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  • Towards an F# .NET Reflector add-in

    - by CliveT
    When I had the opportunity to spent some time during Red Gate's recent "down tools" week on a project of my choice, the obvious project was an F# add-in for Reflector . To be honest, this was a bit of a misnomer as the amount of time in the designated week for coding was really less than three days, so it was always unlikely that very much progress would be made in such a small amount of time (and that certainly proved to be the case), but I did learn some things from the experiment. Like lots of problems, one useful technique is to take examples, get them to work, and then generalise to get something that works across the board. Unfortunately, I didn't have enough time to do the last stage. The obvious first step is to take a few function definitions, starting with the obvious hello world, moving on to a non-recursive function and finishing with the ubiquitous recursive Fibonacci function. let rec printMessage message  =     printfn  message let foo x  =    (x + 1) let rec fib x  =     if (x >= 2) then (fib (x - 1) + fib (x - 2)) else 1 The major problem in decompiling these simple functions is that Reflector has an in-memory object model that is designed to support object-oriented languages. In particular it has a return statement that allows function bodies to finish early. I used some of the in-built functionality to take the IL and produce an in-memory object model for the language, but then needed to write a transformer to push the return statements to the top of the tree to make it easy to render the code into a functional language. This tree transform works in some scenarios, but not in others where we simply regenerate code that looks more like CPS style. The next thing to get working was library level bindings of values where these values are calculated at runtime. let x = [1 ; 2 ; 3 ; 4] let y = List.map  (fun x -> foo x) x The way that this is translated into a set of classes for the underlying platform means that the code needs to follow references around, from the property exposing the calculated value to the class in which the code for generating the value is embedded. One of the strongest selling points of functional languages is the algebraic datatypes, which allow definitions via standard mathematical-style inductive definitions across the union cases. type Foo =     | Something of int     | Nothing type 'a Foo2 =     | Something2 of 'a     | Nothing2 Such a definition is compiled into a number of classes for the cases of the union, which all inherit from a class representing the type itself. It wasn't too hard to get such a de-compilation happening in the cases I tried. What did I learn from this? Firstly, that there are various bits of functionality inside Reflector that it would be useful for us to allow add-in writers to access. In particular, there are various implementations of the Visitor pattern which implement algorithms such as calculating the number of references for particular variables, and which perform various substitutions which could be more generally useful to add-in writers. I hope to do something about this at some point in the future. Secondly, when you transform a functional language into something that runs on top of an object-based platform, you lose some fidelity in the representation. The F# compiler leaves attributes in place so that tools can tell which classes represent classes from the source program and which are there for purposes of the implementation, allowing the decompiler to regenerate these constructs again. However, decompilation technology is a long way from being able to take unannotated IL and transform it into a program in a different language. For a simple function definition, like Fibonacci, I could write a simple static function and have it come out in F# as the same function, but it would be practically impossible to take a mass of class definitions and have a decompiler translate it automatically into an F# algebraic data type. What have we got out of this? Some data on the feasibility of implementing an F# decompiler inside Reflector, though it's hard at the moment to say how long this would take to do. The work we did is included the 6.5 EAP for Reflector that you can get from the EAP forum. All things considered though, it was a useful way to gain more familiarity with the process of writing an add-in and understand difficulties other add-in authors might experience. If you'd like to check out a video of Down Tools Week, click here.

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  • Getting started with Oracle Database In-Memory Part III - Querying The IM Column Store

    - by Maria Colgan
    In my previous blog posts, I described how to install, enable, and populate the In-Memory column store (IM column store). This weeks post focuses on how data is accessed within the IM column store. Let’s take a simple query “What is the most expensive air-mail order we have received to date?” SELECT Max(lo_ordtotalprice) most_expensive_order FROM lineorderWHERE  lo_shipmode = 5; The LINEORDER table has been populated into the IM column store and since we have no alternative access paths (indexes or views) the execution plan for this query is a full table scan of the LINEORDER table. You will notice that the execution plan has a new set of keywords “IN MEMORY" in the access method description in the Operation column. These keywords indicate that the LINEORDER table has been marked for INMEMORY and we may use the IM column store in this query. What do I mean by “may use”? There are a small number of cases were we won’t use the IM column store even though the object has been marked INMEMORY. This is similar to how the keyword STORAGE is used on Exadata environments. You can confirm that the IM column store was actually used by examining the session level statistics, but more on that later. For now let's focus on how the data is accessed in the IM column store and why it’s faster to access the data in the new column format, for analytical queries, rather than the buffer cache. There are four main reasons why accessing the data in the IM column store is more efficient. 1. Access only the column data needed The IM column store only has to scan two columns – lo_shipmode and lo_ordtotalprice – to execute this query while the traditional row store or buffer cache has to scan all of the columns in each row of the LINEORDER table until it reaches both the lo_shipmode and the lo_ordtotalprice column. 2. Scan and filter data in it's compressed format When data is populated into the IM column it is automatically compressed using a new set of compression algorithms that allow WHERE clause predicates to be applied against the compressed formats. This means the volume of data scanned in the IM column store for our query will be far less than the same query in the buffer cache where it will scan the data in its uncompressed form, which could be 20X larger. 3. Prune out any unnecessary data within each column The fastest read you can execute is the read you don’t do. In the IM column store a further reduction in the amount of data accessed is possible due to the In-Memory Storage Indexes(IM storage indexes) that are automatically created and maintained on each of the columns in the IM column store. IM storage indexes allow data pruning to occur based on the filter predicates supplied in a SQL statement. An IM storage index keeps track of minimum and maximum values for each column in each of the In-Memory Compression Unit (IMCU). In our query the WHERE clause predicate is on the lo_shipmode column. The IM storage index on the lo_shipdate column is examined to determine if our specified column value 5 exist in any IMCU by comparing the value 5 to the minimum and maximum values maintained in the Storage Index. If the value 5 is outside the minimum and maximum range for an IMCU, the scan of that IMCU is avoided. For the IMCUs where the value 5 does fall within the min, max range, an additional level of data pruning is possible via the metadata dictionary created when dictionary-based compression is used on IMCU. The dictionary contains a list of the unique column values within the IMCU. Since we have an equality predicate we can easily determine if 5 is one of the distinct column values or not. The combination of the IM storage index and dictionary based pruning, enables us to only scan the necessary IMCUs. 4. Use SIMD to apply filter predicates For the IMCU that need to be scanned Oracle takes advantage of SIMD vector processing (Single Instruction processing Multiple Data values). Instead of evaluating each entry in the column one at a time, SIMD vector processing allows a set of column values to be evaluated together in a single CPU instruction. The column format used in the IM column store has been specifically designed to maximize the number of column entries that can be loaded into the vector registers on the CPU and evaluated in a single CPU instruction. SIMD vector processing enables the Oracle Database In-Memory to scan billion of rows per second per core versus the millions of rows per second per core scan rate that can be achieved in the buffer cache. I mentioned earlier in this post that in order to confirm the IM column store was used; we need to examine the session level statistics. You can monitor the session level statistics by querying the performance views v$mystat and v$statname. All of the statistics related to the In-Memory Column Store begin with IM. You can see the full list of these statistics by typing: display_name format a30 SELECT display_name FROM v$statname WHERE  display_name LIKE 'IM%'; If we check the session statistics after we execute our query the results would be as follow; SELECT Max(lo_ordtotalprice) most_expensive_order FROM lineorderWHERE lo_shipmode = 5; SELECT display_name FROM v$statname WHERE  display_name IN ('IM scan CUs columns accessed',                        'IM scan segments minmax eligible',                        'IM scan CUs pruned'); As you can see, only 2 IMCUs were accessed during the scan as the majority of the IMCUs (44) in the LINEORDER table were pruned out thanks to the storage index on the lo_shipmode column. In next weeks post I will describe how you can control which queries use the IM column store and which don't. +Maria Colgan

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  • 5 Android Keyboard Replacements to Help You Type Faster

    - by Chris Hoffman
    Android allows developers to replace its keyboard with their own keyboard apps. This has led to experimentation and great new features, like the gesture-typing feature that’s made its way into Android’s official keyboard after proving itself in third-party keyboards. This sort of customization isn’t possible on Apple’s iOS or even Microsoft’s modern Windows environments. Installing a third-party keyboard is easy — install it from Google Play, launch it like another app, and it will explain how to enable it. Google Keyboard Google Keyboard is Android’s official keyboard, as seen on Google’s Nexus devices. However, there’s a good chance your Android smartphone or tablet comes with a keyboard designed by its manufacturer instead. You can install the Google Keyboard from Google Play, even if your device doesn’t come with it. This keyboard offers a wide variety of features, including a built-in gesture-typing feature, as popularized by Swype. It also offers prediction, including full next-word prediction based on your previous word, and includes voice recognition that works offline on modern versions of Android. Google’s keyboard may not offer the most accurate swiping feature or the best autocorrection, but it’s a great keyboard that feels like it belongs in Android. SwiftKey SwiftKey costs $4, although you can try it free for one month. In spite of its price, many people who rarely buy apps have been sold on SwiftKey. It offers amazing auto-correction and word-prediction features. Just mash away on your touch-screen keyboard, typing as fast as possible, and SwiftKey will notice your mistakes and type what you actually meant to type. SwiftKey also now has built-in support for gesture-typing via SwiftKey Flow, so you get a lot of flexibility. At $4, SwiftKey may seem a bit pricey, but give the month-long trial a try. A great keyboard makes all the typing you do everywhere on your phone better. SwiftKey is an amazing keyboard if you tap-to-type rather than swipe-to-type. Swype While other keyboards have copied Swype’s swipe-to-type feature, none have completely matched its accuracy. Swype has been designing a gesture-typing keyboard for longer than anyone else and its gesture feature still seems more accurate than its competitors’ gesture support. If you use gesture-typing all the time, you’ll probably want to use Swype. Swype can now be installed directly from Google Play without the old, tedious process of registering a beta account and sideloading the Swype app. Swype offers a month-long free trial and the full version is available for $1 afterwards. Minuum Minuum is a crowdfunded keyboard that is currently still in beta and only supports English. We include it here because it’s so interesting — it’s a great example of the kind of creativity and experimentation that happens when you allow developers to experiment with their own forms of keyboard. Minuum uses a tiny, minimum keyboard that frees up your screen space, so your touch-screen keyboard doesn’t hog your device’s screen. Rather than displaying a full keyboard on your screen, Minuum displays a single row of letters.  Each letter is small and may be difficult to hit, but that doesn’t matter — Minuum’s smart autocorrection algorithms interpret what you intended to type rather than typing the exact letters you press. Just swipe to the right to type a space and accept Minuum’s suggestion. At $4 for a beta version with no trial, Minuum may seem a bit pricy. But it’s a great example of the flexibility Android allows. If there’s a problem with this keyboard, it’s that it’s a bit late — in an age of 5″ smartphones with 1080p screens, full-size keyboards no longer feel as cramped. MessagEase MessagEase is another example of a new take on text input. Thankfully, this keyboard is available for free. MessagEase presents all letters in a nine-button grid. To type a common letter, you’d tap the button. To type an uncommon letter, you’d tap the button, hold down, and swipe in the appropriate direction. This gives you large buttons that can work well as touch targets, especially when typing with one hand. Like any other unique twist on a traditional keyboard, you’d have to give it a few minutes to get used to where the letters are and the new way it works. After giving it some practice, you may find this is a faster way to type on a touch-screen — especially with one hand, as the targets are so large. Google Play is full of replacement keyboards for Android phones and tablets. Keyboards are just another type of app that you can swap in. Leave a comment if you’ve found another great keyboard that you prefer using. Image Credit: Cheon Fong Liew on Flickr     

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  • Checksum Transformation

    The Checksum Transformation computes a hash value, the checksum, across one or more columns, returning the result in the Checksum output column. The transformation provides functionality similar to the T-SQL CHECKSUM function, but is encapsulated within SQL Server Integration Services, for use within the pipeline without code or a SQL Server connection. As featured in The Microsoft Data Warehouse Toolkit by Joy Mundy and Warren Thornthwaite from the Kimbal Group. Have a look at the book samples especially Sample package for custom SCD handling. All input columns are passed through the transformation unaltered, those selected are used to generate the checksum which is passed out through a single output column, Checksum. This does not restrict the number of columns available downstream from the transformation, as columns will always flow through a transformation. The Checksum output column is in addition to all existing columns within the pipeline buffer. The Checksum Transformation uses an algorithm based on the .Net framework GetHashCode method, it is not consistent with the T-SQL CHECKSUM() or BINARY_CHECKSUM() functions. The transformation does not support the following Integration Services data types, DT_NTEXT, DT_IMAGE and DT_BYTES. ChecksumAlgorithm Property There ChecksumAlgorithm property is defined with an enumeration. It was first added in v1.3.0, when the FrameworkChecksum was added. All previous algorithms are still supported for backward compatibility as ChecksumAlgorithm.Original (0). Original - Orginal checksum function, with known issues around column separators and null columns. This was deprecated in the first SQL Server 2005 RTM release. FrameworkChecksum - The hash function is based on the .NET Framework GetHash method for object types. This is based on the .NET Object.GetHashCode() method, which unfortunately differs between x86 and x64 systems. For that reason we now default to the CRC32 option. CRC32 - Using a standard 32-bit cyclic redundancy check (CRC), this provides a more open implementation. The component is provided as an MSI file, however to complete the installation, you will have to add the transformation to the Visual Studio toolbox by hand. This process has been described in detail in the related FAQ entry for How do I install a task or transform component?, just select Checksum from the SSIS Data Flow Items list in the Choose Toolbox Items window. Downloads The Checksum Transformation is available for SQL Server 2005, SQL Server 2008 (includes R2) and SQL Server 2012. Please choose the version to match your SQL Server version, or you can install multiple versions and use them side by side if you have more than one version of SQL Server installed. Checksum Transformation for SQL Server 2005 Checksum Transformation for SQL Server 2008 Checksum Transformation for SQL Server 2012 Version History SQL Server 2012 Version 3.0.0.27 – SQL Server 2012 release. Includes upgrade support for both 2005 and 2008 packages to 2012. (5 Jun 2010) SQL Server 2008 Version 2.0.0.27 – Fix for CRC-32 algorithm that inadvertently made it sort dependent. Fix for race condition which sometimes lead to the error Item has already been added. Key in dictionary: '79764919' . Fix for upgrade mappings between 2005 and 2008. (19 Oct 2010) Version 2.0.0.24 - SQL Server 2008 release. Introduces the new CRC-32 algorithm, which is consistent across x86 and x64.. The default algorithm is now CRC32. (29 Oct 2008) Version 2.0.0.6 - SQL Server 2008 pre-release. This version was released by mistake as part of the site migration, and had known issues. (20 Oct 2008) SQL Server 2005 Version 1.5.0.43 – Fix for CRC-32 algorithm that inadvertently made it sort dependent. Fix for race condition which sometimes lead to the error Item has already been added. Key in dictionary: '79764919' . (19 Oct 2010) Version 1.5.0.16 - Introduces the new CRC-32 algorithm, which is consistent across x86 and x64. The default algorithm is now CRC32. (20 Oct 2008) Version 1.4.0.0 - Installer refresh only. (22 Dec 2007) Version 1.4.0.0 - Refresh for minor UI enhancements. (5 Mar 2006) Version 1.3.0.0 - SQL Server 2005 RTM. The checksum algorithm has changed to improve cardinality when calculating multiple column checksums. The original algorithm is still available for backward compatibility. Fixed custom UI bug with Output column name not persisting. (10 Nov 2005) Version 1.2.0.1 - SQL Server 2005 IDW 15 June CTP. A user interface is provided, as well as the ability to change the checksum output column name. (29 Aug 2005) Version 1.0.0 - Public Release (Beta). (30 Oct 2004) Screenshot

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  • Oracle NoSQL Database Exceeds 1 Million Mixed YCSB Ops/Sec

    - by Charles Lamb
    We ran a set of YCSB performance tests on Oracle NoSQL Database using SSD cards and Intel Xeon E5-2690 CPUs with the goal of achieving 1M mixed ops/sec on a 95% read / 5% update workload. We used the standard YCSB parameters: 13 byte keys and 1KB data size (1,102 bytes after serialization). The maximum database size was 2 billion records, or approximately 2 TB of data. We sized the shards to ensure that this was not an "in-memory" test (i.e. the data portion of the B-Trees did not fit into memory). All updates were durable and used the "simple majority" replica ack policy, effectively 'committing to the network'. All read operations used the Consistency.NONE_REQUIRED parameter allowing reads to be performed on any replica. In the past we have achieved 100K ops/sec using SSD cards on a single shard cluster (replication factor 3) so for this test we used 10 shards on 15 Storage Nodes with each SN carrying 2 Rep Nodes and each RN assigned to its own SSD card. After correcting a scaling problem in YCSB, we blew past the 1M ops/sec mark with 8 shards and proceeded to hit 1.2M ops/sec with 10 shards.  Hardware Configuration We used 15 servers, each configured with two 335 GB SSD cards. We did not have homogeneous CPUs across all 15 servers available to us so 12 of the 15 were Xeon E5-2690, 2.9 GHz, 2 sockets, 32 threads, 193 GB RAM, and the other 3 were Xeon E5-2680, 2.7 GHz, 2 sockets, 32 threads, 193 GB RAM.  There might have been some upside in having all 15 machines configured with the faster CPU, but since CPU was not the limiting factor we don't believe the improvement would be significant. The client machines were Xeon X5670, 2.93 GHz, 2 sockets, 24 threads, 96 GB RAM. Although the clients had 96 GB of RAM, neither the NoSQL Database or YCSB clients require anywhere near that amount of memory and the test could have just easily been run with much less. Networking was all 10GigE. YCSB Scaling Problem We made three modifications to the YCSB benchmark. The first was to allow the test to accommodate more than 2 billion records (effectively int's vs long's). To keep the key size constant, we changed the code to use base 32 for the user ids. The second change involved to the way we run the YCSB client in order to make the test itself horizontally scalable.The basic problem has to do with the way the YCSB test creates its Zipfian distribution of keys which is intended to model "real" loads by generating clusters of key collisions. Unfortunately, the percentage of collisions on the most contentious keys remains the same even as the number of keys in the database increases. As we scale up the load, the number of collisions on those keys increases as well, eventually exceeding the capacity of the single server used for a given key.This is not a workload that is realistic or amenable to horizontal scaling. YCSB does provide alternate key distribution algorithms so this is not a shortcoming of YCSB in general. We decided that a better model would be for the key collisions to be limited to a given YCSB client process. That way, as additional YCSB client processes (i.e. additional load) are added, they each maintain the same number of collisions they encounter themselves, but do not increase the number of collisions on a single key in the entire store. We added client processes proportionally to the number of records in the database (and therefore the number of shards). This change to the use of YCSB better models a use case where new groups of users are likely to access either just their own entries, or entries within their own subgroups, rather than all users showing the same interest in a single global collection of keys. If an application finds every user having the same likelihood of wanting to modify a single global key, that application has no real hope of getting horizontal scaling. Finally, we used read/modify/write (also known as "Compare And Set") style updates during the mixed phase. This uses versioned operations to make sure that no updates are lost. This mode of operation provides better application behavior than the way we have typically run YCSB in the past, and is only practical at scale because we eliminated the shared key collision hotspots.It is also a more realistic testing scenario. To reiterate, all updates used a simple majority replica ack policy making them durable. Scalability Results In the table below, the "KVS Size" column is the number of records with the number of shards and the replication factor. Hence, the first row indicates 400m total records in the NoSQL Database (KV Store), 2 shards, and a replication factor of 3. The "Clients" column indicates the number of YCSB client processes. "Threads" is the number of threads per process with the total number of threads. Hence, 90 threads per YCSB process for a total of 360 threads. The client processes were distributed across 10 client machines. Shards KVS Size Clients Mixed (records) Threads OverallThroughput(ops/sec) Read Latencyav/95%/99%(ms) Write Latencyav/95%/99%(ms) 2 400m(2x3) 4 90(360) 302,152 0.76/1/3 3.08/8/35 4 800m(4x3) 8 90(720) 558,569 0.79/1/4 3.82/16/45 8 1600m(8x3) 16 90(1440) 1,028,868 0.85/2/5 4.29/21/51 10 2000m(10x3) 20 90(1800) 1,244,550 0.88/2/6 4.47/23/53

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  • Objects won't render when Texture Compression + Mipmapping is Enabled

    - by felipedrl
    I'm optimizing my game and I've just implemented compressed (DXTn) texture loading in OpenGL. I've worked my way removing bugs but I can't figure out this one: objects w/ DXTn + mipmapped textures are not being rendered. It's not like they are appearing with a flat color, they just don't appear at all. DXTn textured objs render and mipmapped non-compressed textures render just fine. The texture in question is 256x256 I generate the mips all the way down 4x4, i.e 1 block. I've checked on gDebugger and it display all the levels (7) just fine. I'm using GL_LINEAR_MIPMAP_NEAREST for min filter and GL_LINEAR for mag one. The texture is being compressed and mipmaps being created offline with Paint.NET tool using super sampling method. (I also tried bilinear just in case) Source follow: [SNIPPET 1: Loading DDS into sys memory + Initializing Object] // Read header DDSHeader header; file.read(reinterpret_cast<char*>(&header), sizeof(DDSHeader)); uint pos = static_cast<uint>(file.tellg()); file.seekg(0, std::ios_base::end); uint dataSizeInBytes = static_cast<uint>(file.tellg()) - pos; file.seekg(pos, std::ios_base::beg); // Read file data mData = new unsigned char[dataSizeInBytes]; file.read(reinterpret_cast<char*>(mData), dataSizeInBytes); file.close(); mMipmapCount = header.mipmapcount; mHeight = header.height; mWidth = header.width; mCompressionType = header.pf.fourCC; // Only support files divisible by 4 (for compression blocks algorithms) massert(mWidth % 4 == 0 && mHeight % 4 == 0); massert(mCompressionType == NO_COMPRESSION || mCompressionType == COMPRESSION_DXT1 || mCompressionType == COMPRESSION_DXT3 || mCompressionType == COMPRESSION_DXT5); // Allow textures up to 65536x65536 massert(header.mipmapcount <= MAX_MIPMAP_LEVELS); mTextureFilter = TextureFilter::LINEAR; if (mMipmapCount > 0) { mMipmapFilter = MipmapFilter::NEAREST; } else { mMipmapFilter = MipmapFilter::NO_MIPMAP; } mBitsPerPixel = header.pf.bitcount; if (mCompressionType == NO_COMPRESSION) { if (header.pf.flags & DDPF_ALPHAPIXELS) { // The only format supported w/ alpha is A8R8G8B8 massert(header.pf.amask == 0xFF000000 && header.pf.rmask == 0xFF0000 && header.pf.gmask == 0xFF00 && header.pf.bmask == 0xFF); mInternalFormat = GL_RGBA8; mFormat = GL_BGRA; mDataType = GL_UNSIGNED_BYTE; } else { massert(header.pf.rmask == 0xFF0000 && header.pf.gmask == 0xFF00 && header.pf.bmask == 0xFF); mInternalFormat = GL_RGB8; mFormat = GL_BGR; mDataType = GL_UNSIGNED_BYTE; } } else { uint blockSizeInBytes = 16; switch (mCompressionType) { case COMPRESSION_DXT1: blockSizeInBytes = 8; if (header.pf.flags & DDPF_ALPHAPIXELS) { mInternalFormat = GL_COMPRESSED_RGBA_S3TC_DXT1_EXT; } else { mInternalFormat = GL_COMPRESSED_RGB_S3TC_DXT1_EXT; } break; case COMPRESSION_DXT3: mInternalFormat = GL_COMPRESSED_RGBA_S3TC_DXT3_EXT; break; case COMPRESSION_DXT5: mInternalFormat = GL_COMPRESSED_RGBA_S3TC_DXT5_EXT; break; default: // Not Supported (DXT2, DXT4 or any compression format) massert(false); } } [SNIPPET 2: Uploading into video memory] massert(mData != NULL); glGenTextures(1, &mHandle); massert(mHandle!=0); glBindTexture(GL_TEXTURE_2D, mHandle); commitFiltering(); uint offset = 0; Renderer* renderer = Renderer::getInstance(); switch (mInternalFormat) { case GL_RGB: case GL_RGBA: case GL_RGB8: case GL_RGBA8: for (uint i = 0; i < mMipmapCount + 1; ++i) { uint width = std::max(1U, mWidth >> i); uint height = std::max(1U, mHeight >> i); glTexImage2D(GL_TEXTURE_2D, i, mInternalFormat, width, height, mHasBorder, mFormat, mDataType, &mData[offset]); offset += width * height * (mBitsPerPixel / 8); } break; case GL_COMPRESSED_RGB_S3TC_DXT1_EXT: case GL_COMPRESSED_RGBA_S3TC_DXT1_EXT: case GL_COMPRESSED_RGBA_S3TC_DXT3_EXT: case GL_COMPRESSED_RGBA_S3TC_DXT5_EXT: { uint blockSize = 16; if (mInternalFormat == GL_COMPRESSED_RGB_S3TC_DXT1_EXT || mInternalFormat == GL_COMPRESSED_RGBA_S3TC_DXT1_EXT) { blockSize = 8; } uint width = mWidth; uint height = mHeight; for (uint i = 0; i < mMipmapCount + 1; ++i) { uint nBlocks = ((width + 3) / 4) * ((height + 3) / 4); // Only POT textures allowed for mipmapping massert(width % 4 == 0 && height % 4 == 0); glCompressedTexImage2D(GL_TEXTURE_2D, i, mInternalFormat, width, height, mHasBorder, nBlocks * blockSize, &mData[offset]); offset += nBlocks * blockSize; if (width <= 4 && height <= 4) { break; } width = std::max(4U, width / 2); height = std::max(4U, height / 2); } break; } default: // Not Supported massert(false); } Also I don't understand the "+3" in the block size computation but looking for a solution for my problema I've encountered people defining it as that. I guess it won't make a differente for POT textures but I put just in case. Thanks.

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  • Quadratic Programming with Oracle R Enterprise

    - by Jeff Taylor-Oracle
         I wanted to use quadprog with ORE on a server running Oracle Solaris 11.2 on a Oracle SPARC T-4 server For background, see: Oracle SPARC T4-2 http://docs.oracle.com/cd/E23075_01/ Oracle Solaris 11.2 http://www.oracle.com/technetwork/server-storage/solaris11/overview/index.html quadprog: Functions to solve Quadratic Programming Problems http://cran.r-project.org/web/packages/quadprog/index.html Oracle R Enterprise 1.4 ("ORE") 1.4 http://www.oracle.com/technetwork/database/options/advanced-analytics/r-enterprise/ore-downloads-1502823.html Problem: path to Solaris Studio doesn't match my installation: $ ORE CMD INSTALL quadprog_1.5-5.tar.gz * installing to library \u2018/u01/app/oracle/product/12.1.0/dbhome_1/R/library\u2019 * installing *source* package \u2018quadprog\u2019 ... ** package \u2018quadprog\u2019 successfully unpacked and MD5 sums checked ** libs /opt/SunProd/studio12u3/solarisstudio12.3/bin/f95 -m64   -PIC  -g  -c aind.f -o aind.o bash: /opt/SunProd/studio12u3/solarisstudio12.3/bin/f95: No such file or directory *** Error code 1 make: Fatal error: Command failed for target `aind.o' ERROR: compilation failed for package \u2018quadprog\u2019 * removing \u2018/u01/app/oracle/product/12.1.0/dbhome_1/R/library/quadprog\u2019 $ ls -l /opt/solarisstudio12.3/bin/f95 lrwxrwxrwx   1 root     root          15 Aug 19 17:36 /opt/solarisstudio12.3/bin/f95 -> ../prod/bin/f90 Solution: a symbolic link: $ sudo mkdir -p /opt/SunProd/studio12u3 $ sudo ln -s /opt/solarisstudio12.3 /opt/SunProd/studio12u3/ Now, it is all good: $ ORE CMD INSTALL quadprog_1.5-5.tar.gz * installing to library \u2018/u01/app/oracle/product/12.1.0/dbhome_1/R/library\u2019 * installing *source* package \u2018quadprog\u2019 ... ** package \u2018quadprog\u2019 successfully unpacked and MD5 sums checked ** libs /opt/SunProd/studio12u3/solarisstudio12.3/bin/f95 -m64   -PIC  -g  -c aind.f -o aind.o /opt/SunProd/studio12u3/solarisstudio12.3/bin/ cc -xc99 -m64 -I/usr/lib/64/R/include -DNDEBUG -KPIC  -xlibmieee  -c init.c -o init.o /opt/SunProd/studio12u3/solarisstudio12.3/bin/f95 -m64  -PIC -g  -c -o solve.QP.compact.o solve.QP.compact.f /opt/SunProd/studio12u3/solarisstudio12.3/bin/f95 -m64  -PIC -g  -c -o solve.QP.o solve.QP.f /opt/SunProd/studio12u3/solarisstudio12.3/bin/f95 -m64   -PIC  -g  -c util.f -o util.o /opt/SunProd/studio12u3/solarisstudio12.3/bin/ cc -xc99 -m64 -G -o quadprog.so aind.o init.o solve.QP.compact.o solve.QP.o util.o -xlic_lib=sunperf -lsunmath -lifai -lsunimath -lfai -lfai2 -lfsumai -lfprodai -lfminlai -lfmaxlai -lfminvai -lfmaxvai -lfui -lfsu -lsunmath -lmtsk -lm -lifai -lsunimath -lfai -lfai2 -lfsumai -lfprodai -lfminlai -lfmaxlai -lfminvai -lfmaxvai -lfui -lfsu -lsunmath -lmtsk -lm -L/usr/lib/64/R/lib -lR installing to /u01/app/oracle/product/12.1.0/dbhome_1/R/library/quadprog/libs ** R ** preparing package for lazy loading ** help *** installing help indices   converting help for package \u2018quadprog\u2019     finding HTML links ... done     solve.QP                                html      solve.QP.compact                        html  ** building package indices ** testing if installed package can be loaded * DONE (quadprog) ====== Here is an example from http://cran.r-project.org/web/packages/quadprog/quadprog.pdf > require(quadprog) > Dmat <- matrix(0,3,3) > diag(Dmat) <- 1 > dvec <- c(0,5,0) > Amat <- matrix(c(-4,-3,0,2,1,0,0,-2,1),3,3) > bvec <- c(-8,2,0) > solve.QP(Dmat,dvec,Amat,bvec=bvec) $solution [1] 0.4761905 1.0476190 2.0952381 $value [1] -2.380952 $unconstrained.solution [1] 0 5 0 $iterations [1] 3 0 $Lagrangian [1] 0.0000000 0.2380952 2.0952381 $iact [1] 3 2 Here, the standard example is modified to work with Oracle R Enterprise require(ORE) ore.connect("my-name", "my-sid", "my-host", "my-pass", 1521) ore.doEval(   function () {     require(quadprog)   } ) ore.doEval(   function () {     Dmat <- matrix(0,3,3)     diag(Dmat) <- 1     dvec <- c(0,5,0)     Amat <- matrix(c(-4,-3,0,2,1,0,0,-2,1),3,3)     bvec <- c(-8,2,0)    solve.QP(Dmat,dvec,Amat,bvec=bvec)   } ) $solution [1] 0.4761905 1.0476190 2.0952381 $value [1] -2.380952 $unconstrained.solution [1] 0 5 0 $iterations [1] 3 0 $Lagrangian [1] 0.0000000 0.2380952 2.0952381 $iact [1] 3 2 Now I can combine the quadprog compute algorithms with the Oracle R Enterprise Database engine functionality: Scale to large datasets Access to tables, views, and external tables in the database, as well as those accessible through database links Use SQL query parallel execution Use in-database statistical and data mining functionality

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  • [Wireless LAN]hostapd is giving error whwn running in target board

    - by Renjith G
    hi, I got the following error when i tried to run the hostapd command in my target board. Any idea about this? /etc # hostapd -dd hostapd.conf Configuration file: hostapd.conf madwifi_set_iface_flags: dev_up=0 madwifi_set_privacy: enabled=0 BSS count 1, BSSID mask ff:ff:ff:ff:ff:ff (0 bits) Flushing old station entries madwifi_sta_deauth: addr=ff:ff:ff:ff:ff:ff reason_code=3 ioctl[IEEE80211_IOCTL_SETMLME]: Invalid argument madwifi_sta_deauth: Failed to deauth STA (addr ff:ff:ff:ff:ff:ff reason 3) Could not connect to kernel driver. Deauthenticate all stations madwifi_sta_deauth: addr=ff:ff:ff:ff:ff:ff reason_code=2 ioctl[IEEE80211_IOCTL_SETMLME]: Invalid argument madwifi_sta_deauth: Failed to deauth STA (addr ff:ff:ff:ff:ff:ff reason 2) madwifi_set_privacy: enabled=0 madwifi_del_key: addr=00:00:00:00:00:00 key_idx=0 madwifi_del_key: addr=00:00:00:00:00:00 key_idx=1 madwifi_del_key: addr=00:00:00:00:00:00 key_idx=2 madwifi_del_key: addr=00:00:00:00:00:00 key_idx=3 Using interface ath0 with hwaddr 00:0b:6b:33:8c:30 and ssid '"RG_WLAN Testing Renjith G"' SSID - hexdump_ascii(len=27): 22 52 47 5f 57 4c 41 4e 20 54 65 73 74 69 6e 67 "RG_WLAN Testing 20 52 65 6e 6a 69 74 68 20 47 22 Renjith G" PSK (ASCII passphrase) - hexdump_ascii(len=12): 6d 79 70 61 73 73 70 68 72 61 73 65 mypassphrase PSK (from passphrase) - hexdump(len=32): 70 6f a6 92 da 9c a8 3b ff 36 85 76 f3 11 9c 5e 5d 4a 4b 79 f4 4e 18 f6 b1 b8 09 af 6c 9c 6c 21 madwifi_set_ieee8021x: enabled=1 madwifi_configure_wpa: group key cipher=1 madwifi_configure_wpa: pairwise key ciphers=0xa madwifi_configure_wpa: key management algorithms=0x2 madwifi_configure_wpa: rsn capabilities=0x0 madwifi_configure_wpa: enable WPA=0x1 WPA: group state machine entering state GTK_INIT (VLAN-ID 0) GMK - hexdump(len=32): [REMOVED] GTK - hexdump(len=32): [REMOVED] WPA: group state machine entering state SETKEYSDONE (VLAN-ID 0) madwifi_set_key: alg=TKIP addr=00:00:00:00:00:00 key_idx=1 madwifi_set_privacy: enabled=1 madwifi_set_iface_flags: dev_up=1 ath0: Setup of interface done. l2_packet_receive - recvfrom: Network is down Wireless event: cmd=0x8b1a len=40 Register Fail Register Fail WPA: group state machine entering state SETKEYS (VLAN-ID 0) GMK - hexdump(len=32): [REMOVED] GTK - hexdump(len=32): [REMOVED] wpa_group_setkeys: GKeyDoneStations=0 WPA: group state machine entering state SETKEYSDONE (VLAN-ID 0) madwifi_set_key: alg=TKIP addr=00:00:00:00:00:00 key_idx=2 Signal 2 received - terminating Flushing old station entries madwifi_sta_deauth: addr=ff:ff:ff:ff:ff:ff reason_code=3 ioctl[IEEE80211_IOCTL_SETMLME]: Invalid argument madwifi_sta_deauth: Failed to deauth STA (addr ff:ff:ff:ff:ff:ff reason 3) Could not connect to kernel driver. Deauthenticate all stations madwifi_sta_deauth: addr=ff:ff:ff:ff:ff:ff reason_code=2 ioctl[IEEE80211_IOCTL_SETMLME]: Invalid argument madwifi_sta_deauth: Failed to deauth STA (addr ff:ff:ff:ff:ff:ff reason 2) madwifi_set_privacy: enabled=0 madwifi_set_ieee8021x: enabled=0 madwifi_set_iface_flags: dev_up=0

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  • C++ vs Matlab vs Python as a main language for Computer Vision Postgraduate

    - by Hough
    Hi all, Firstly, sorry for a somewhat long question but I think that many people are in the same situation as me and hopefully they can also gain some benefit from this. I'll be starting my PhD very soon which involve the fields of computer vision, pattern recognition and machine learning. Currently, I'm using opencv (2.1) C++ interface and I especially like its powerful Mat class and the overloaded operations available for matrix and image seamless operations and transformations. I've also tried (and implemented many small vision projects) using opencv python interface (new bindings; opencv 2.1) and I really enjoy python's ability to integrate opencv, numpy, scipy and matplotlib. But recently, I went back to opencv C++ interface because I felt that the official python new bindings were not stable enough and no overloaded operations are available for matrices and images, not to mention the lack of machine learning modules and slow speeds in certain operations. I've also used Matlab extensively in the past and although I've used mex files and other means to speed up the program, I just felt that Matlab's performance was inadequate for real-time vision tasks, be it for fast prototyping or not. When the project becomes larger and larger, many tasks have to be re-written in C and compiled into Mex files increasingly and Matlab becomes nothing more than a glue language. Here comes the sub-questions: For postgrad studies in these fields (machine learning, vision, pattern recognition), what is your main or ideal programming language for rapid prototyping of ideas and testing algorithms contained in papers? For postgrad studies, can you list down the pros and cons of using the following languages? C++ (with opencv + gsl + svmlib + other libraries) vs Matlab (with all its toolboxes) vs python (with the imcomplete opencv bindings + numpy + scipy + matplotlib). Are there computer vision PhD/postgrad students here who are using only C++ (with all its availabe libraries including opencv) without even needing to resort to Matlab or python? In other words, given the current existing computer vision or machine learning libraries, is C++ alone sufficient for fast prototyping of ideas? If you're currently using Java or C# for your postgrad work, can you list down the reasons why they should be used and how they compare to other languages in terms of available libraries? What is the de facto vision/machine learning programming language and its associated libraries used in your university research group? Thanks in advance.

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  • How to make efficient code emerge through unit testing

    - by Jean
    Hi, I participate in a TDD Coding Dojo, where we try to practice pure TDD on simple problems. It occured to me however that the code which emerges from the unit tests isn't the most efficient. Now this is fine most of the time, but what if the code usage grows so that efficiency becomes a problem. I love the way the code emerges from unit testing, but is it possible to make the efficiency property emerge through further tests ? Here is a trivial example in ruby: prime factorization. I followed a pure TDD approach making the tests pass one after the other validating my original acceptance test (commented at the bottom). What further steps could I take, if I wanted to make one of the generic prime factorization algorithms emerge ? To reduce the problem domain, let's say I want to get a quadratic sieve implementation ... Now in this precise case I know the "optimal algorithm, but in most cases, the client will simply add a requirement that the feature runs in less than "x" time for a given environment. require 'shoulda' require 'lib/prime' class MathTest < Test::Unit::TestCase context "The math module" do should "have a method to get primes" do assert Math.respond_to? 'primes' end end context "The primes method of Math" do should "return [] for 0" do assert_equal [], Math.primes(0) end should "return [1] for 1 " do assert_equal [1], Math.primes(1) end should "return [1,2] for 2" do assert_equal [1,2], Math.primes(2) end should "return [1,3] for 3" do assert_equal [1,3], Math.primes(3) end should "return [1,2] for 4" do assert_equal [1,2,2], Math.primes(4) end should "return [1,5] for 5" do assert_equal [1,5], Math.primes(5) end should "return [1,2,3] for 6" do assert_equal [1,2,3], Math.primes(6) end should "return [1,3] for 9" do assert_equal [1,3,3], Math.primes(9) end should "return [1,2,5] for 10" do assert_equal [1,2,5], Math.primes(10) end end # context "Functionnal Acceptance test 1" do # context "the prime factors of 14101980 are 1,2,2,3,5,61,3853"do # should "return [1,2,3,5,61,3853] for ${14101980*14101980}" do # assert_equal [1,2,2,3,5,61,3853], Math.primes(14101980*14101980) # end # end # end end and the naive algorithm I created by this approach module Math def self.primes(n) if n==0 return [] else primes=[1] for i in 2..n do if n%i==0 while(n%i==0) primes<<i n=n/i end end end primes end end end

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  • Rush Hour - Solving the game

    - by Rubys
    Rush Hour if you're not familiar with it, the game consists of a collection of cars of varying sizes, set either horizontally or vertically, on a NxM grid that has a single exit. Each car can move forward/backward in the directions it's set in, as long as another car is not blocking it. You can never change the direction of a car. There is one special car, usually it's the red one. It's set in the same row that the exit is in, and the objective of the game is to find a series of moves (a move - moving a car N steps back or forward) that will allow the red car to drive out of the maze. I've been trying to think how to solve this problem computationally, and I can really not think of any good solution. I came up with a few: Backtracking. This is pretty simple - Recursion and some more recursion until you find the answer. However, each car can be moved a few different ways, and in each game state a few cars can be moved, and the resulting game tree will be HUGE. Some sort of constraint algorithm that will take into account what needs to be moved, and work recursively somehow. This is a very rough idea, but it is an idea. Graphs? Model the game states as a graph and apply some sort of variation on a coloring algorithm, to resolve dependencies? Again, this is a very rough idea. A friend suggested genetic algorithms. This is sort of possible but not easily. I can't think of a good way to make an evaluation function, and without that we've got nothing. So the question is - How to create a program that takes a grid and the vehicle layout, and outputs a series of steps needed to get the red car out? Sub-issues: Finding some solution. Finding an optimal solution (minimal number of moves) Evaluating how good a current state is Example: How can you move the cars in this setting, so that the red car can "exit" the maze through the exit on the right?

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  • C++ vs Matlab vs Python as a main language for Computer Vision Research

    - by Hough
    Hi all, Firstly, sorry for a somewhat long question but I think that many people are in the same situation as me and hopefully they can also gain some benefit from this. I'll be starting my PhD very soon which involves the fields of computer vision, pattern recognition and machine learning. Currently, I'm using opencv (2.1) C++ interface and I especially like its powerful Mat class and the overloaded operations available for matrix and image operations and seamless transformations. I've also tried (and implemented many small vision projects) using opencv python interface (new bindings; opencv 2.1) and I really enjoy python's ability to integrate opencv, numpy, scipy and matplotlib. But recently, I went back to opencv C++ interface because I felt that the official python new bindings were not stable enough and no overloaded operations are available for matrices and images, not to mention the lack of machine learning modules and slow speeds in certain operations. I've also used Matlab extensively in the past and although I've used mex files and other means to speed up the program, I just felt that Matlab's performance was inadequate for real-time vision tasks, be it for fast prototyping or not. When the project becomes larger and larger, many tasks have to be re-written in C and compiled into Mex files increasingly and Matlab becomes nothing more than a glue language. Here comes the sub-questions: For carrying out research in these fields (machine learning, vision, pattern recognition), what is your main or ideal programming language for rapid prototyping of ideas and testing algorithms contained in papers? For computer vision research work, can you list down the pros and cons of using the following languages? C++ (with opencv + gsl + svmlib + other libraries) vs Matlab (with all its toolboxes) vs python (with the imcomplete opencv bindings + numpy + scipy + matplotlib). Are there computer vision PhD/postgrad students here who are using only C++ (with all its availabe libraries including opencv) without even needing to resort to Matlab or python? In other words, given the current existing computer vision or machine learning libraries, is C++ alone sufficient for fast prototyping of ideas? If you're currently using Java or C# for your research, can you list down the reasons why they should be used and how they compare to other languages in terms of available libraries? What is the de facto vision/machine learning programming language and its associated libraries used in your research group? Thanks in advance. Edit: As suggested, I've opened the question to both academic and non-academic computer vision/machine learning/pattern recognition researchers and groups.

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  • Is it normal for a programmer with 2 years experience to take a long time to code simple programs?

    - by ajax81
    Hi all, I'm a relatively new programmer (18 months on the scene), and I'm finally getting to the point where I'm comfortable accepting projects and developing solutions under minimal supervision. Unfortunately, this also means that I've become acutely aware of my performance shortfalls, the most prevalent of which is the amount of time it takes me to develop, test, and submit algorithms for review. A great example of what I'm talking about occurred this week when I was tasked with developing a simple XML web service (asp.net 3.5) callable via client-side JavaScript, that accepts a single parameter and returns a dataset output to a modal window (please note this is the first time I've had to develop a web service and have had ZERO experience creating/consuming them...let alone calling them from JS client side). Keeping a long story short -- I worked on it for 4 days straight, all day each day, for a grand total of 36 hours, not including the time I spent dwelling on the problem in the shower, the morning commute, and laying awake in bed at night. I learned a great deal about web services and xml/json/javascript...but was called in for a management review to discuss the length of time it took me to develop the solution. In the meeting, I was praised for the quality of my work and was in fact told that my effort was commendable. However, they (senior leads and pm's) weren't impressed with the amount of time it took me to develop the solution and expressed that they would have liked to see the solution in roughly 1/3 of the time it took me. I guess what concerns me the most is that I've identified this pattern as common for myself. Between online videos, book research, and trial/error coding...if its something I haven't seen before, I can spend up to two weeks on a problem that seems to only take the pros in the videos moments to code up. And of course, knowing that management isn't happy with this pattern has shaken me up a bit. To sum up, I have some very specific questions I'd like to ask, and would greatly appreciate your objective professional feedback. Is my experience as a junior programmer common among new developers? Or is it possible that I'm just not cut out for the work? If you suspect that my experience is not common and that there may be an aptitude issue, do you have any suggestions/solutions that I could propose to management to help bring me up to speed? Do seasoned, professional programmers ever encounter knowledge barriers that considerably delay deliverables? When you started out in the industry, did you know how to "do it all"? If not, how long did it take you to be perceived as "proficient"? Was it a natural progression of trial and error, or was there a particular zen moment when you knew you had achieved super saiyen power level? Anyways, thanks for taking the time to read my question(s). I don't know if this is the right place to ask for professional career guidance, but I greatly appreciate your willingness to help me out. Cheers, Daniel

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  • Most efficient method to query a Young Tableau

    - by Matthieu M.
    A Young Tableau is a 2D matrix A of dimensions M*N such that: i,j in [0,M)x[0,N): for each p in (i,M), A[i,j] <= A[p,j] for each q in (j,N), A[i,j] <= A[i,q] That is, it's sorted row-wise and column-wise. Since it may contain less than M*N numbers, the bottom-right values might be represented either as missing or using (in algorithm theory) infinity to denote their absence. Now the (elementary) question: how to check if a given number is contained in the Young Tableau ? Well, it's trivial to produce an algorithm in O(M*N) time of course, but what's interesting is that it is very easy to provide an algorithm in O(M+N) time: Bottom-Left search: Let x be the number we look for, initialize i,j as M-1, 0 (bottom left corner) If x == A[i,j], return true If x < A[i,j], then if i is 0, return false else decrement i and go to 2. Else, if j is N-1, return false else increment j This algorithm does not make more than M+N moves. The correctness is left as an exercise. It is possible though to obtain a better asymptotic runtime. Pivot Search: Let x be the number we look for, initialize i,j as floor(M/2), floor(N/2) If x == A[i,j], return true If x < A[i,j], search (recursively) in A[0:i-1, 0:j-1], A[i:M-1, 0:j-1] and A[0:i-1, j:N-1] Else search (recursively) in A[i+1:M-1, 0:j], A[i+1:M-1, j+1:N-1] and A[0:i, j+1:N-1] This algorithm proceed by discarding one of the 4 quadrants at each iteration and running recursively on the 3 left (divide and conquer), the master theorem yields a complexity of O((N+M)**(log 3 / log 4)) which is better asymptotically. However, this is only a big-O estimation... So, here are the questions: Do you know (or can think of) an algorithm with a better asymptotical runtime ? Like introsort prove, sometimes it's worth switching algorithms depending on the input size or input topology... do you think it would be possible here ? For 2., I am notably thinking that for small size inputs, the bottom-left search should be faster because of its O(1) space requirement / lower constant term.

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  • Calculating CPU frequency in C with RDTSC always returns 0

    - by Nazgulled
    Hi, The following piece of code was given to us from our instructor so we could measure some algorithms performance: #include <stdio.h> #include <unistd.h> static unsigned cyc_hi = 0, cyc_lo = 0; static void access_counter(unsigned *hi, unsigned *lo) { asm("rdtsc; movl %%edx,%0; movl %%eax,%1" : "=r" (*hi), "=r" (*lo) : /* No input */ : "%edx", "%eax"); } void start_counter() { access_counter(&cyc_hi, &cyc_lo); } double get_counter() { unsigned ncyc_hi, ncyc_lo, hi, lo, borrow; double result; access_counter(&ncyc_hi, &ncyc_lo); lo = ncyc_lo - cyc_lo; borrow = lo > ncyc_lo; hi = ncyc_hi - cyc_hi - borrow; result = (double) hi * (1 << 30) * 4 + lo; return result; } However, I need this code to be portable to machines with different CPU frequencies. For that, I'm trying to calculate the CPU frequency of the machine where the code is being run like this: int main(void) { double c1, c2; start_counter(); c1 = get_counter(); sleep(1); c2 = get_counter(); printf("CPU Frequency: %.1f MHz\n", (c2-c1)/1E6); printf("CPU Frequency: %.1f GHz\n", (c2-c1)/1E9); return 0; } The problem is that the result is always 0 and I can't understand why. I'm running Linux (Arch) as guest on VMware. On a friend's machine (MacBook) it is working to some extent; I mean, the result is bigger than 0 but it's variable because the CPU frequency is not fixed (we tried to fix it but for some reason we are not able to do it). He has a different machine which is running Linux (Ubuntu) as host and it also reports 0. This rules out the problem being on the virtual machine, which I thought it was the issue at first. Any ideas why this is happening and how can I fix it?

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  • Constraint Satisfaction Problem

    - by Carl Smotricz
    I'm struggling my way through Artificial Intelligence: A Modern Approach in order to alleviate my natural stupidity. In trying to solve some of the exercises, I've come up against the "Who Owns the Zebra" problem, Exercise 5.13 in Chapter 5. This has been a topic here on SO but the responses mostly addressed the question "how would you solve this if you had a free choice of problem solving software available?" I accept that Prolog is a very appropriate programming language for this kind of problem, and there are some fine packages available, e.g. in Python as shown by the top-ranked answer and also standalone. Alas, none of this is helping me "tough it out" in a way as outlined by the book. The book appears to suggest building a set of dual or perhaps global constraints, and then implementing some of the algorithms mentioned to find a solution. I'm having a lot of trouble coming up with a set of constraints suitable for modelling the problem. I'm studying this on my own so I don't have access to a professor or TA to get me over the hump - this is where I'm asking for your help. I see little similarity to the examples in the chapter. I was eager to build dual constraints and started out by creating (the logical equivalent of) 25 variables: nationality1, nationality2, nationality3, ... nationality5, pet1, pet2, pet3, ... pet5, drink1 ... drink5 and so on, where the number was indicative of the house's position. This is fine for building the unary constraints, e.g. The Norwegian lives in the first house: nationality1 = { :norway }. But most of the constraints are a combination of two such variables through a common house number, e.g. The Swede has a dog: nationality[n] = { :sweden } AND pet[n] = { :dog } where n can range from 1 to 5, obviously. Or stated another way: nationality1 = { :sweden } AND pet1 = { :dog } XOR nationality2 = { :sweden } AND pet2 = { :dog } XOR nationality3 = { :sweden } AND pet3 = { :dog } XOR nationality4 = { :sweden } AND pet4 = { :dog } XOR nationality5 = { :sweden } AND pet5 = { :dog } ...which has a decidedly different feel to it than the "list of tuples" advocated by the book: ( X1, X2, X3 = { val1, val2, val3 }, { val4, val5, val6 }, ... ) I'm not looking for a solution per se; I'm looking for a start on how to model this problem in a way that's compatible with the book's approach. Any help appreciated.

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  • Permutations of Varying Size

    - by waiwai933
    I'm trying to write a function in PHP that gets all permutations of all possible sizes. I think an example would be the best way to start off: $my_array = array(1,1,2,3); Possible permutations of varying size: 1 1 // * See Note 2 3 1,1 1,2 1,3 // And so forth, for all the sets of size 2 1,1,2 1,1,3 1,2,1 // And so forth, for all the sets of size 3 1,1,2,3 1,1,3,2 // And so forth, for all the sets of size 4 Note: I don't care if there's a duplicate or not. For the purposes of this example, all future duplicates have been omitted. What I have so far in PHP: function getPermutations($my_array){ $permutation_length = 1; $keep_going = true; while($keep_going){ while($there_are_still_permutations_with_this_length){ // Generate the next permutation and return it into an array // Of course, the actual important part of the code is what I'm having trouble with. } $permutation_length++; if($permutation_length>count($my_array)){ $keep_going = false; } else{ $keep_going = true; } } return $return_array; } The closest thing I can think of is shuffling the array, picking the first n elements, seeing if it's already in the results array, and if it's not, add it in, and then stop when there are mathematically no more possible permutations for that length. But it's ugly and resource-inefficient. Any pseudocode algorithms would be greatly appreciated. Also, for super-duper (worthless) bonus points, is there a way to get just 1 permutation with the function but make it so that it doesn't have to recalculate all previous permutations to get the next? For example, I pass it a parameter 3, which means it's already done 3 permutations, and it just generates number 4 without redoing the previous 3? (Passing it the parameter is not necessary, it could keep track in a global or static). The reason I ask this is because as the array grows, so does the number of possible combinations. Suffice it to say that one small data set with only a dozen elements grows quickly into the trillions of possible combinations and I don't want to task PHP with holding trillions of permutations in its memory at once.

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  • Algorithm to Render a Horizontal Binary-ish Tree in Text/ASCII form

    - by Justin L.
    It's a pretty normal binary tree, except for the fact that one of the nodes may be empty. I'd like to find a way to output it in a horizontal way (that is, the root node is on the left and expands to the right). I've had some experience expanding trees vertically (root node at the top, expanding downwards), but I'm not sure where to start, in this case. Preferably, it would follow these couple of rules: If a node has only one child, it can be skipped as redundant (an "end node", with no children, is always displayed) All nodes of the same depth must be aligned vertically; all nodes must be to the right of all less-deep nodes and to the left of all deeper nodes. Nodes have a string representation which includes their depth. Each "end node" has its own unique line; that is, the number of lines is the number of end nodes in the tree, and when an end node is on a line, there may be nothing else on that line after that end node. As a consequence of the last rule, the root node should be in either the top left or the bottom left corner; top left is preferred. For example, this is a valid tree, with six end nodes (node is represented by a name, and its depth): [a0]------------[b3]------[c5]------[d8] \ \ \----------[e9] \ \----[f5] \--[g1]--------[h4]------[i6] \ \--------------------[j10] \-[k3] Which represents the horizontal, explicit binary tree: 0 a / \ 1 g * / \ \ 2 * * * / \ \ 3 k * b / / \ 4 h * * / \ \ \ 5 * * f c / \ / \ 6 * i * * / / \ 7 * * * / / \ 8 * * d / / 9 * e / 10 j (branches folded for compactness; * representing redundant, one-child nodes; note that *'s are actual nodes, storing one child each, just with names omitted here for presentation sake) (also, to clarify, I'd like to generate the first, horizontal tree; not this vertical tree) I say language-agnostic because I'm just looking for an algorithm; I say ruby because I'm eventually going to have to implement it in ruby anyway. Assume that each Node data structure stores only its id, a left node, and a right node. A master Tree class keeps tracks of all nodes and has adequate algorithms to find: A node's nth ancestor A node's nth descendant The generation of a node The lowest common ancestor of two given nodes Anyone have any ideas of where I could start? Should I go for the recursive approach? Iterative?

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  • Classifying captured data in unknown format?

    - by monch1962
    I've got a large set of captured data (potentially hundreds of thousands of records), and I need to be able to break it down so I can both classify it and also produce "typical" data myself. Let me explain further... If I have the following strings of data: 132T339G1P112S 164T897F5A498S 144T989B9B223T 155T928X9Z554T ... you might start to infer the following: possibly all strings are 14 characters long the 4th, 8th, 10th and 14th characters may always be alphas, while the rest are numeric the first character may always be a '1' the 4th character may always be the letter 'T' the 14th character may be limited to only being 'S' or 'T' and so on... As you get more and more samples of real data, some of these "rules" might disappear; if you see a 15 character long string, then you have evidence that the 1st "rule" is incorrect. However, given a sufficiently large sample of strings that are exactly 14 characters long, you can start to assume that "all strings are 14 characters long" and assign a numeric figure to your degree of confidence (with an appropriate set of assumptions around the fact that you're seeing a suitably random set of all possible captured data). As you can probably tell, a human can do a lot of this classification by eye, but I'm not aware of libraries or algorithms that would allow a computer to do it. Given a set of captured data (significantly more complex than the above...), are there libraries that I can apply in my code to do this sort of classification for me, that will identify "rules" with a given degree of confidence? As a next step, I need to be able to take those rules, and use them to create my own data that conforms to these rules. I assume this is a significantly easier step than the classification, but I've never had to perform a task like this before so I'm really not sure how complex it is. At a guess, Python or Java (or possibly Perl or R) are possibly the "common" languages most likely to have these sorts of libraries, and maybe some of the bioinformatic libraries do this sort of thing. I really don't care which language I have to use; I need to solve the problem in whatever way I can. Any sort of pointer to information would be very useful. As you can probably tell, I'm struggling to describe this problem clearly, and there may be a set of appropriate keywords I can plug into Google that will point me towards the solution. Thanks in advance

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  • Why is it still so hard to write software?

    - by nornagon
    Writing software, I find, is composed of two parts: the Idea, and the Implementation. The Idea is about thinking: "I have this problem; how do I solve it?" and further, "how do I solve it elegantly?" The answers to these questions are obtainable by thinking about algorithms and architecture. The ideas come partially through analysis and partially through insight and intuition. The Idea is usually the easy part. You talk to your friends and co-workers and you nut it out in a meeting or over coffee. It takes an hour or two, plus revisions as you implement and find new problems. The Implementation phase of software development is so difficult that we joke about it. "Oh," we say, "the rest is a Simple Matter of Code." Because it should be simple, but it never is. We used to write our code on punch cards, and that was hard: mistakes were very difficult to spot, so we had to spend extra effort making sure every line was perfect. Then we had serial terminals: we could see all our code at once, search through it, organise it hierarchically and create things abstracted from raw machine code. First we had assemblers, one level up from machine code. Mnemonics freed us from remembering the machine code. Then we had compilers, which freed us from remembering the instructions. We had virtual machines, which let us step away from machine-specific details. And now we have advanced tools like Eclipse and Xcode that perform analysis on our code to help us write code faster and avoid common pitfalls. But writing code is still hard. Writing code is about understanding large, complex systems, and tools we have today simply don't go very far to help us with that. When I click "find all references" in Eclipse, I get a list of them at the bottom of the window. I click on one, and I'm torn away from what I was looking at, forced to context switch. Java architecture is usually several levels deep, so I have to switch and switch and switch until I find what I'm really looking for -- by which time I've forgotten where I came from. And I do that all day until I've understood a system. It's taxing mentally, and Eclipse doesn't do much that couldn't be done in 1985 with grep, except eat hundreds of megs of RAM. Writing code has barely changed since we were staring at amber on black. We have the theoretical groundwork for much more advanced tools, tools that actually work to help us comprehend and extend the complex systems we work with every day. So why is writing code still so hard?

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  • Recursive N-way merge/diff algorithm for directory trees?

    - by BobMcGee
    What algorithms or Java libraries are available to do N-way, recursive diff/merge of directories? I need to be able to generate a list of folder trees that have many identical files, and have subdirectories with many similar files. I want to be able to use 2-way merge operations to quickly remove as much redundancy as possible. Goals: Find pairs of directories that have many similar files between them. Generate short list of directory pairs that can be synchronized with 2-way merge to eliminate duplicates Should operate recursively (there may be nested duplicates of higher-level directories) Run time and storage should be O(n log n) in numbers of directories and files Should be able to use an embedded DB or page to disk for processing more files than fit in memory (100,000+). Optional: generate an ancestry and change-set between folders Optional: sort the merge operations by how many duplicates they can elliminate I know how to use hashes to find duplicate files in roughly O(n) space, but I'm at a loss for how to go from this to finding partially overlapping sets between folders and their children. EDIT: some clarification The tricky part is the difference between "exact same" contents (otherwise hashing file hashes would work) and "similar" (which will not). Basically, I want to feed this algorithm at a set of directories and have it return a set of 2-way merge operations I can perform in order to reduce duplicates as much as possible with as few conflicts possible. It's effectively constructing an ancestry tree showing which folders are derived from each other. The end goal is to let me incorporate a bunch of different folders into one common tree. For example, I may have a folder holding programming projects, and then copy some of its contents to another computer to work on it. Then I might back up and intermediate version to flash drive. Except I may have 8 or 10 different versions, with slightly different organizational structures or folder names. I need to be able to merge them one step at a time, so I can chose how to incorporate changes at each step of the way. This is actually more or less what I intend to do with my utility (bring together a bunch of scattered backups from different points in time). I figure if I can do it right I may as well release it as a small open source util. I think the same tricks might be useful for comparing XML trees though.

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  • Combinations and Permutations in F#

    - by Noldorin
    I've recently written the following combinations and permutations functions for an F# project, but I'm quite aware they're far from optimised. /// Rotates a list by one place forward. let rotate lst = List.tail lst @ [List.head lst] /// Gets all rotations of a list. let getRotations lst = let rec getAll lst i = if i = 0 then [] else lst :: (getAll (rotate lst) (i - 1)) getAll lst (List.length lst) /// Gets all permutations (without repetition) of specified length from a list. let rec getPerms n lst = match n, lst with | 0, _ -> seq [[]] | _, [] -> seq [] | k, _ -> lst |> getRotations |> Seq.collect (fun r -> Seq.map ((@) [List.head r]) (getPerms (k - 1) (List.tail r))) /// Gets all permutations (with repetition) of specified length from a list. let rec getPermsWithRep n lst = match n, lst with | 0, _ -> seq [[]] | _, [] -> seq [] | k, _ -> lst |> Seq.collect (fun x -> Seq.map ((@) [x]) (getPermsWithRep (k - 1) lst)) // equivalent: | k, _ -> lst |> getRotations |> Seq.collect (fun r -> List.map ((@) [List.head r]) (getPermsWithRep (k - 1) r)) /// Gets all combinations (without repetition) of specified length from a list. let rec getCombs n lst = match n, lst with | 0, _ -> seq [[]] | _, [] -> seq [] | k, (x :: xs) -> Seq.append (Seq.map ((@) [x]) (getCombs (k - 1) xs)) (getCombs k xs) /// Gets all combinations (with repetition) of specified length from a list. let rec getCombsWithRep n lst = match n, lst with | 0, _ -> seq [[]] | _, [] -> seq [] | k, (x :: xs) -> Seq.append (Seq.map ((@) [x]) (getCombsWithRep (k - 1) lst)) (getCombsWithRep k xs) Does anyone have any suggestions for how these functions (algorithms) can be sped up? I'm particularly interested in how the permutation (with and without repetition) ones can be improved. The business involving rotations of lists doesn't look too efficient to me in retrospect. Update Here's my new implementation for the getPerms function, inspired by Tomas's answer. Unfortunately, it's not really any fast than the existing one. Suggestions? let getPerms n lst = let rec getPermsImpl acc n lst = seq { match n, lst with | k, x :: xs -> if k > 0 then for r in getRotations lst do yield! getPermsImpl (List.head r :: acc) (k - 1) (List.tail r) if k >= 0 then yield! getPermsImpl acc k [] | 0, [] -> yield acc | _, [] -> () } getPermsImpl List.empty n lst

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  • Are there programs that iteratively write new programs?

    - by chris
    For about a year I have been thinking about writing a program that writes programs. This would primarily be a playful exercise that might teach me some new concepts. My inspiration came from negentropy and the ability for order to emerge from chaos and new chaos to arise out of order in infinite succession. To be more specific, the program would start by writing a short random string. If the string compiles the programs will log it for later comparison. If the string does not compile the program will try to rewrite it until it does compile. As more strings (mini 'useless' programs) are logged they can be parsed for similarities and used to generate a grammar. This grammar can then be drawn on to write more strings that have a higher probability of compilation than purely random strings. This is obviously more than a little silly, but I thought it would be fun to try and grow a program like this. And as a byproduct I get a bunch of unique programs that I can visualize and call art. I'll probably write this in Ruby due to its simple syntax and dynamic compilation and then I will visualize in processing using ruby-processing. What I would like to know is: Is there a name for this type of programming? What currently exists in this field? Who are the primary contributors? BONUS! - In what ways can I procedurally assign value to output programs beyond compiles(y/n)? I may want to extend the functionality of this program to generate a program based on parameters, but I want the program to define those parameters through running the programs that compile and assigning meaning to the programs output. This question is probably more involved than reasonable for a bonus, but if you can think of a simple way to get something like this done in less than 23 lines or one hyperlink, please toss it into your response. I know that this is not quite meta-programming and from the little I know of AI and generative algorithms they are usually more goal oriented than what I am thinking. What would be optimal is a program that continually rewrites and improves itself so I don't have to ^_^

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  • Checking if an int is prime more efficiently

    - by SipSop
    I recently was part of a small java programming competition at my school. My partner and I have just finished our first pure oop class and most of the questions were out of our league so we settled on this one (and I am paraphrasing somewhat): "given an input integer n return the next int that is prime and its reverse is also prime for example if n = 18 your program should print 31" because 31 and 13 are both prime. Your .class file would then have a test case of all the possible numbers from 1-2,000,000,000 passed to it and it had to return the correct answer within 10 seconds to be considered valid. We found a solution but with larger test cases it would take longer than 10 seconds. I am fairly certain there is a way to move the range of looping from n,..2,000,000,000 down as the likely hood of needing to loop that far when n is a low number is small, but either way we broke the loop when a number is prime under both conditions is found. At first we were looping from 2,..n no matter how large it was then i remembered the rule about only looping to the square root of n. Any suggestions on how to make my program more efficient? I have had no classes dealing with complexity analysis of algorithms. Here is our attempt. public class P3 { public static void main(String[] args){ long loop = 2000000000; long n = Integer.parseInt(args[0]); for(long i = n; i<loop; i++) { String s = i +""; String r = ""; for(int j = s.length()-1; j>=0; j--) r = r + s.charAt(j); if(prime(i) && prime(Long.parseLong(r))) { System.out.println(i); break; } } System.out.println("#"); } public static boolean prime(long p){ for(int i = 2; i<(int)Math.sqrt(p); i++) { if(p%i==0) return false; } return true; } } ps sorry if i did the formatting for code wrong this is my first time posting here. Also the output had to have a '#' after each line thats what the line after the loop is about Thanks for any help you guys offer!!!

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  • Special scheduling Algorithm (pattern expansion)

    - by tovare
    Question Do you think genetic algorithms worth trying out for the problem below, or will I hit local-minima issues? I think maybe aspects of the problem is great for a generator / fitness-function style setup. (If you've botched a similar project I would love hear from you, and not do something similar) Thank you for any tips on how to structure things and nail this right. The problem I'm searching a good scheduling algorithm to use for the following real-world problem. I have a sequence with 15 slots like this (The digits may vary from 0 to 20) : 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 (And there are in total 10 different sequences of this type) Each sequence needs to expand into an array, where each slot can take 1 position. 1 1 0 0 1 1 1 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 0 0 0 1 1 1 0 0 0 0 1 1 0 0 0 1 1 1 0 0 0 1 1 0 0 1 1 0 0 0 1 1 1 0 0 0 1 1 The constraints on the matrix is that: [row-wise, i.e. horizontally] The number of ones placed, must either be 11 or 111 [row-wise] The distance between two sequences of 1 needs to be a minimum of 00 The sum of each column should match the original array. The number of rows in the matrix should be optimized. The array then needs to allocate one of 4 different matrixes, which may have different number of rows: A, B, C, D A, B, C and D are real-world departments. The load needs to be placed reasonably fair during the course of a 10-day period, not to interfere with other department goals. Each of the matrix is compared with expansion of 10 different original sequences so you have: A1, A2, A3, A4, A5, A6, A7, A8, A9, A10 B1, B2, B3, B4, B5, B6, B7, B8, B9, B10 C1, C2, C3, C4, C5, C6, C7, C8, C9, C10 D1, D2, D3, D4, D5, D6, D7, D8, D9, D10 Certain spots on these may be reserved (Not sure if I should make it just reserved/not reserved or function-based). The reserved spots might be meetings and other events The sum of each row (for instance all the A's) should be approximately the same within 2%. i.e. sum(A1 through A10) should be approximately the same as (B1 through B10) etc. The number of rows can vary, so you have for instance: A1: 5 rows A2: 5 rows A3: 1 row, where that single row could for instance be: 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 etc.. Sub problem* I'de be very happy to solve only part of the problem. For instance being able to input: 1 1 2 3 4 2 2 3 4 2 2 3 3 2 3 And get an appropriate array of sequences with 1's and 0's minimized on the number of rows following th constraints above.

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