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  • Parallel.Foreach loop creating multiple db connections throws connection errors?

    - by shawn.mek
    Login failed. The login is from an untrusted domain and cannot be used with Windows authentication I wanted to get my code running in parallel, so I changed my foreach loop to a parallel foreach loop. It seemed simple enough. Each loop connects to the database, looks up some stuff, performs some logic, adds some stuff, closes the connection. But I get the above error? I'm using my local sql server and entity framework (each loop uses it's own context). Is there some problem with connecting multiple times using the same local login or something? How did I get around this? I have (before trying to covert to a parallel.foreach loop) split my list of objects that I am foreach looping through into four groups (separate csv files) and run four concurrent instances of my program (which ran faster overall than just one, thus the idea for parallel). So it seems connecting to the db shouldn't be a problem? Any ideas? EDIT: Here's before var gtgGenerator = new CustomGtgGenerator(); var connectionString = ConfigurationManager.ConnectionStrings["BioEntities"].ConnectionString; var allAccessionsFromObs = _GetAccessionListFromDataFiles(collectionId); ForEach(cloneIdAndAccessions in allAccessionsFromObs) DoWork(gtgGenerator, taxonId, organismId, cloneIdAndAccessions, connectionString)); after var gtgGenerator = new CustomGtgGenerator(); var connectionString = ConfigurationManager.ConnectionStrings["BioEntities"].ConnectionString; var allAccessionsFromObs = _GetAccessionListFromDataFiles(collectionId); Parallel.ForEach(allAccessionsFromObs, cloneIdAndAccessions => DoWork(gtgGenerator, taxonId, organismId, cloneIdAndAccessions, connectionString)); Inside the DoWork I use the BioEntities using (var bioEntities = new BioEntities(connectionString)) {...}

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  • "Parallel Programming Talk" show

    Over at the Intel Software Network Aaron Tersteeg runs a "Parallel Programming Talk" audio show on which I was invited as a guest (for the 55th episode) to talk about Microsoft's parallelism offerings in Visual Studio 2010. The call started at 7:45AM, so if my voice sounds croaky to you, now you know why ;)Check out the 20-minute chat (and related hyperlinks) on Aaron's blog. Comments about this post welcome at the original blog.

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  • ODI 12c - Parallel Table Load

    - by David Allan
    In this post we will look at the ODI 12c capability of parallel table load from the aspect of the mapping developer and the knowledge module developer - two quite different viewpoints. This is about parallel table loading which isn't to be confused with loading multiple targets per se. It supports the ability for ODI mappings to be executed concurrently especially if there is an overlap of the datastores that they access, so any temporary resources created may be uniquely constructed by ODI. Temporary objects can be anything basically - common examples are staging tables, indexes, views, directories - anything in the ETL to help the data integration flow do its job. In ODI 11g users found a few workarounds (such as changing the technology prefixes - see here) to build unique temporary names but it was more of a challenge in error cases. ODI 12c mappings by default operate exactly as they did in ODI 11g with respect to these temporary names (this is also true for upgraded interfaces and scenarios) but can be configured to support the uniqueness capabilities. We will look at this feature from two aspects; that of a mapping developer and that of a developer (of procedures or KMs). 1. Firstly as a Mapping Developer..... 1.1 Control when uniqueness is enabled A new property is available to set unique name generation on/off. When unique names have been enabled for a mapping, all temporary names used by the collection and integration objects will be generated using unique names. This property is presented as a check-box in the Property Inspector for a deployment specification. 1.2 Handle cleanup after successful execution Provided that all temporary objects that are created have a corresponding drop statement then all of the temporary objects should be removed during a successful execution. This should be the case with the KMs developed by Oracle. 1.3 Handle cleanup after unsuccessful execution If an execution failed in ODI 11g then temporary tables would have been left around and cleaned up in the subsequent run. In ODI 12c, KM tasks can now have a cleanup-type task which is executed even after a failure in the main tasks. These cleanup tasks will be executed even on failure if the property 'Remove Temporary Objects on Error' is set. If the agent was to crash and not be able to execute this task, then there is an ODI tool (OdiRemoveTemporaryObjects here) you can invoke to cleanup the tables - it supports date ranges and the like. That's all there is to it from the aspect of the mapping developer it's much, much simpler and straightforward. You can now execute the same mapping concurrently or execute many mappings using the same resource concurrently without worrying about conflict.  2. Secondly as a Procedure or KM Developer..... In the ODI Operator the executed code shows the actual name that is generated - you can also see the runtime code prior to execution (introduced in 11.1.1.7), for example below in the code type I selected 'Pre-executed Code' this lets you see the code about to be processed and you can also see the executed code (which is the default view). References to the collection (C$) and integration (I$) names will be automatically made unique by using the odiRef APIs - these objects will have unique names whenever concurrency has been enabled for a particular mapping deployment specification. It's also possible to use name uniqueness functions in procedures and your own KMs. 2.1 New uniqueness tags  You can also make your own temporary objects have unique names by explicitly including either %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG in the name passed to calls to the odiRef APIs. Such names would always include the unique tag regardless of the concurrency setting. To illustrate, let's look at the getObjectName() method. At <% expansion time, this API will append %UNIQUE_STEP_TAG to the object name for collection and integration tables. The name parameter passed to this API may contain  %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG. This API always generates to the <? version of getObjectName() At execution time this API will replace the unique tag macros with a string that is unique to the current execution scope. The returned name will conform to the name-length restriction for the target technology, and its pattern for the unique tag. Any necessary truncation will be performed against the initial name for the object and any other fixed text that may have been specified. Examples are:- <?=odiRef.getObjectName("L", "%COL_PRFEMP%UNIQUE_STEP_TAG", "D")?> SCOTT.C$_EABH7QI1BR1EQI3M76PG9SIMBQQ <?=odiRef.getObjectName("L", "EMP%UNIQUE_STEP_TAG_AE", "D")?> SCOTT.EMPAO96Q2JEKO0FTHQP77TMSAIOSR_ Methods which have this kind of support include getFrom, getTableName, getTable, getObjectShortName and getTemporaryIndex. There are APIs for retrieving this tag info also, the getInfo API has been extended with the following properties (the UNIQUE* properties can also be used in ODI procedures); UNIQUE_STEP_TAG - Returns the unique value for the current step scope, e.g. 5rvmd8hOIy7OU2o1FhsF61 Note that this will be a different value for each loop-iteration when the step is in a loop. UNIQUE_SESSION_TAG - Returns the unique value for the current session scope, e.g. 6N38vXLrgjwUwT5MseHHY9 IS_CONCURRENT - Returns info about the current mapping, will return 0 or 1 (only in % phase) GUID_SRC_SET - Returns the UUID for the current source set/execution unit (only in % phase) The getPop API has been extended with the IS_CONCURRENT property which returns info about an mapping, will return 0 or 1.  2.2 Additional APIs Some new APIs are provided including getFormattedName which will allow KM developers to construct a name from fixed-text or ODI symbols that can be optionally truncate to a max length and use a specific encoding for the unique tag. It has syntax getFormattedName(String pName[, String pTechnologyCode]) This API is available at both the % and the ? phase.  The format string can contain the ODI prefixes that are available for getObjectName(), e.g. %INT_PRF, %COL_PRF, %ERR_PRF, %IDX_PRF alongwith %UNIQUE_STEP_TAG or %UNIQUE_SESSION_TAG. The latter tags will be expanded into a unique string according to the specified technology. Calls to this API within the same execution context are guaranteed to return the same unique name provided that the same parameters are passed to the call. e.g. <%=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG_AE", "ORACLE")%> <?=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG_AE", "ORACLE")?> C$_MY_TAB7wDiBe80vBog1auacS1xB_AE <?=odiRef.getFormattedName("%COL_PRFMY_TABLE%UNIQUE_STEP_TAG.log", "FILE")?> C2_MY_TAB7wDiBe80vBog1auacS1xB.log 2.3 Name length generation  As part of name generation, the length of the generated name will be compared with the maximum length for the target technology and truncation may need to be applied. When a unique tag is included in the generated string it is important that uniqueness is not compromised by truncation of the unique tag. When a unique tag is NOT part of the generated name, the name will be truncated by removing characters from the end - this is the existing 11g algorithm. When a unique tag is included, the algorithm will first truncate the <postfix> and if necessary  the <prefix>. It is recommended that users will ensure there is sufficient uniqueness in the <prefix> section to ensure uniqueness of the final resultant name. SUMMARY To summarize, ODI 12c make it much simpler to utilize mappings in concurrent cases and provides APIs for helping developing any procedures or custom knowledge modules in such a way they can be used in highly concurrent, parallel scenarios. 

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  • Using the Parallel class to make multithreading easy

    - by thycotic
    Kevin has posted about the Parallel class and how to use it to easily do multiple operations at once without radically changing the structure of your code.  Very neat stuff.   Jonathan Cogley is the CEO of Thycotic Software, an agile software services and product development company based in Washington DC.  Secret Server is our flagship enterprise password vault.

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  • ANSI SQL Hierarchical Data Processing Basics

    The SQL-92 standard unknowingly and without planning introduced the capability to perform full hierarchical data processing with its introduction of the LEFT Outer Join operation. This natural hierarchical processing capability will be explained in this article.

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  • Parallel Data Warehouse

    - by jchang
    The Microsoft Parallel Data Warehouse diagram was somewhat difficult to understand in terms of the functionality of each subsystem in relation to the configuration of its components. So now that HP has provided a detailed list of the PDW components , the diagram below shows the PDW subsystems with component configuration (InfiniBand, FC, and network connections not shown). Observe that there are three different ProLiant server models, the DL360 G7, DL370 G6 and the DL380 G7, in five different configurations...(read more)

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  • Google I/O 2010 - Batch data processing with App Engine

    Google I/O 2010 - Batch data processing with App Engine Google I/O 2010 - Batch data processing with App Engine App Engine 201 Mike Aizatsky In this session, attendees will learn how to write map() functions, how to do simple reduce() operations, how to run these over large datasets, and how App Engine is used to accomplish such parallelism. For all I/O 2010 sessions, please go to code.google.com From: GoogleDevelopers Views: 6 0 ratings Time: 38:45 More in Science & Technology

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  • Parallel Tasks in .NET 3.0

    Provide a mechanism to execute a list of tasks in parallel on multiple threads and communicate back to the calling thread useful state such as exceptions, timeouts and successful task completion.

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  • Efficiently separating Read/Compute/Write steps for concurrent processing of entities in Entity/Component systems

    - by TravisG
    Setup I have an entity-component architecture where Entities can have a set of attributes (which are pure data with no behavior) and there exist systems that run the entity logic which act on that data. Essentially, in somewhat pseudo-code: Entity { id; map<id_type, Attribute> attributes; } System { update(); vector<Entity> entities; } A system that just moves along all entities at a constant rate might be MovementSystem extends System { update() { for each entity in entities position = entity.attributes["position"]; position += vec3(1,1,1); } } Essentially, I'm trying to parallelise update() as efficiently as possible. This can be done by running entire systems in parallel, or by giving each update() of one system a couple of components so different threads can execute the update of the same system, but for a different subset of entities registered with that system. Problem In reality, these systems sometimes require that entities interact(/read/write data from/to) each other, sometimes within the same system (e.g. an AI system that reads state from other entities surrounding the current processed entity), but sometimes between different systems that depend on each other (i.e. a movement system that requires data from a system that processes user input). Now, when trying to parallelize the update phases of entity/component systems, the phases in which data (components/attributes) from Entities are read and used to compute something, and the phase where the modified data is written back to entities need to be separated in order to avoid data races. Otherwise the only way (not taking into account just "critical section"ing everything) to avoid them is to serialize parts of the update process that depend on other parts. This seems ugly. To me it would seem more elegant to be able to (ideally) have all processing running in parallel, where a system may read data from all entities as it wishes, but doesn't write modifications to that data back until some later point. The fact that this is even possible is based on the assumption that modification write-backs are usually very small in complexity, and don't require much performance, whereas computations are very expensive (relatively). So the overhead added by a delayed-write phase might be evened out by more efficient updating of entities (by having threads work more % of the time instead of waiting). A concrete example of this might be a system that updates physics. The system needs to both read and write a lot of data to and from entities. Optimally, there would be a system in place where all available threads update a subset of all entities registered with the physics system. In the case of the physics system this isn't trivially possible because of race conditions. So without a workaround, we would have to find other systems to run in parallel (which don't modify the same data as the physics system), other wise the remaining threads are waiting and wasting time. However, that has disadvantages Practically, the L3 cache is pretty much always better utilized when updating a large system with multiple threads, as opposed to multiple systems at once, which all act on different sets of data. Finding and assembling other systems to run in parallel can be extremely time consuming to design well enough to optimize performance. Sometimes, it might even not be possible at all because a system just depends on data that is touched by all other systems. Solution? In my thinking, a possible solution would be a system where reading/updating and writing of data is separated, so that in one expensive phase, systems only read data and compute what they need to compute, and then in a separate, performance-wise cheap, write phase, attributes of entities that needed to be modified are finally written back to the entities. The Question How might such a system be implemented to achieve optimal performance, as well as making programmer life easier? What are the implementation details of such a system and what might have to be changed in the existing EC-architecture to accommodate this solution?

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  • deciding between subprocess, multiprocesser and thread in Python?

    - by user248237
    I'd like to parallelize my Python program so that it can make use of multiple processors on the machine that it runs on. My parallelization is very simple, in that all the parallel "threads" of the program are independent and write their output to separate files. I don't need the threads to exchange information but it is imperative that I know when the threads finish since some steps of my pipeline depend on their output. Portability is important, in that I'd like this to run on any Python version on Mac, Linux and Windows. Given these constraints, which is the most appropriate Python module for implementing this? I am tryign to decide between thread, subprocess and multiprocessing, which all seem to provide related functionality. Any thoughts on this? I'd like the simplest solution that's portable. Thanks.

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  • Parallelizing some LINQ to XML

    - by Lol coder
    How can I make this code run in parallel? List<Crop> crops = new List<Crop>(); //Get up to 10 pages of data. for (int i = 1; i < 10; i++) { //i is basically used for paging. XDocument document = XDocument.Load(string.Format(url, i)); crops.AddRange(from c in document.Descendants("CropType") select new Crop { //The data here. }); }

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  • Hardware advice for bitmap / openGL image processing server?

    - by pdizz
    I am trying to work out a build for a processing server to handle bitmap processing as well as openGL rendering for chroma-keying images and Photoshop automation. My searches here and on Google have turned up surprisingly few results, and seeing that there aren't tags for bitmap or image processing I take it this is a specialized application. The bitmap processing is very cpu-intensive while the chroma-keying and Photoshop stuff is gpu-intensive. I doubt this is a case of over-optimization as our company batches thousands of images a day (currently on individual workstations) and any saving in processing time and workstation down-time would be beneficial. Does anyone have any experience with this type of processing server? Any special considerations that would go into a build like this or am I over-thinking it?

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  • In ParallelPython, a method of an object ( object.func() ) fails to manipulate a variable of an object ( object.value )

    - by mehmet.ali.anil
    With parallelpython, I am trying to convert my old serial code to parallel, which heavily relies on objects that have methods that change that object's variables. A stripped example in which I omit the syntax in favor of simplicity: class Network: self.adjacency_matrix = [ ... ] self.state = [ ... ] self.equilibria = [ ... ] ... def populate_equilibria(self): # this function takes every possible value that self.state can be in # runs the boolean dynamical system # and writes an integer within self.equilibria for each self.state # doesn't return anything I call this method as: Code: j1 = jobserver.submit(net2.populate_equilibria,(),(),("numpy as num")) The job is sumbitted, and I know that a long computation takes place, so I speculate that my code is ran. The problem is, i am new to parallelpython , I was expecting that, when the method is called, the variable net2.equilibria would be written accordingly, and I would get a revised object (net2) . That is how my code works, independent objects with methods that act upon the object's variables. Rather, though the computation is apparent, and reasonably timed, the variable net2.equilibria remains unchanged. As if PP only takes the function and the object, computes it elsewhere, but never returns the object, so I am left with the old one. What do I miss? Thanks in advance.

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  • Would this method work to scale out SQL queries?

    - by David
    I have a database containing a single huge table. At the moment a query can take anything from 10 to 20 minutes and I need that to go down to 10 seconds. I have spent months trying different products like GridSQL. GridSQL works fine, but is using its own parser which does not have all the needed features. I have also optimized my database in various ways without getting the speedup I need. I have a theory on how one could scale out queries, meaning that I utilize several nodes to run a single query in parallel. The idea is to take an incoming SQL query and simply run it exactly like it is on all the nodes. When the results are returned to a coordinator node, the same query is run on the union of the resultsets. I realize that an aggregate function like average need to be rewritten into a count and sum to the nodes and that the coordinator divides the sum of the sums with the sum of the counts to get the average. What kinds of problems could not easily be solved using this model. I believe one issue would be the count distinct function. Edit: I am getting so many nice suggestions, but none have addressed the method.

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  • ALERT: Error Processing US Wage Attachment Elements In Payroll Run After RUP Patches

    - by LuciaC
    Customers who have run the Upgrade Wage Attachments process after applying the 2012 RUP are reporting errors similar to those listed below when either running a quickpay or processing a payroll for employee(s) with involuntary deductions. Error: HR_51118_HRPROC_ERR_ON_ASG ASGNO 1115 APP-PAY-51118: Error was encountered when processing assignment 1115 HR_51119_HRPROC_ERR_OCC_ON_ET ETNAME: Garnishment 3 APP-PAY-51119: Error was encountered when processing Element Type Garnishment 3 HR_6881_HRPROC_ORA_ERR SQLERRMC ORA-01403: No data found SQL_NO 520 TABLE_NAME pay_input_values_f APP-PAY-06881:Error ORA-01403: no data found has occured in table pay_input_values_f at location 520 This issue was logged in Bug 14679161 - QUICK PAY ERROR AFTER RUP (2012) AND WAGE ATTACHMENT UPGRADE APP-PAY-06881. The following one off patches have been released to My Oracle Support to resolve this issue*: 11i -  Patch 14679161 12.0 - Patch 14849394:R12.PAY.A 12.1 - Patch 14849394:R12.PAY.B * IMPORTANT:  Depending on when/if customers have run the Wage Attachment upgrade process will determine the appropriate action to take. Any customer who is encountering the above error and/or has run the Wage Attachment upgrade process AFTER applying the 2012 RUP (applicable to their release level) should log a Service Request with Oracle Support to receive assistance on the necessary steps to take to resolve the problem BEFORE applying the above patch. Any customer who has not yet run the Wage Attachment Upgrade process (either before or after applying the 2012 RUP), should follow the action plan documented in the patch readme. For those customers who have already run the Wage Attachment Upgrade process BEFORE applying the 2012 RUP, should apply the patch (applicable to your release) listed above. Be sure to run any post install processes, such as the data install utility and HR global driver.  See the patch readme for full details. Please consult Note 404478.1: Americas (US, CA, MX) HCM High Priority Alert for the latest Alert status.

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  • Impact of Server Failure on Coherence Request Processing

    - by jpurdy
    Requests against a given cache server may be temporarily blocked for several seconds following the failure of other cluster members. This may cause issues for applications that can not tolerate multi-second response times even during failover processing (ignoring for the moment that in practice there are a variety of issues that make such absolute guarantees challenging even when there are no server failures). In general, Coherence is designed around the principle that failures in one member should not affect the rest of the cluster if at all possible. However, it's obvious that if that failed member was managing a piece of state that another member depends on, the second member will need to wait until a new member assumes responsibility for managing that state. This transfer of responsibility is (as of Coherence 3.7) performed by the primary service thread for each cache service. The finest possible granularity for transferring responsibility is a single partition. So the question becomes how to minimize the time spent processing each partition. Here are some optimizations that may reduce this period: Reduce the size of each partition (by increasing the partition count) Increase the number of JVMs across the cluster (increasing the total number of primary service threads) Increase the number of CPUs across the cluster (making sure that each JVM has a CPU core when needed) Re-evaluate the set of configured indexes (as these will need to be rebuilt when a partition moves) Make sure that the backing map is as fast as possible (in most cases this means running on-heap) Make sure that the cluster is running on hardware with fast CPU cores (since the partition processing is single-threaded) As always, proper testing is required to make sure that configuration changes have the desired effect (and also to quantify that effect).

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  • Reducing moire when downsampling halftone comic images.

    - by drawnonward
    How can I reduce moire effects when downsampling halftone comic book images during live zoom on an iPhone or iPad? I am writing a comic book viewer. It would be nice to provide higher resolution images and allow the user to zoom in while reading the comic book. However, my client is averse to moire effects and will not allow this feature if there are noticeable moire artifacts while zooming, which of course there are. Modifying the images to be less susceptible to moire would only work if the modifications were not perceptible. Blur was specifically prohibited, as is anything that removes the beloved halftone dots. The images are black and white halftone and line art. The originals are 600 dpi but what we ship with the application will be half that at best, so probably 2500 pixels or less tall. So what are my options? If I write a custom downsampling algorithm would it be fast enough for real time on these devices? Are there other tricks I can do? Would it work to just avoid the size ratios that have the most visual moire effects? As you zoom in an out, there are definitely peaks where the moire effects are worst. Is there a way to calculate what those points are and just zoom to a nearby scale that is not as bad? Any suggestions are welcome. I have very little experience with image and signal processing, but am enjoying the opportunity to learn. I know nothing of wavelets and acutance and other jargon, so please be verbose.

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  • Recommendations for distributed processing/distributed storage systems

    - by Eddie
    At my organization we have a processing and storage system spread across two dozen linux machines that handles over a petabyte of data. The system right now is very ad-hoc; processing automation and data management is handled by a collection of large perl programs on independent machines. I am looking at distributed processing and storage systems to make it easier to maintain, evenly distribute load and data with replication, and grow in disk space and compute power. The system needs to be able to handle millions of files, varying in size between 50 megabytes to 50 gigabytes. Once created, the files will not be appended to, only replaced completely if need be. The files need to be accessible via HTTP for customer download. Right now, processing is automated by perl scripts (that I have complete control over) which call a series of other programs (that I don't have control over because they are closed source) that essentially transforms one data set into another. No data mining happening here. Here is a quick list of things I am looking for: Reliability: These data must be accessible over HTTP about 99% of the time so I need something that does data replication across the cluster. Scalability: I want to be able to add more processing power and storage easily and rebalance the data on across the cluster. Distributed processing: Easy and automatic job scheduling and load balancing that fits with processing workflow I briefly described above. Data location awareness: Not strictly required but desirable. Since data and processing will be on the same set of nodes I would like the job scheduler to schedule jobs on or close to the node that the data is actually on to cut down on network traffic. Here is what I've looked at so far: Storage Management: GlusterFS: Looks really nice and easy to use but doesn't seem to have a way to figure out what node(s) a file actually resides on to supply as a hint to the job scheduler. GPFS: Seems like the gold standard of clustered filesystems. Meets most of my requirements except, like glusterfs, data location awareness. Ceph: Seems way to immature right now. Distributed processing: Sun Grid Engine: I have a lot of experience with this and it's relatively easy to use (once it is configured properly that is). But Oracle got its icy grip around it and it no longer seems very desirable. Both: Hadoop/HDFS: At first glance it looked like hadoop was perfect for my situation. Distributed storage and job scheduling and it was the only thing I found that would give me the data location awareness that I wanted. But I don't like the namename being a single point of failure. Also, I'm not really sure if the MapReduce paradigm fits the type of processing workflow that I have. It seems like you need to write all your software specifically for MapReduce instead of just using Hadoop as a generic job scheduler. OpenStack: I've done some reading on this but I'm having trouble deciding if it fits well with my problem or not. Does anyone have opinions or recommendations for technologies that would fit my problem well? Any suggestions or advise would be greatly appreciated. Thanks!

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  • Parallel Computing Platform Developer Lab

    This is an exciting announcement that I must share: "Microsoft Developer & Platform Evangelism, in collaboration with the Microsoft Parallel Computing Platform product team, is hosting a developer lab at the Platform Adoption Center on April 12-15, 2010.  This event is for Microsoft Partners and Customers seeking to incorporate either .NET Framework 4 or Visual C++ 2010 parallelism features into their new or existing applications, and to gain expertise with new Visual Studio 2010 tools...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • White Paper on Parallel Processing

    - by Andrew Kelly
      I just ran across what I think is a newly published white paper on parallel  processing in SQL Server 2008 R2. The date is October 2010 but this is the first time I have seen it so I am not 100% sure how new it really is. Maybe you have seen it already but if not I recommend taking a look. So far I haven’t had time to read it extensively but from a cursory look it appears to be quite informative and this is one of the areas least understood by a lot of dba’s. It was authored by Don Pinto...(read more)

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