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  • How should I implement a command processing application?

    - by Nini Michaels
    I want to make a simple, proof-of-concept application (REPL) that takes a number and then processes commands on that number. Example: I start with 1. Then I write "add 2", it gives me 3. Then I write "multiply 7", it gives me 21. Then I want to know if it is prime, so I write "is prime" (on the current number - 21), it gives me false. "is odd" would give me true. And so on. Now, for a simple application with few commands, even a simple switch would do for processing the commands. But if I want extensibility, how would I need to implement the functionality? Do I use the command pattern? Do I build a simple parser/interpreter for the language? What if I want more complex commands, like "multiply 5 until >200" ? What would be an easy way to extend it (add new commands) without recompiling? Edit: to clarify a few things, my end goal would not be to make something similar to WolframAlpha, but rather a list (of numbers) processor. But I want to start slowly at first (on single numbers). I'm having in mind something similar to the way one would use Haskell to process lists, but a very simple version. I'm wondering if something like the command pattern (or equivalent) would suffice, or if I have to make a new mini-language and a parser for it to achieve my goals?

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  • Form, function and complexity in rule processing

    - by Charles Young
    Tim Bass posted on ‘Orwellian Event Processing’. I was involved in a heated exchange in the comments, and he has more recently published a post entitled ‘Disadvantages of Rule-Based Systems (Part 1)’. Whatever the rights and wrongs of our exchange, it clearly failed to generate any agreement or understanding of our different positions. I don't particularly want to promote further argument of that kind, but I do want to take the opportunity of offering a different perspective on rule-processing and an explanation of my comments. For me, the ‘red rag’ lay in Tim’s claim that “...rules alone are highly inefficient for most classes of (not simple) problems” and a later paragraph that appears to equate the simplicity of form (‘IF-THEN-ELSE’) with simplicity of function.   It is not the first time Tim has expressed these views and not the first time I have responded to his assertions.   Indeed, Tim has a long history of commenting on the subject of complex event processing (CEP) and, less often, rule processing in ‘robust’ terms, often asserting that very many other people’s opinions on this subject are mistaken.   In turn, I am of the opinion that, certainly in terms of rule processing, which is an area in which I have a specific interest and knowledge, he is often mistaken. There is no simple answer to the fundamental question ‘what is a rule?’ We use the word in a very fluid fashion in English. Likewise, the term ‘rule processing’, as used widely in IT, is equally difficult to define simplistically. The best way to envisage the term is as a ‘centre of gravity’ within a wider domain. That domain contains many other ‘centres of gravity’, including CEP, statistical analytics, neural networks, natural language processing and so much more. Whole communities tend to gravitate towards and build themselves around some of these centres. The term 'rule processing' is associated with many different technology types, various software products, different architectural patterns, the functional capability of many applications and services, etc. There is considerable variation amongst these different technologies, techniques and products. Very broadly, a common theme is their ability to manage certain types of processing and problem solving through declarative, or semi-declarative, statements of propositional logic bound to action-based consequences. It is generally important to be able to decouple these statements from other parts of an overall system or architecture so that they can be managed and deployed independently.  As a centre of gravity, ‘rule processing’ is no island. It exists in the context of a domain of discourse that is, itself, highly interconnected and continuous.   Rule processing does not, for example, exist in splendid isolation to natural language processing.   On the contrary, an on-going theme of rule processing is to find better ways to express rules in natural language and map these to executable forms.   Rule processing does not exist in splendid isolation to CEP.   On the contrary, an event processing agent can reasonably be considered as a rule engine (a theme in ‘Power of Events’ by David Luckham).   Rule processing does not live in splendid isolation to statistical approaches such as Bayesian analytics. On the contrary, rule processing and statistical analytics are highly synergistic.   Rule processing does not even live in splendid isolation to neural networks. For example, significant research has centred on finding ways to translate trained nets into explicit rule sets in order to support forms of validation and facilitate insight into the knowledge stored in those nets. What about simplicity of form?   Many rule processing technologies do indeed use a very simple form (‘If...Then’, ‘When...Do’, etc.)   However, it is a fundamental mistake to equate simplicity of form with simplicity of function.   It is absolutely mistaken to suggest that simplicity of form is a barrier to the efficient handling of complexity.   There are countless real-world examples which serve to disprove that notion.   Indeed, simplicity of form is often the key to handling complexity. Does rule processing offer a ‘one size fits all’. No, of course not.   No serious commentator suggests it does.   Does the design and management of large knowledge bases, expressed as rules, become difficult?   Yes, it can do, but that is true of any large knowledge base, regardless of the form in which knowledge is expressed.   The measure of complexity is not a function of rule set size or rule form.  It tends to be correlated more strongly with the size of the ‘problem space’ (‘search space’) which is something quite different.   Analysis of the problem space and the algorithms we use to search through that space are, of course, the very things we use to derive objective measures of the complexity of a given problem. This is basic computer science and common practice. Sailing a Dreadnaught through the sea of information technology and lobbing shells at some of the islands we encounter along the way does no one any good.   Building bridges and causeways between islands so that the inhabitants can collaborate in open discourse offers hope of real progress.

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  • TV audio processing with TV capture card

    - by Jonathan Barbero
    Hello, I'm looking for an open source library or framework to process audio signal from a TV capture card. The idea is to detect TV ad spots and register the time and the channel where them happends. I never worked in something like this, so, any information, link, idea is welcome. Thanks in advance!

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  • configure Squid3 proxy server on Ubuntu with caching and logging

    - by Panshul
    I have a ubuntu 11.10 machine. Installed Squid3. When i configure the squid as http_access allow all, everything works fine. my current configuration mostly default is as follows: 2012/09/10 13:19:57| Processing Configuration File: /etc/squid3/squid.conf (depth 0) 2012/09/10 13:19:57| Processing: acl manager proto cache_object 2012/09/10 13:19:57| Processing: acl localhost src 127.0.0.1/32 ::1 2012/09/10 13:19:57| Processing: acl to_localhost dst 127.0.0.0/8 0.0.0.0/32 ::1 2012/09/10 13:19:57| Processing: acl SSL_ports port 443 2012/09/10 13:19:57| Processing: acl Safe_ports port 80 # http 2012/09/10 13:19:57| Processing: acl Safe_ports port 21 # ftp 2012/09/10 13:19:57| Processing: acl Safe_ports port 443 # https 2012/09/10 13:19:57| Processing: acl Safe_ports port 70 # gopher 2012/09/10 13:19:57| Processing: acl Safe_ports port 210 # wais 2012/09/10 13:19:57| Processing: acl Safe_ports port 1025-65535 # unregistered ports 2012/09/10 13:19:57| Processing: acl Safe_ports port 280 # http-mgmt 2012/09/10 13:19:57| Processing: acl Safe_ports port 488 # gss-http 2012/09/10 13:19:57| Processing: acl Safe_ports port 591 # filemaker 2012/09/10 13:19:57| Processing: acl Safe_ports port 777 # multiling http 2012/09/10 13:19:57| Processing: acl CONNECT method CONNECT 2012/09/10 13:19:57| Processing: http_access allow manager localhost 2012/09/10 13:19:57| Processing: http_access deny manager 2012/09/10 13:19:57| Processing: http_access deny !Safe_ports 2012/09/10 13:19:57| Processing: http_access deny CONNECT !SSL_ports 2012/09/10 13:19:57| Processing: http_access allow localhost 2012/09/10 13:19:57| Processing: http_access deny all 2012/09/10 13:19:57| Processing: http_port 3128 2012/09/10 13:19:57| Processing: coredump_dir /var/spool/squid3 2012/09/10 13:19:57| Processing: refresh_pattern ^ftp: 1440 20% 10080 2012/09/10 13:19:57| Processing: refresh_pattern ^gopher: 1440 0% 1440 2012/09/10 13:19:57| Processing: refresh_pattern -i (/cgi-bin/|\?) 0 0% 0 2012/09/10 13:19:57| Processing: refresh_pattern (Release|Packages(.gz)*)$ 0 20% 2880 2012/09/10 13:19:57| Processing: refresh_pattern . 0 20% 4320 2012/09/10 13:19:57| Processing: http_access allow all 2012/09/10 13:19:57| Processing: cache_mem 512 MB 2012/09/10 13:19:57| Processing: logformat squid3 %ts.%03tu %6tr %>a %Ss/%03>Hs %<st %rm %ru 2012/09/10 13:19:57| Processing: access_log /home/panshul/squidCache/log/access.log squid3 The problem starts when I enable the following line: access_log /home/panshul/squidCache/log/access.log I start to get proxy server is refusing connections error in the browser. on commenting out the above line in my config, things go back to normal. The second problem starts when i add the following line to my config: cache_dir ufs /home/panshul/squidCache/cache 100 16 256 The squid server fails to start. Any suggestions what am I missing in the config. Please help.!!

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  • Most useful parallel programming algorithm?

    - by Zubair
    I recenty asked a question about parallel programming algorithms which was closed quite fast due to my bad ability to communicate my intent: http://stackoverflow.com/questions/2407631/what-is-the-most-useful-parallel-programming-algorithm-closed I had also recently asked another question, specifically: http://stackoverflow.com/questions/2407493/is-mapreduce-such-a-generalisation-of-another-programming-principle/2407570#2407570 The other question was specifically about map reduce and to see if mapreduce was a more specific version of some other concept in parallel programming. This question (about a useful parallel programming algorithm) is more about the whole series of algorithms for parallel programming. You will have to excuse me though as I am quite new to parallel programming, so maybe MapReduce or something that is a more general form of mapreduce is the "only" parallel programming construct which is available, in which case I apologise for my ignorance

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  • Python Access Parallel Port

    - by PPTim
    Hi, I've been trying to access the parallel port with pyParallel, which is in the same sourceforge as PySerial: http://sourceforge.net/projects/pyserial/files/ I'm getting a WidowsError: exception: priviledged instruciton. Has anyone used this module before? import parallel p = parallel.Parallel() Traceback (most recent call last): File "<interactive input>", line 1, in <module> File "C:\Python26\lib\site-packages\parallel\parallelwin32.py", line 74, in __init__ self.ctrlReg = _pyparallel.inp(self.ctrlRegAdr) WindowsError: exception: priviledged instruction

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  • Parallel Computing in .Net 4.0

    - by kaleidoscope
    Technorati Tags: Ram,Parallel Computing in .Net 4.0 Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem: To be run using multiple CPUs A problem is broken into discrete parts that can be solved concurrently Each part is further broken down to a series of instructions Instructions from each part execute simultaneously on different CPUs Parallel Extensions in .NET 4.0 provides a set of libraries and tools to achieve the above mentioned objectives. This supports two paradigms of parallel computing Data Parallelism – This refers to dividing the data across multiple processors for parallel execution.e.g we are processing an array of 1000 elements we can distribute the data between two processors say 500 each. This is supported by the Parallel LINQ (PLINQ) in .NET 4.0 Task Parallelism – This breaks down the program into multiple tasks which can be parallelized and are executed on different processors. This is supported by Task Parallel Library (TPL) in .NET 4.0 A high level view is shown below:

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  • Scalable / Parallel Large Graph Analysis Library?

    - by Joel Hoff
    I am looking for good recommendations for scalable and/or parallel large graph analysis libraries in various languages. The problems I am working on involve significant computational analysis of graphs/networks with 1-100 million nodes and 10 million to 1+ billion edges. The largest SMP computer I am using has 256 GB memory, but I also have access to an HPC cluster with 1000 cores, 2 TB aggregate memory, and MPI for communication. I am primarily looking for scalable, high-performance graph libraries that could be used in either single or multi-threaded scenarios, but parallel analysis libraries based on MPI or a similar protocol for communication and/or distributed memory are also of interest for high-end problems. Target programming languages include C++, C, Java, and Python. My research to-date has come up with the following possible solutions for these languages: C++ -- The most viable solutions appear to be the Boost Graph Library and Parallel Boost Graph Library. I have looked briefly at MTGL, but it is currently slanted more toward massively multithreaded hardware architectures like the Cray XMT. C - igraph and SNAP (Small-world Network Analysis and Partitioning); latter uses OpenMP for parallelism on SMP systems. Java - I have found no parallel libraries here yet, but JGraphT and perhaps JUNG are leading contenders in the non-parallel space. Python - igraph and NetworkX look like the most solid options, though neither is parallel. There used to be Python bindings for BGL, but these are now unsupported; last release in 2005 looks stale now. Other topics here on SO that I've looked at have discussed graph libraries in C++, Java, Python, and other languages. However, none of these topics focused significantly on scalability. Does anyone have recommendations they can offer based on experience with any of the above or other library packages when applied to large graph analysis problems? Performance, scalability, and code stability/maturity are my primary concerns. Most of the specialized algorithms will be developed by my team with the exception of any graph-oriented parallel communication or distributed memory frameworks (where the graph state is distributed across a cluster).

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  • SQLAuthority News – Download Whitepaper – Understanding and Controlling Parallel Query Processing in SQL Server

    - by pinaldave
    My recently article SQL SERVER – Reducing CXPACKET Wait Stats for High Transactional Database has received many good comments regarding MAXDOP 1 and MAXDOP 0. I really enjoyed reading the comments as the comments are received from industry leaders and gurus. I was further researching on the subject and I end up on following white paper written by Microsoft. Understanding and Controlling Parallel Query Processing in SQL Server Data warehousing and general reporting applications tend to be CPU intensive because they need to read and process a large number of rows. To facilitate quick data processing for queries that touch a large amount of data, Microsoft SQL Server exploits the power of multiple logical processors to provide parallel query processing operations such as parallel scans. Through extensive testing, we have learned that, for most large queries that are executed in a parallel fashion, SQL Server can deliver linear or nearly linear response time speedup as the number of logical processors increases. However, some queries in high parallelism scenarios perform suboptimally. There are also some parallelism issues that can occur in a multi-user parallel query workload. This white paper describes parallel performance problems you might encounter when you run such queries and workloads, and it explains why these issues occur. In addition, it presents how data warehouse developers can detect these issues, and how they can work around them or mitigate them. To review the document, please download the Understanding and Controlling Parallel Query Processing in SQL Server Word document. Note: Above abstract has been taken from here. The real question is what does the parallel queries has made life of DBA much simpler or is it looked at with potential issue related to degradation of the performance? Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, SQL White Papers, SQLAuthority News, T SQL, Technology

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  • Parallel port blocking

    - by asalamon74
    I have a legacy Java program which handles a special card printer by sending binary data to the LPT1 port (no printer driver is involved, the Java program creates the binary stream). The program was working correctly with the client's old computer. The Java program sent all the bytes to the printer and after sending the last byte the program was not blocked. It took an other minute to finish the card printing, but the user was able to continue the work with the program. After changing the client's computer (but not the printer, or the Java program), the program does not finish the task till the card is ready, it is blocked until the last second. It seems to me that LPT1 has a different behavior now than was before. Is it possible to change this in Windows? I've checked BIOS for parallel port settings: The parallel port is set to EPP+ECP (but also tried the other two options: Bidirectional, Output only). Maybe some kind of parallel port buffer is too small? How can I increase it?

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  • Read non-blocking from multiple fifos in parallel

    - by Ole Tange
    I sometimes sit with a bunch of output fifos from programs that run in parallel. I would like to merge these fifos. The naïve solution is: cat fifo* > output But this requires the first fifo to complete before reading the first byte from the second fifo, and this will block the parallel running programs. Another way is: (cat fifo1 & cat fifo2 & ... ) > output But this may mix the output thus getting half-lines in output. When reading from multiple fifos, there must be some rules for merging the files. Typically doing it on a line by line basis is enough for me, so I am looking for something that does: parallel_non_blocking_cat fifo* > output which will read from all fifos in parallel and merge the output on with a full line at a time. I can see it is not hard to write that program. All you need to do is: open all fifos do a blocking select on all of them read nonblocking from the fifo which has data into the buffer for that fifo if the buffer contains a full line (or record) then print out the line if all fifos are closed/eof: exit goto 2 So my question is not: can it be done? My question is: Is it done already and can I just install a tool that does this?

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  • Parallel computing in .net

    - by HotTester
    Since the launch of .net 4.0 a new term that has got into lime light is parallel computing. Does parallel computing provide us some benefits or its just another concept or feature. Further is .net really going to utilize it in applications ? Further is parallel computing different from parallel programming ? Kindly throw some light on the issue in perspective of .net and some examples would be helpful. Thanks...

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  • Parallelism in .NET – Part 11, Divide and Conquer via Parallel.Invoke

    - by Reed
    Many algorithms are easily written to work via recursion.  For example, most data-oriented tasks where a tree of data must be processed are much more easily handled by starting at the root, and recursively “walking” the tree.  Some algorithms work this way on flat data structures, such as arrays, as well.  This is a form of divide and conquer: an algorithm design which is based around breaking up a set of work recursively, “dividing” the total work in each recursive step, and “conquering” the work when the remaining work is small enough to be solved easily. Recursive algorithms, especially ones based on a form of divide and conquer, are often a very good candidate for parallelization. This is apparent from a common sense standpoint.  Since we’re dividing up the total work in the algorithm, we have an obvious, built-in partitioning scheme.  Once partitioned, the data can be worked upon independently, so there is good, clean isolation of data. Implementing this type of algorithm is fairly simple.  The Parallel class in .NET 4 includes a method suited for this type of operation: Parallel.Invoke.  This method works by taking any number of delegates defined as an Action, and operating them all in parallel.  The method returns when every delegate has completed: Parallel.Invoke( () => { Console.WriteLine("Action 1 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); }, () => { Console.WriteLine("Action 2 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); }, () => { Console.WriteLine("Action 3 executing in thread {0}", Thread.CurrentThread.ManagedThreadId); } ); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Running this simple example demonstrates the ease of using this method.  For example, on my system, I get three separate thread IDs when running the above code.  By allowing any number of delegates to be executed directly, concurrently, the Parallel.Invoke method provides us an easy way to parallelize any algorithm based on divide and conquer.  We can divide our work in each step, and execute each task in parallel, recursively. For example, suppose we wanted to implement our own quicksort routine.  The quicksort algorithm can be designed based on divide and conquer.  In each iteration, we pick a pivot point, and use that to partition the total array.  We swap the elements around the pivot, then recursively sort the lists on each side of the pivot.  For example, let’s look at this simple, sequential implementation of quicksort: public static void QuickSort<T>(T[] array) where T : IComparable<T> { QuickSortInternal(array, 0, array.Length - 1); } private static void QuickSortInternal<T>(T[] array, int left, int right) where T : IComparable<T> { if (left >= right) { return; } SwapElements(array, left, (left + right) / 2); int last = left; for (int current = left + 1; current <= right; ++current) { if (array[current].CompareTo(array[left]) < 0) { ++last; SwapElements(array, last, current); } } SwapElements(array, left, last); QuickSortInternal(array, left, last - 1); QuickSortInternal(array, last + 1, right); } static void SwapElements<T>(T[] array, int i, int j) { T temp = array[i]; array[i] = array[j]; array[j] = temp; } Here, we implement the quicksort algorithm in a very common, divide and conquer approach.  Running this against the built-in Array.Sort routine shows that we get the exact same answers (although the framework’s sort routine is slightly faster).  On my system, for example, I can use framework’s sort to sort ten million random doubles in about 7.3s, and this implementation takes about 9.3s on average. Looking at this routine, though, there is a clear opportunity to parallelize.  At the end of QuickSortInternal, we recursively call into QuickSortInternal with each partition of the array after the pivot is chosen.  This can be rewritten to use Parallel.Invoke by simply changing it to: // Code above is unchanged... SwapElements(array, left, last); Parallel.Invoke( () => QuickSortInternal(array, left, last - 1), () => QuickSortInternal(array, last + 1, right) ); } This routine will now run in parallel.  When executing, we now see the CPU usage across all cores spike while it executes.  However, there is a significant problem here – by parallelizing this routine, we took it from an execution time of 9.3s to an execution time of approximately 14 seconds!  We’re using more resources as seen in the CPU usage, but the overall result is a dramatic slowdown in overall processing time. This occurs because parallelization adds overhead.  Each time we split this array, we spawn two new tasks to parallelize this algorithm!  This is far, far too many tasks for our cores to operate upon at a single time.  In effect, we’re “over-parallelizing” this routine.  This is a common problem when working with divide and conquer algorithms, and leads to an important observation: When parallelizing a recursive routine, take special care not to add more tasks than necessary to fully utilize your system. This can be done with a few different approaches, in this case.  Typically, the way to handle this is to stop parallelizing the routine at a certain point, and revert back to the serial approach.  Since the first few recursions will all still be parallelized, our “deeper” recursive tasks will be running in parallel, and can take full advantage of the machine.  This also dramatically reduces the overhead added by parallelizing, since we’re only adding overhead for the first few recursive calls.  There are two basic approaches we can take here.  The first approach would be to look at the total work size, and if it’s smaller than a specific threshold, revert to our serial implementation.  In this case, we could just check right-left, and if it’s under a threshold, call the methods directly instead of using Parallel.Invoke. The second approach is to track how “deep” in the “tree” we are currently at, and if we are below some number of levels, stop parallelizing.  This approach is a more general-purpose approach, since it works on routines which parse trees as well as routines working off of a single array, but may not work as well if a poor partitioning strategy is chosen or the tree is not balanced evenly. This can be written very easily.  If we pass a maxDepth parameter into our internal routine, we can restrict the amount of times we parallelize by changing the recursive call to: // Code above is unchanged... SwapElements(array, left, last); if (maxDepth < 1) { QuickSortInternal(array, left, last - 1, maxDepth); QuickSortInternal(array, last + 1, right, maxDepth); } else { --maxDepth; Parallel.Invoke( () => QuickSortInternal(array, left, last - 1, maxDepth), () => QuickSortInternal(array, last + 1, right, maxDepth)); } We no longer allow this to parallelize indefinitely – only to a specific depth, at which time we revert to a serial implementation.  By starting the routine with a maxDepth equal to Environment.ProcessorCount, we can restrict the total amount of parallel operations significantly, but still provide adequate work for each processing core. With this final change, my timings are much better.  On average, I get the following timings: Framework via Array.Sort: 7.3 seconds Serial Quicksort Implementation: 9.3 seconds Naive Parallel Implementation: 14 seconds Parallel Implementation Restricting Depth: 4.7 seconds Finally, we are now faster than the framework’s Array.Sort implementation.

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  • Which databases support parallel processing across multiple servers?

    - by David
    I need a database engine that can utilize multiple servers for processing a single SQL query in parallel. So far I know that this is possible with the some engines, though none of them are feasible for me either because of pricing or missing features. The engines currently known to me are: MS SQL (enterprise) DB2 (enterprise) Oracle (enterprise) GridSQL Greenplum Which other engines have this feature? Do you have any experience with using this feature? Edit: I have now proposed a method for creating one myself. Any input is welcome. Edit: I have found another one: Informix Extended Parallel Server

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  • SQL Peer-to-Peer Dynamic Structured Data Processing Collaboration

    Unstructured and XML semi-structured data is now used more than structured data. But fixed structured data still keeps businesses running day in and day out, which requires consistent predictable highly principled processing for correct results. For this reason, it would be very useful to have a general purpose SQL peer-to-peer collaboration capability that can utilize highly principled hierarchical data processing and its flexible and advanced structured processing to support dynamically structured data and its dynamic structured processing. This flexible dynamic structured processing can change the structure of the data as necessary for the required processing while preserving the relational and hierarchical data principles. This processing will perform freely across remote unrelated peer locations anytime and transparently process unpredictable and unknown structured data and data type changes automatically for immediate processing using automatic metadata maintenance.

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  • Parallel Port Problem in 12.04

    - by Frank Oberle
    I have a “dumb” printer attached to a parallel port in my machine which works fine under the “other” resident operating system (from Redmond) on the same machine. I recently added Ubuntu 12.04 as a dual boot on the machine, but Ubuntu doesn't seem to recognize the parallel port at all. All I need to set up a printer is a really plain-vanilla fixed pitch text-only generic driver, which is present, but no parallel ports show up. (The other printers, all on USB ports, seem to work just fine). Following what appeared to me to be the most reasonable of the many conflicting pieces of advice on the web, here's what I did: I added the following lines to /etc/modules parport_pc ppdev parport Then, after rebooting, I checked to see that the lines were still present, and they were. I ran dmesg | grep par and got the following references in the output that seemed like they might have to do with the parallel port: [ 14.169511] parport_pc 0000:03:07.0: PCI INT A -> GSI 21 (level, low) -> IRQ 21 [ 14.169516] PCI parallel port detected: 9710:9805, I/O at 0xce00(0xcd00), IRQ 21 [ 14.169577] parport0: PC-style at 0xce00 (0xcd00), irq 21, using FIFO [PCSPP,TRISTATE,COMPAT,ECP] [ 14.354254] lp0: using parport0 (interrupt-driven). [ 14.571358] ppdev: user-space parallel port driver [ 16.588304] type=1400 audit(1347226670.386:5): apparmor="STATUS" operation="profile_load" name="/usr/lib/cups/backend/cups-pdf" pid=964 comm="apparmor_parser" [ 16.588756] type=1400 audit(1347226670.386:6): apparmor="STATUS" operation="profile_load" name="/usr/sbin/cupsd" pid=964 comm="apparmor_parser" [ 16.673679] type=1400 audit(1347226670.470:7): apparmor="STATUS" operation="profile_load" name="/usr/lib/lightdm/lightdm/lightdm-guest-session-wrapper" pid=1010 comm="apparmor_parser" [ 16.675252] type=1400 audit(1347226670.470:8): apparmor="STATUS" operation="profile_load" name="/usr/lib/telepathy/mission-control-5" pid=1014 comm="apparmor_parser" [ 16.675716] type=1400 audit(1347226670.470:9): apparmor="STATUS" operation="profile_load" name="/usr/lib/telepathy/telepathy-*" pid=1014 comm="apparmor_parser" [ 16.676636] type=1400 audit(1347226670.474:10): apparmor="STATUS" operation="profile_replace" name="/usr/lib/cups/backend/cups-pdf" pid=1015 comm="apparmor_parser" [ 16.677124] type=1400 audit(1347226670.474:11): apparmor="STATUS" operation="profile_replace" name="/usr/sbin/cupsd" pid=1015 comm="apparmor_parser" [ 1545.725328] parport0: ppdev0 forgot to release port I have no idea what any of that means, but the line “parport0: ppdev0 forgot to release port ” seems unusual. I was still unable to add a printer for my old clunker, so I tried the direct approach, typing echo “Hello” > /dev/lp0 and received a Permission denied message. I then tried echo “Hello” > /dev/parport0 which didn't give me any message at all, but still didn't print anything. Running the command sudo /usr/lib/cups/backend/parallel gives the following: direct parallel:/dev/lp0 "unknown" "LPT #1" "" "" Checking the permissions for /dev/parport0, Owner, Group, and Other are all set to read and write. crw-rw---- 1 root lp 6, 0 Sep 9 16:37 /dev/lp0 crw-rw-rw- 1 root lp 99, 0 Sep 9 16:37 /dev/parport0 The output of the command lpinfo -v includes the following line: direct parallel:/dev/lp0 I've read several web postings that seem to suggest this has been a problem for several years, but the bug reports were closed because there wasn't enough information to address the issue (shades of Microsoft!). Any suggestions as to what I might be missing here?

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  • Improve efficiency when using parallel to read from compressed stream

    - by Yoga
    Is another question extended from the previous one [1] I have a compressed file and stream them to feed into a python program, e.g. bzcat data.bz2 | parallel --no-notice -j16 --pipe python parse.py > result.txt The parse.py can read from stdin continusuoly and print to stdout My ec2 instance is 16 cores but from the top command it is showing 3 to 4 load average only. From the ps, I am seeing a lot of stuffs like.. sh -c 'dd bs=1 count=1 of=/tmp/7D_YxccfY7.chr 2>/dev/null'; I know I can improve using the -a in.txtto improve performance, but with my case I am streaming from bz2 (I cannot exact it since I don't have enought disk space) How to improve the efficiency for my case? [1] Gnu parallel not utilizing all the CPU

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  • Databases supporting parallel processing across multiple servers

    - by David
    I need a database engine that can utilize multiple servers for processing a single SQL query in parallel. So far I know that this is possible with the some engines, though none of them are feasible for me either because of pricing or missing features. The engines currently known to me are: MS SQL (enterprise) DB2 (enterprise) Oracle (enterprise) GridSQL Greenplum

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  • Parallel File Copy

    - by Jon
    I have a list of files I need to copy on a Linux system - each file ranges from 10 to 100GB in size. I only want to copy to the local filesystem. Is there a way to do this in parallel - with multiple processes each responsible for copying a file - in a simple manner? I can easily write a multithreaded program to do this, but I'm interested in finding out if there's a low level Linux method for doing this.

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  • Dns with parallel resolution

    - by viraptor
    I'm looking for a simple caching (can be caching-only) DNS server, which can do parallel resolving on its own. Is there something like that available? Alternatively I know there's the c-ares library, which can do multiple-hosts resolution, but it's not a drop-in replacement for libresolve that I could use in the affected software. Maybe there is some other lib which can fulfill this requirement?

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