and large numbers of temporary values make it difficult for the compiler to generate tightly packed, highly efficient code. Conditionals and function calls introduce jumps that disrupt code flow. Users should eliminate or isolate conditionls to their own loops as much as possible. Often logical expressions can be substituted for if-then-else statements. For example, code 2 includes the following snippet MaxDelta = 0.0 do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) if (Delta > MaxDelta) MaxDelta = Delta enddo enddo if (MaxDelta .gt. 0.001) goto 200 Since the only use of MaxDelta is to control the jump to 200 and all that matters is whether or not it is greater than 0.001, I made MaxDelta a boolean and rewrote the snippet as MaxDelta = .false. do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) MaxDelta = MaxDelta .or. (Delta .gt. 0.001) enddo enddo if (MaxDelta) goto 200 thereby, eliminating the conditional expression from the inner loop. A microprocessor can execute many instructions per instruction cycle. Typically, it can execute one or more memory, floating point, integer, and jump operations. To be executed simultaneously, the operations must be independent. Thick loops tend to have more instruction level parallelism than thin loops. Moreover, they reduce memory traffice by maximizing data reuse. Loop unrolling and loop fusion are two techniques to increase the size of loop bodies. Several of the codes studied benefitted from loop unrolling, but none benefitted from loop fusion. This observation is not too surpising since it is the general tendency of programmers to write thick loops. As loops become thicker, the number of temporary values grows, increasing register pressure. If registers spill, then memory traffic increases and code flow is disrupted. A thick loop with many temporary values may execute slower than an equivalent series of thin loops. The biggest gain will be achieved if the thick loop can be split into a series of independent loops eliminating the need to write and read temporary arrays. I found such an occasion in code 10 where I split the loop do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do into two disjoint loops do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) end do end do do i = 1, n do j = 1, m C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do Conclusions Over the course of the last year, I have had the opportunity to work with over two dozen academic scientific programmers at leading research universities. Their research interests span a broad range of scientific fields. Except for two programs that relied almost exclusively on library routines (matrix multiply and fast Fourier transform), I was able to improve significantly the single processor performance of all codes. Improvements range from 2x to 15.5x with a simple average of 4.75x. Changes to the source code were at a very high level. I did not use sophisticated techniques or programming tools to discover inefficiencies or effect the changes. Only one code was parallel despite the availability of parallel systems to all developers. Clearly, we have a problem—personal scientific research codes are highly inefficient and not running parallel. The developers are unaware of simple optimization techniques to make programs run faster. They lack education in the art of code optimization and parallel programming. I do not believe we can fix the problem by publishing additional books or training manuals. To date, the developers in questions have not studied the books or manual available, and are unlikely to do so in the future. Short courses are a possible solution, but I believe they are too concentrated to be much use. The general concepts can be taught in a three or four day course, but that is not enough time for students to practice what they learn and acquire the experience to apply and extend the concepts to their codes. Practice is the key to becoming proficient at optimization. I recommend that graduate students be required to take a semester length course in optimization and parallel programming. We would never give someone access to state-of-the-art scientific equipment costing hundreds of thousands of dollars without first requiring them to demonstrate that they know how to use the equipment. Yet the criterion for time on state-of-the-art supercomputers is at most an interesting project. Requestors are never asked to demonstrate that they know how to use the system, or can use the system effectively. A semester course would teach them the required skills. Government agencies that fund academic scientific research pay for most of the computer systems supporting scientific research as well as the development of most personal scientific codes. These agencies should require graduate schools to offer a course in optimization and parallel programming as a requirement for funding. About the Author John Feo received his Ph.D. in Computer Science from The University of Texas at Austin in 1986. After graduate school, Dr. Feo worked at Lawrence Livermore National Laboratory where he was the Group Leader of the Computer Research Group and principal investigator of the Sisal Language Project. In 1997, Dr. Feo joined Tera Computer Company where he was project manager for the MTA, and oversaw the programming and evaluation of the MTA at the San Diego Supercomputer Center. In 2000, Dr. Feo joined Sun Microsystems as an HPC application specialist. He works with university research groups to optimize and parallelize scientific codes. Dr. Feo has published over two dozen research articles in the areas of parallel parallel programming, parallel programming languages, and application performance.

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• Working with Analytic Workflow Manager (AWM) - Part 8 Cube Metadata Analysis

  - by Mohan Ramanuja

  CUBE SIZEselect dbal.owner||'.'||substr(dbal.table_name,4) awname, sum(dbas.bytes)/1024/1024 as mb, dbas.tablespace_name from dba_lobs dbal, dba_segments dbas where dbal.column_name = 'AWLOB' and dbal.segment_name = dbas.segment_name group by dbal.owner, dbal.table_name, dbas.tablespace_name order by dbal.owner, dbal.table_name SESSION RESOURCES select vses.username||':'||vsst.sid username, vstt.name, max(vsst.value) valuefrom v$sesstat vsst, v$statname vstt, v$session vseswhere vstt.statistic# = vsst.statistic# and vsst.sid = vses.sid andVSES.USERNAME LIKE ('ATTRIBDW_OWN') ANDvstt.name in ('session pga memory', 'session pga memory max', 'session uga memory','session uga memory max', 'session cursor cache count', 'session cursor cache hits', 'session stored procedure space', 'opened cursors current', 'opened cursors cumulative') andvses.username is not null group by vsst.sid, vses.username, vstt.name order by vsst.sid, vses.username, vstt.name OLAP PGA USE select 'OLAP Pages Occupying: '|| round((((select sum(nvl(pool_size,1)) from v$aw_calc)) / (select value from v$pgastat where name = 'total PGA inuse')),2)*100||'%' info from dual union select 'Total PGA Inuse Size: '||value/1024||' KB' info from v$pgastat where name = 'total PGA inuse' union select 'Total OLAP Page Size: '|| round(sum(nvl(pool_size,1))/1024,0)||' KB' info from v$aw_calc order by info desc OLAP PGA USAGE PER USER select vs.username, vs.sid, round(pga_used_mem/1024/1024,2)||' MB' pga_used, round(pga_max_mem/1024/1024,2)||' MB' pga_max, round(pool_size/1024/1024,2)||' MB' olap_pp, round(100*(pool_hits-pool_misses)/pool_hits,2) || '%' olap_ratio from v$process vp, v$session vs, v$aw_calc va where session_id=vs.sid and addr = paddr CUBE LOADING SCRIPT REM The 'set define off' statement is needed only if running this script through SQLPlus.REM If you are using another tool to run this script, the line below may be commented out.set define offBEGIN  DBMS_CUBE.BUILD(    'VALIDATE  ATTRIBDW_OWN.CURRENCY USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNT USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.DATEDIM USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.CUSIP USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNTRETURN',    'CCCCC', -- refresh methodfalse, -- refresh after errors    0, -- parallelismtrue, -- atomic refreshtrue, -- automatic orderfalse); -- add dimensionsEND;/BEGIN  DBMS_CUBE.BUILD(    '  ATTRIBDW_OWN.CURRENCY USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNT USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.DATEDIM USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.CUSIP USING  (    LOAD NO SYNCH,    COMPILE SORT  ),  ATTRIBDW_OWN.ACCOUNTRETURN',    'CCCCC', -- refresh methodfalse, -- refresh after errors    0, -- parallelismtrue, -- atomic refreshtrue, -- automatic orderfalse); -- add dimensionsEND;/ VISUALIZATION OBJECT - AW$ATTRIBDW_OWN  CREATE TABLE "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"        (            "PS#"    NUMBER(10,0),            "GEN#"   NUMBER(10,0),            "EXTNUM" NUMBER(8,0),            "AWLOB" BLOB,            "OBJNAME"  VARCHAR2(256 BYTE),            "PARTNAME" VARCHAR2(256 BYTE)        )        PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE        (            BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "AWLOB"        )        STORE AS SECUREFILE        (            TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        )        PARTITION BY RANGE        (            "GEN#"        )        SUBPARTITION BY HASH        (            "PS#",            "EXTNUM"        )        SUBPARTITIONS 8        (            PARTITION "PTN1" VALUES LESS THAN (1) PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOB ("AWLOB") STORE AS SECUREFILE ( TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE READS LOGGING NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)) ( SUBPARTITION "SYS_SUBP661" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP662" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP663" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP664" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP665" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION            "SYS_SUBP666" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP667" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP668" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" ) ,            PARTITION "PTNN" VALUES LESS THAN (MAXVALUE) PCTFREE 10 PCTUSED 40 INITRANS 4 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOB ("AWLOB") STORE AS SECUREFILE ( TABLESPACE "ATTRIBDW_DATA" DISABLE STORAGE IN ROW CHUNK 8192 RETENTION MIN 1 CACHE NOCOMPRESS KEEP_DUPLICATES STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)) ( SUBPARTITION "SYS_SUBP669" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP670" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP671" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP672" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP673" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION            "SYS_SUBP674" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP675" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_SUBP676" LOB ("AWLOB") STORE AS ( TABLESPACE "ATTRIBDW_DATA" ) TABLESPACE "ATTRIBDW_DATA" )        ) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."ATTRIBDW_OWN_I$" ON "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"    (        "PS#", "GEN#", "EXTNUM"    )    PCTFREE 10 INITRANS 4 MAXTRANS 255 COMPUTE STATISTICS STORAGE    (        INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT    )    TABLESPACE "ATTRIBDW_DATA" ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000406980C00004$$" ON "ATTRIBDW_OWN"."AW$ATTRIBDW_OWN"    (        PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" LOCAL (PARTITION "SYS_IL_P711" PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) ( SUBPARTITION "SYS_IL_SUBP695" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP696" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP697" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP698" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP699" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP700" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP701" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP702" TABLESPACE "ATTRIBDW_DATA" ) , PARTITION "SYS_IL_P712" PCTFREE 10 INITRANS 1 MAXTRANS 255 STORAGE( BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) ( SUBPARTITION "SYS_IL_SUBP703" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP704" TABLESPACE        "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP705" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP706" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP707" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP708" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP709" TABLESPACE "ATTRIBDW_DATA" , SUBPARTITION "SYS_IL_SUBP710" TABLESPACE "ATTRIBDW_DATA" ) ) PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE BUILD LOG  CREATE TABLE "ATTRIBDW_OWN"."CUBE_BUILD_LOG"        (            "BUILD_ID"          NUMBER,            "SLAVE_NUMBER"      NUMBER,            "STATUS"            VARCHAR2(10 BYTE),            "COMMAND"           VARCHAR2(25 BYTE),            "BUILD_OBJECT"      VARCHAR2(30 BYTE),            "BUILD_OBJECT_TYPE" VARCHAR2(10 BYTE),            "OUTPUT" CLOB,            "AW"            VARCHAR2(30 BYTE),            "OWNER"         VARCHAR2(30 BYTE),            "PARTITION"     VARCHAR2(50 BYTE),            "SCHEDULER_JOB" VARCHAR2(100 BYTE),            "TIME" TIMESTAMP (6)WITH TIME ZONE,        "BUILD_SCRIPT" CLOB,        "BUILD_TYPE"            VARCHAR2(22 BYTE),        "COMMAND_DEPTH"         NUMBER(2,0),        "BUILD_SUB_OBJECT"      VARCHAR2(30 BYTE),        "REFRESH_METHOD"        VARCHAR2(1 BYTE),        "SEQ_NUMBER"            NUMBER,        "COMMAND_NUMBER"        NUMBER,        "IN_BRANCH"             NUMBER(1,0),        "COMMAND_STATUS_NUMBER" NUMBER,        "BUILD_NAME"            VARCHAR2(100 BYTE)        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "OUTPUT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        )        LOB        (            "BUILD_SCRIPT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407294C00013$$" ON "ATTRIBDW_OWN"."CUBE_BUILD_LOG"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ;CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407294C00007$$" ON "ATTRIBDW_OWN"."CUBE_BUILD_LOG" ( PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE DIMENSION COMPILE  CREATE TABLE "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"        (            "ID"               NUMBER,            "SEQ_NUMBER"       NUMBER,            "ERROR#"           NUMBER(8,0) NOT NULL ENABLE,            "ERROR_MESSAGE"    VARCHAR2(2000 BYTE),            "DIMENSION"        VARCHAR2(100 BYTE),            "DIMENSION_MEMBER" VARCHAR2(100 BYTE),            "MEMBER_ANCESTOR"  VARCHAR2(100 BYTE),            "HIERARCHY1"       VARCHAR2(100 BYTE),            "HIERARCHY2"       VARCHAR2(100 BYTE),            "ERROR_CONTEXT" CLOB        )        SEGMENT CREATION DEFERRED PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING TABLESPACE "ATTRIBDW_DATA" LOB        (            "ERROR_CONTEXT"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR#"IS    'Error number being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR_MESSAGE"IS    'Error text being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."DIMENSION"IS    'Name of dimension being compiled';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."DIMENSION_MEMBER"IS    'Problem dimension member';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."MEMBER_ANCESTOR"IS    'Problem dimension member''s parent';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."HIERARCHY1"IS    'First hierarchy involved in error';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."HIERARCHY2"IS    'Second hierarchy involved in error';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"."ERROR_CONTEXT"IS    'Extra information for error';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"IS    'Cube dimension compile log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407307C00010$$" ON "ATTRIBDW_OWN"."CUBE_DIMENSION_COMPILE"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE( INITIAL 1048576 NEXT 1048576 MAXEXTENTS 2147483645) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE OPERATING LOG  CREATE TABLE "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"        (            "INST_ID"    NUMBER NOT NULL ENABLE,            "SID"        NUMBER NOT NULL ENABLE,            "SERIAL#"    NUMBER NOT NULL ENABLE,            "USER#"      NUMBER NOT NULL ENABLE,            "SQL_ID"     VARCHAR2(13 BYTE),            "JOB"        NUMBER,            "ID"         NUMBER,            "PARENT_ID"  NUMBER,            "SEQ_NUMBER" NUMBER,            "TIME" TIMESTAMP (6)WITH TIME ZONE NOT NULL ENABLE,        "LOG_LEVEL"    NUMBER(4,0) NOT NULL ENABLE,        "DEPTH"        NUMBER(4,0),        "OPERATION"    VARCHAR2(15 BYTE) NOT NULL ENABLE,        "SUBOPERATION" VARCHAR2(20 BYTE),        "STATUS"       VARCHAR2(10 BYTE) NOT NULL ENABLE,        "NAME"         VARCHAR2(20 BYTE) NOT NULL ENABLE,        "VALUE"        VARCHAR2(4000 BYTE),        "DETAILS" CLOB        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "DETAILS"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."INST_ID"IS    'Instance ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SID"IS    'Session ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SERIAL#"IS    'Session serial#';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."USER#"IS    'User ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SQL_ID"IS    'Executing SQL statement ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."JOB"IS    'Identifier of job';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."PARENT_ID"IS    'Parent operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."TIME"IS    'Time of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."LOG_LEVEL"IS    'Verbosity level of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."DEPTH"IS    'Nesting depth of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."OPERATION"IS    'Current operation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."SUBOPERATION"IS    'Current suboperation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."STATUS"IS    'Status of current operation';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."NAME"IS    'Name of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."VALUE"IS    'Value of record';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"."DETAILS"IS    'Extra information for record';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"IS    'Cube operations log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407301C00018$$" ON "ATTRIBDW_OWN"."CUBE_OPERATIONS_LOG"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ; CUBE REJECTED RECORDS CREATE TABLE "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"        (            "ID"            NUMBER,            "SEQ_NUMBER"    NUMBER,            "ERROR#"        NUMBER(8,0) NOT NULL ENABLE,            "ERROR_MESSAGE" VARCHAR2(2000 BYTE),            "RECORD#"       NUMBER(38,0),            "SOURCE_ROW" ROWID,            "REJECTED_RECORD" CLOB        )        SEGMENT CREATION IMMEDIATE PCTFREE 10 PCTUSED 40 INITRANS 1 MAXTRANS 255 NOCOMPRESS LOGGING STORAGE        (            INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT        )        TABLESPACE "ATTRIBDW_DATA" LOB        (            "REJECTED_RECORD"        )        STORE AS BASICFILE        (            TABLESPACE "ATTRIBDW_DATA" ENABLE STORAGE IN ROW CHUNK 8192 RETENTION NOCACHE LOGGING STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT)        ) ;COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ID"IS    'Current operation ID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."SEQ_NUMBER"IS    'Cube build log sequence number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ERROR#"IS    'Error number being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."ERROR_MESSAGE"IS    'Error text being reported';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."RECORD#"IS    'Rejected record number';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."SOURCE_ROW"IS    'Rejected record''s ROWID';    COMMENT ON COLUMN "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"."REJECTED_RECORD"IS    'Rejected record copy';    COMMENT ON TABLE "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"IS    'Cube rejected records log';CREATE UNIQUE INDEX "ATTRIBDW_OWN"."SYS_IL0000407304C00007$$" ON "ATTRIBDW_OWN"."CUBE_REJECTED_RECORDS"    (        PCTFREE 10 INITRANS 2 MAXTRANS 255 STORAGE(INITIAL 1048576 NEXT 1048576 MINEXTENTS 1 MAXEXTENTS 2147483645 PCTINCREASE 0 FREELISTS 1 FREELIST GROUPS 1 BUFFER_POOL DEFAULT FLASH_CACHE DEFAULT CELL_FLASH_CACHE DEFAULT) TABLESPACE "ATTRIBDW_DATA" PARALLEL (DEGREE 0 INSTANCES 0) ;

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