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  • C++ STL: Array vs Vector: Raw element accessing performance

    - by oh boy
    I'm building an interpreter and as I'm aiming for raw speed this time, every clock cycle matters for me in this (raw) case. Do you have any experience or information what of the both is faster: Vector or Array? All what matters is the speed I can access an element (opcode receiving), I don't care about inserting, allocation, sorting, etc. I'm going to lean myself out of the window now and say: Arrays are at least a bit faster than vectors in terms of accessing an element i. It seems really logical for me. With vectors you have all those security and controlling overhead which doesn't exist for arrays. (Why) Am I wrong? No, I can't ignore the performance difference - even if it is so small - I have already optimized and minimized every other part of the VM which executes the opcodes :)

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  • C++ Questions about vectors

    - by xbonez
    Hey guys, I have a CS exam tomorrow. Just want to get a few questions cleared up. Thanks a lot, and I really appreciate the help. Que 1. What are parallel vectors? Vectors of the same length that contain data that is meant to be processed together Vectors that are all of the same data type Vectors that are of the same length Any vector of data type parallel Que 2. Arrays are faster and more efficient than vectors. True False Que 3. Arrays can be a return type of a function call. True False Que 4. Vectors can be a return type of a function call. True False

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  • [perl] where can i find an array of the Unicode code points for a particular block?

    - by gitparade
    At the moment, I'm writing these arrays by hand. For example, the Miscellaneous Mathematical Symbols-A block has an entry in hash like this: my %symbols = ( ... miscellaneous_mathematical_symbols_a => [(0x27C0..0x27CA), 0x27CC, (0x27D0..0x27EF)], ... ) The simpler, 'continuous' array miscellaneous_mathematical_symbols_a => [0x27C0..0x27EF] doesn't work because Unicode blocks have holes in them. For example, there's nothing at 0x27CB. Take a look at the code chart [PDF]. Writing these arrays by hand is tedious, error-prone and a bit fun. And I get the feeling that someone has already tackled this in Perl!

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  • What Happens if i create a byte array continuously in a while loop with different size and add read an stream into it?

    - by SajidKhan
    I want to read an audio file into multiple byte arrays , with different size . And then add into a shared memory. What will happen if use below code. Does the byte array gets over written. I understand it will creat multiple byte array , how do i erase those byte arrays after my code does what it needs to do. int TotalBuffer = 10; while (TotalBuffer !=0){ bufferData = new byte[AClipTextFileHandler.BufferSize.get(j)]; input.read(bufferData); Sharedbuffer.put(bufferData); i++; j++; TotalBuffer--; }

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  • Java: How to store Vector<String[]> in XML (or save in any other way)

    - by hatboysam
    Basically I have a proof-of-concept application that is a digital recipe book. Each Recipe is an object and each object has, among other fields, a Vector containing arrays. The Vector is the list of all ingredients in the Recipe while each ingredient has an array showing the name of the ingredient, the amount, and the unit for that amount. I want to save each Recipe to XML so that they can be accessed by the user. How can I store a Vector of String arrays in XML or any other sort of file so that it can later be recalled and accessed?

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  • Where can I find an array of the Unicode code points for a particular block?

    - by gitparade
    At the moment, I'm writing these arrays by hand. For example, the Miscellaneous Mathematical Symbols-A block has an entry in hash like this: my %symbols = ( ... miscellaneous_mathematical_symbols_a => [(0x27C0..0x27CA), 0x27CC, (0x27D0..0x27EF)], ... ) The simpler, 'continuous' array miscellaneous_mathematical_symbols_a => [0x27C0..0x27EF] doesn't work because Unicode blocks have holes in them. For example, there's nothing at 0x27CB. Take a look at the code chart [PDF]. Writing these arrays by hand is tedious, error-prone and a bit fun. And I get the feeling that someone has already tackled this in Perl!

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  • python array.array with strings as data type

    - by Gladius
    Is there an object that acts like array.array, yet can handle strings (or character arrays) as its data type? It should be able to convert the string array to binary and back again, preferably with null terminated strings, however fixed length strings would be acceptable. >>> my_array = stringarray(['foo', 'bar']) >>> my_array.tostring() 'foo\0bar\0' >>> re_read = stringarray('foo\0bar\0') >>> re_read[:] ['foo', 'bar'] I will be using it with arrays that contain a couple million strings.

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  • Generating a perfectly distributed grid from array

    - by zath
    I'm looking for a formula or rule that will allow me to distribute n characters into a n*n grid with as perfect of a distribution as possible. Let's say we have an array of 5 characters, A through E. Here's an example of how it definitely shouldn't turn out: A B C D E B C D E A C D E A B D E A B C E A B C D The pattern is very clear here, it doesn't look "random". It would look better this way: A B C D E D E A B C B C D E A E A B C D C D E A B What I basically did here was place the A B C D E on the first row, then shift it by 2 on the second row, by 4 on the third row, 1 on the fourth row and 3 on the fifth row. Compared to the very bad example, this one shows no clear pattern. Though I'm certainly hoping there is a pattern, so I can use it to calculate not only small arrays such as this one, but arrays of any size. Any ideas as to how this can be accomplished?

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  • Is object clearing/array deallocation really necessary in VB6/VBA (Pros/Cons?)

    - by Oorang
    Hello, A lot of what I have learned about VB I learned from using Static Code Analysis (Particularly Aivosto's Project Analyzer). And one one of things it checks for is whether or not you cleared all objects and arrays. I used to just do this blindly because PA said so. But now that I know a little bit more about the way VB releases resources, it seems to me that these things should be happening automatically. Is this a legacy feature from pre VB6, or is there a reason why you should explicitly set objects back to nothing and use Erase on arrays?

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  • How to get rid of a stubborn 'removed' device in mdadm

    - by T.J. Crowder
    One of my server's drives failed and so I removed the failed drive from all three relevant arrays, had the drive swapped out, and then added the new drive to the arrays. Two of the arrays worked perfectly. The third added the drive back as a spare, and there's an odd "removed" entry in the mdadm details. I tried both mdadm /dev/md2 --remove failed and mdadm /dev/md2 --remove detached as suggested here and here, neither of which complained, but neither of which had any effect, either. Does anyone know how I can get rid of that entry and get the drive added back properly? (Ideally without resyncing a third time, I've already had to do it twice and it takes hours. But if that's what it takes, that's what it takes.) The new drive is /dev/sda, the relevant partition is /dev/sda3. Here's the detail on the array: # mdadm --detail /dev/md2 /dev/md2: Version : 0.90 Creation Time : Wed Oct 26 12:27:49 2011 Raid Level : raid1 Array Size : 729952192 (696.14 GiB 747.47 GB) Used Dev Size : 729952192 (696.14 GiB 747.47 GB) Raid Devices : 2 Total Devices : 2 Preferred Minor : 2 Persistence : Superblock is persistent Update Time : Tue Nov 12 17:48:53 2013 State : clean, degraded Active Devices : 1 Working Devices : 2 Failed Devices : 0 Spare Devices : 1 UUID : 2fdbf68c:d572d905:776c2c25:004bd7b2 (local to host blah) Events : 0.34665 Number Major Minor RaidDevice State 0 0 0 0 removed 1 8 19 1 active sync /dev/sdb3 2 8 3 - spare /dev/sda3 If it's relevant, it's a 64-bit server. It normally runs Ubuntu, but right now I'm in the data centre's "rescue" OS, which is Debian 7 (wheezy). The "removed" entry was there the last time I was in Ubuntu (it won't, currently, boot from the disk), so I don't think that's not some Ubuntu/Debian conflict (and they are, of course, closely related). Update: Having done extensive tests with test devices on a local machine, I'm just plain getting anomalous behavior from mdadm with this array. For instance, with /dev/sda3 removed from the array again, I did this: mdadm /dev/md2 --grow --force --raid-devices=1 And that got rid of the "removed" device, leaving me just with /dev/sdb3. Then I nuked /dev/sda3 (wrote a file system to it, so it didn't have the raid fs anymore), then: mdadm /dev/md2 --grow --raid-devices=2 ...which gave me an array with /dev/sdb3 in slot 0 and "removed" in slot 1 as you'd expect. Then mdadm /dev/md2 --add /dev/sda3 ...added it — as a spare again. (Another 3.5 hours down the drain.) So with the rebuilt spare in the array, given that mdadm's man page says RAID-DEVICES CHANGES ... When the number of devices is increased, any hot spares that are present will be activated immediately. ...I grew the array to three devices, to try to activate the "spare": mdadm /dev/md2 --grow --raid-devices=3 What did I get? Two "removed" devices, and the spare. And yet when I do this with a test array, I don't get this behavior. So I nuked /dev/sda3 again, used it to create a brand-new array, and am copying the data from the old array to the new one: rsync -r -t -v --exclude 'lost+found' --progress /mnt/oldarray/* /mnt/newarray This will, of course, take hours. Hopefully when I'm done, I can stop the old array entirely, nuke /dev/sdb3, and add it to the new array. Hopefully, it won't get added as a spare!

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  • Using Unity Application Block – from basics to generics

    - by nmarun
    I just wanted to have one place where I list all the six Unity blogs I’ve written. Part 1: The very basics – Begin using Unity (code here) Part 2: Registering other types and resolving them (code here) Part 3: Lifetime Management (code here) Part 4: Constructor and Property or Setter Injection (code here) Part 5: Arrays (code here) Part 6: Generics (code here) Hope this helps someone (and this is the smallest blog I’ve posted till now).

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  • Links to Success Enabled With Permanent Links

    The right website is the one that will be able to make your business reach the heights of success. Today the entire world relies on the internet and you have to work really hard to make sure that your website gets noticed among the vast arrays of websites that are out there.

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  • World Record Performance on PeopleSoft Enterprise Financials Benchmark on SPARC T4-2

    - by Brian
    Oracle's SPARC T4-2 server achieved World Record performance on Oracle's PeopleSoft Enterprise Financials 9.1 executing 20 Million Journals lines in 8.92 minutes on Oracle Database 11g Release 2 running on Oracle Solaris 11. This is the first result published on this version of the benchmark. The SPARC T4-2 server was able to process 20 million general ledger journal edit and post batch jobs in 8.92 minutes on this benchmark that reflects a large customer environment that utilizes a back-end database of nearly 500 GB. This benchmark demonstrates that the SPARC T4-2 server with PeopleSoft Financials 9.1 can easily process 100 million journal lines in less than 1 hour. The SPARC T4-2 server delivered more than 146 MB/sec of IO throughput with Oracle Database 11g running on Oracle Solaris 11. Performance Landscape Results are presented for PeopleSoft Financials Benchmark 9.1. Results obtained with PeopleSoft Financials Benchmark 9.1 are not comparable to the the previous version of the benchmark, PeopleSoft Financials Benchmark 9.0, due to significant change in data model and supports only batch. PeopleSoft Financials Benchmark, Version 9.1 Solution Under Test Batch (min) SPARC T4-2 (2 x SPARC T4, 2.85 GHz) 8.92 Results from PeopleSoft Financials Benchmark 9.0. PeopleSoft Financials Benchmark, Version 9.0 Solution Under Test Batch (min) Batch with Online (min) SPARC Enterprise M4000 (Web/App) SPARC Enterprise M5000 (DB) 33.09 34.72 SPARC T3-1 (Web/App) SPARC Enterprise M5000 (DB) 35.82 37.01 Configuration Summary Hardware Configuration: 1 x SPARC T4-2 server 2 x SPARC T4 processors, 2.85 GHz 128 GB memory Storage Configuration: 1 x Sun Storage F5100 Flash Array (for database and redo logs) 2 x Sun Storage 2540-M2 arrays and 2 x Sun Storage 2501-M2 arrays (for backup) Software Configuration: Oracle Solaris 11 11/11 SRU 7.5 Oracle Database 11g Release 2 (11.2.0.3) PeopleSoft Financials 9.1 Feature Pack 2 PeopleSoft Supply Chain Management 9.1 Feature Pack 2 PeopleSoft PeopleTools 8.52 latest patch - 8.52.03 Oracle WebLogic Server 10.3.5 Java Platform, Standard Edition Development Kit 6 Update 32 Benchmark Description The PeopleSoft Enterprise Financials 9.1 benchmark emulates a large enterprise that processes and validates a large number of financial journal transactions before posting the journal entry to the ledger. The validation process certifies that the journal entries are accurate, ensuring that ChartFields values are valid, debits and credits equal out, and inter/intra-units are balanced. Once validated, the entries are processed, ensuring that each journal line posts to the correct target ledger, and then changes the journal status to posted. In this benchmark, the Journal Edit & Post is set up to edit and post both Inter-Unit and Regular multi-currency journals. The benchmark processes 20 million journal lines using AppEngine for edits and Cobol for post processes. See Also Oracle PeopleSoft Benchmark White Papers oracle.com SPARC T4-2 Server oracle.com OTN PeopleSoft Financial Management oracle.com OTN Oracle Solaris oracle.com OTN Oracle Database 11g Release 2 Enterprise Edition oracle.com OTN Disclosure Statement Copyright 2012, Oracle and/or its affiliates. All rights reserved. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Results as of 1 October 2012.

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  • Anatomy of a .NET Assembly - Custom attribute encoding

    - by Simon Cooper
    In my previous post, I covered how field, method, and other types of signatures are encoded in a .NET assembly. Custom attribute signatures differ quite a bit from these, which consequently affects attribute specifications in C#. Custom attribute specifications In C#, you can apply a custom attribute to a type or type member, specifying a constructor as well as the values of fields or properties on the attribute type: public class ExampleAttribute : Attribute { public ExampleAttribute(int ctorArg1, string ctorArg2) { ... } public Type ExampleType { get; set; } } [Example(5, "6", ExampleType = typeof(string))] public class C { ... } How does this specification actually get encoded and stored in an assembly? Specification blob values Custom attribute specification signatures use the same building blocks as other types of signatures; the ELEMENT_TYPE structure. However, they significantly differ from other types of signatures, in that the actual parameter values need to be stored along with type information. There are two types of specification arguments in a signature blob; fixed args and named args. Fixed args are the arguments to the attribute type constructor, named arguments are specified after the constructor arguments to provide a value to a field or property on the constructed attribute type (PropertyName = propValue) Values in an attribute blob are limited to one of the basic types (one of the number types, character, or boolean), a reference to a type, an enum (which, in .NET, has to use one of the integer types as a base representation), or arrays of any of those. Enums and the basic types are easy to store in a blob - you simply store the binary representation. Strings are stored starting with a compressed integer indicating the length of the string, followed by the UTF8 characters. Array values start with an integer indicating the number of elements in the array, then the item values concatentated together. Rather than using a coded token, Type values are stored using a string representing the type name and fully qualified assembly name (for example, MyNs.MyType, MyAssembly, Version=1.0.0.0, Culture=neutral, PublicKeyToken=0123456789abcdef). If the type is in the current assembly or mscorlib then just the type name can be used. This is probably done to prevent direct references between assemblies solely because of attribute specification arguments; assemblies can be loaded in the reflection-only context and attribute arguments still processed, without loading the entire assembly. Fixed and named arguments Each entry in the CustomAttribute metadata table contains a reference to the object the attribute is applied to, the attribute constructor, and the specification blob. The number and type of arguments to the constructor (the fixed args) can be worked out by the method signature referenced by the attribute constructor, and so the fixed args can simply be concatenated together in the blob without any extra type information. Named args are different. These specify the value to assign to a field or property once the attribute type has been constructed. In the CLR, fields and properties can be overloaded just on their type; different fields and properties can have the same name. Therefore, to uniquely identify a field or property you need: Whether it's a field or property (indicated using byte values 0x53 and 0x54, respectively) The field or property type The field or property name After the fixed arg values is a 2-byte number specifying the number of named args in the blob. Each named argument has the above information concatenated together, mostly using the basic ELEMENT_TYPE values, in the same way as a method or field signature. A Type argument is represented using the byte 0x50, and an enum argument is represented using the byte 0x55 followed by a string specifying the name and assembly of the enum type. The named argument property information is followed by the argument value, using the same encoding as fixed args. Boxed objects This would be all very well, were it not for object and object[]. Arguments and properties of type object allow a value of any allowed argument type to be specified. As a result, more information needs to be specified in the blob to interpret the argument bytes as the correct type. So, the argument value is simple prepended with the type of the value by specifying the ELEMENT_TYPE or name of the enum the value represents. For named arguments, a field or property of type object is represented using the byte 0x51, with the actual type specified in the argument value. Some examples... All property signatures start with the 2-byte value 0x0001. Similar to my previous post in the series, names in capitals correspond to a particular byte value in the ELEMENT_TYPE structure. For strings, I'll simply give the string value, rather than the length and UTF8 encoding in the actual blob. I'll be using the following enum and attribute types to demonstrate specification encodings: class AttrAttribute : Attribute { public AttrAttribute() {} public AttrAttribute(Type[] tArray) {} public AttrAttribute(object o) {} public AttrAttribute(MyEnum e) {} public AttrAttribute(ushort x, int y) {} public AttrAttribute(string str, Type type1, Type type2) {} public int Prop1 { get; set; } public object Prop2 { get; set; } public object[] ObjectArray; } enum MyEnum : int { Val1 = 1, Val2 = 2 } Now, some examples: Here, the the specification binds to the (ushort, int) attribute constructor, with fixed args only. The specification blob starts off with a prolog, followed by the two constructor arguments, then the number of named arguments (zero): [Attr(42, 84)] 0x0001 0x002a 0x00000054 0x0000 An example of string and type encoding: [Attr("MyString", typeof(Array), typeof(System.Windows.Forms.Form))] 0x0001 "MyString" "System.Array" "System.Windows.Forms.Form, System.Windows.Forms, Version=4.0.0.0, Culture=neutral, PublicKeyToken=b77a5c561934e089" 0x0000 As you can see, the full assembly specification of a type is only needed if the type isn't in the current assembly or mscorlib. Note, however, that the C# compiler currently chooses to fully-qualify mscorlib types anyway. An object argument (this binds to the object attribute constructor), and two named arguments (a null string is represented by 0xff and the empty string by 0x00) [Attr((ushort)40, Prop1 = 12, Prop2 = "")] 0x0001 U2 0x0028 0x0002 0x54 I4 "Prop1" 0x0000000c 0x54 0x51 "Prop2" STRING 0x00 Right, more complicated now. A type array as a fixed argument: [Attr(new[] { typeof(string), typeof(object) })] 0x0001 0x00000002 // the number of elements "System.String" "System.Object" 0x0000 An enum value, which is simply represented using the underlying value. The CLR works out that it's an enum using information in the attribute constructor signature: [Attr(MyEnum.Val1)] 0x0001 0x00000001 0x0000 And finally, a null array, and an object array as a named argument: [Attr((Type[])null, ObjectArray = new object[] { (byte)2, typeof(decimal), null, MyEnum.Val2 })] 0x0001 0xffffffff 0x0001 0x53 SZARRAY 0x51 "ObjectArray" 0x00000004 U1 0x02 0x50 "System.Decimal" STRING 0xff 0x55 "MyEnum" 0x00000002 As you'll notice, a null object is encoded as a null string value, and a null array is represented using a length of -1 (0xffffffff). How does this affect C#? So, we can now explain why the limits on attribute arguments are so strict in C#. Attribute specification blobs are limited to basic numbers, enums, types, and arrays. As you can see, this is because the raw CLR encoding can only accommodate those types. Special byte patterns have to be used to indicate object, string, Type, or enum values in named arguments; you can't specify an arbitary object type, as there isn't a generalised way of encoding the resulting value in the specification blob. In particular, decimal values can't be encoded, as it isn't a 'built-in' CLR type that has a native representation (you'll notice that decimal constants in C# programs are compiled as several integer arguments to DecimalConstantAttribute). Jagged arrays also aren't natively supported, although you can get around it by using an array as a value to an object argument: [Attr(new object[] { new object[] { new Type[] { typeof(string) } }, 42 })] Finally... Phew! That was a bit longer than I thought it would be. Custom attribute encodings are complicated! Hopefully this series has been an informative look at what exactly goes on inside a .NET assembly. In the next blog posts, I'll be carrying on with the 'Inside Red Gate' series.

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  • tile_static, tile_barrier, and tiled matrix multiplication with C++ AMP

    - by Daniel Moth
    We ended the previous post with a mechanical transformation of the C++ AMP matrix multiplication example to the tiled model and in the process introduced tiled_index and tiled_grid. This is part 2. tile_static memory You all know that in regular CPU code, static variables have the same value regardless of which thread accesses the static variable. This is in contrast with non-static local variables, where each thread has its own copy. Back to C++ AMP, the same rules apply and each thread has its own value for local variables in your lambda, whereas all threads see the same global memory, which is the data they have access to via the array and array_view. In addition, on an accelerator like the GPU, there is a programmable cache, a third kind of memory type if you'd like to think of it that way (some call it shared memory, others call it scratchpad memory). Variables stored in that memory share the same value for every thread in the same tile. So, when you use the tiled model, you can have variables where each thread in the same tile sees the same value for that variable, that threads from other tiles do not. The new storage class for local variables introduced for this purpose is called tile_static. You can only use tile_static in restrict(direct3d) functions, and only when explicitly using the tiled model. What this looks like in code should be no surprise, but here is a snippet to confirm your mental image, using a good old regular C array // each tile of threads has its own copy of locA, // shared among the threads of the tile tile_static float locA[16][16]; Note that tile_static variables are scoped and have the lifetime of the tile, and they cannot have constructors or destructors. tile_barrier In amp.h one of the types introduced is tile_barrier. You cannot construct this object yourself (although if you had one, you could use a copy constructor to create another one). So how do you get one of these? You get it, from a tiled_index object. Beyond the 4 properties returning index objects, tiled_index has another property, barrier, that returns a tile_barrier object. The tile_barrier class exposes a single member, the method wait. 15: // Given a tiled_index object named t_idx 16: t_idx.barrier.wait(); 17: // more code …in the code above, all threads in the tile will reach line 16 before a single one progresses to line 17. Note that all threads must be able to reach the barrier, i.e. if you had branchy code in such a way which meant that there is a chance that not all threads could reach line 16, then the code above would be illegal. Tiled Matrix Multiplication Example – part 2 So now that we added to our understanding the concepts of tile_static and tile_barrier, let me obfuscate rewrite the matrix multiplication code so that it takes advantage of tiling. Before you start reading this, I suggest you get a cup of your favorite non-alcoholic beverage to enjoy while you try to fully understand the code. 01: void MatrixMultiplyTiled(vector<float>& vC, const vector<float>& vA, const vector<float>& vB, int M, int N, int W) 02: { 03: static const int TS = 16; 04: array_view<const float,2> a(M, W, vA); 05: array_view<const float,2> b(W, N, vB); 06: array_view<writeonly<float>,2> c(M,N,vC); 07: parallel_for_each(c.grid.tile< TS, TS >(), 08: [=] (tiled_index< TS, TS> t_idx) restrict(direct3d) 09: { 10: int row = t_idx.local[0]; int col = t_idx.local[1]; 11: float sum = 0.0f; 12: for (int i = 0; i < W; i += TS) { 13: tile_static float locA[TS][TS], locB[TS][TS]; 14: locA[row][col] = a(t_idx.global[0], col + i); 15: locB[row][col] = b(row + i, t_idx.global[1]); 16: t_idx.barrier.wait(); 17: for (int k = 0; k < TS; k++) 18: sum += locA[row][k] * locB[k][col]; 19: t_idx.barrier.wait(); 20: } 21: c[t_idx.global] = sum; 22: }); 23: } Notice that all the code up to line 9 is the same as per the changes we made in part 1 of tiling introduction. If you squint, the body of the lambda itself preserves the original algorithm on lines 10, 11, and 17, 18, and 21. The difference being that those lines use new indexing and the tile_static arrays; the tile_static arrays are declared and initialized on the brand new lines 13-15. On those lines we copy from the global memory represented by the array_view objects (a and b), to the tile_static vanilla arrays (locA and locB) – we are copying enough to fit a tile. Because in the code that follows on line 18 we expect the data for this tile to be in the tile_static storage, we need to synchronize the threads within each tile with a barrier, which we do on line 16 (to avoid accessing uninitialized memory on line 18). We also need to synchronize the threads within a tile on line 19, again to avoid the race between lines 14, 15 (retrieving the next set of data for each tile and overwriting the previous set) and line 18 (not being done processing the previous set of data). Luckily, as part of the awesome C++ AMP debugger in Visual Studio there is an option that helps you find such races, but that is a story for another blog post another time. May I suggest reading the next section, and then coming back to re-read and walk through this code with pen and paper to really grok what is going on, if you haven't already? Cool. Why would I introduce this tiling complexity into my code? Funny you should ask that, I was just about to tell you. There is only one reason we tiled our extent, had to deal with finding a good tile size, ensure the number of threads we schedule are correctly divisible with the tile size, had to use a tiled_index instead of a normal index, and had to understand tile_barrier and to figure out where we need to use it, and double the size of our lambda in terms of lines of code: the reason is to be able to use tile_static memory. Why do we want to use tile_static memory? Because accessing tile_static memory is around 10 times faster than accessing the global memory on an accelerator like the GPU, e.g. in the code above, if you can get 150GB/second accessing data from the array_view a, you can get 1500GB/second accessing the tile_static array locA. And since by definition you are dealing with really large data sets, the savings really pay off. We have seen tiled implementations being twice as fast as their non-tiled counterparts. Now, some algorithms will not have performance benefits from tiling (and in fact may deteriorate), e.g. algorithms that require you to go only once to global memory will not benefit from tiling, since with tiling you already have to fetch the data once from global memory! Other algorithms may benefit, but you may decide that you are happy with your code being 150 times faster than the serial-version you had, and you do not need to invest to make it 250 times faster. Also algorithms with more than 3 dimensions, which C++ AMP supports in the non-tiled model, cannot be tiled. Also note that in future releases, we may invest in making the non-tiled model, which already uses tiling under the covers, go the extra step and use tile_static memory on your behalf, but it is obviously way to early to commit to anything like that, and we certainly don't do any of that today. Comments about this post by Daniel Moth welcome at the original blog.

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  • Array Multiplication and Division

    - by Narfanator
    I came across a question that (eventually) landed me wondering about array arithmetic. I'm thinking specifically in Ruby, but I think the concepts are language independent. So, addition and subtraction are defined, in Ruby, as such: [1,6,8,3,6] + [5,6,7] == [1,6,8,3,6,5,6,7] # All the elements of the first, then all the elements of the second [1,6,8,3,6] - [5,6,7] == [1,8,3] # From the first, remove anything found in the second and array * scalar is defined: [1,2,3] * 2 == [1,2,3,1,2,3] But What, conceptually, should the following be? None of these are (as far as I can find) defined: Array x Array: [1,2,3] * [1,2,3] #=> ? Array / Scalar: [1,2,3,4,5] / 2 #=> ? Array / Scalar: [1,2,3,4,5] % 2 #=> ? Array / Array: [1,2,3,4,5] / [1,2] #=> ? Array / Array: [1,2,3,4,5] % [1,2] #=> ? I've found some mathematical descriptions of these operations for set theory, but I couldn't really follow them, and sets don't have duplicates (arrays do). Edit: Note, I do not mean vector (matrix) arithmetic, which is completely defined. Edit2: If this is the wrong stack exchange, tell me which is the right one and I'll move it. Edit 3: Add mod operators to the list. Edit 4: I figure array / scalar is derivable from array * scalar: a * b = c => a = b / c [1,2,3] * 3 = [1,2,3]+[1,2,3]+[1,2,3] = [1,2,3,1,2,3,1,2,3] => [1,2,3] = [1,2,3,1,2,3,1,2,3] / 3 Which, given that programmer's division ignore the remained and has modulus: [1,2,3,4,5] / 2 = [[1,2], [3,4]] [1,2,3,4,5] % 2 = [5] Except that these are pretty clearly non-reversible operations (not that modulus ever is), which is non-ideal. Edit: I asked a question over on Math that led me to Multisets. I think maybe extensible arrays are "multisets", but I'm not sure yet.

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  • Using polygons instead of quads on Cocos2d

    - by rraallvv
    I've been looking under the hood of Cocos2d, and I think (please correct me if I'm wrong) that although working with quads is a key feature of the engine, it should't be dificult to make it work with arrays of vertices (aka polygons) instead of quads, being the quads a special case of an array of four vertices by the way, does anyone have any code that makes cocos2d render a texture filled polygon inside a batch node? the code posted here (http://www.cocos2d-iphone.org/forum/topic/8142/page/2#post-89393) does a nice job rendering a texture filled polygon but the class doesn't work with batch nodes

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  • Is there an appropriate coding style for implementing an algorithm during an interview?

    - by GlenPeterson
    I failed an interview question in C years ago about converting hex to decimal by not exploiting the ASCII table if (inputDigitByte > 9) hex = inputDigitByte - 'a'. The rise of Unicode has made this question pretty silly, but the point was that the interviewer valued raw execution speed above readability and error handling. They tell you to review algorithms textbooks to prepare for these interviews, yet these same textbooks tend to favor the implementation with the fewest lines of code, even if it has to rely on magic numbers (like "infinity") and a slower, more memory-intensive implementation (like a linked list instead of an array) to do that. I don't know what is right. Coding an algorithm within the space of an interview has at least 3 constraints: time to code, elegance/readability, and efficiency of execution. What trade-offs are appropriate for interview code? How much do you follow the textbook definition of an algorithm? Is it better to eliminate recursion, unroll loops, and use arrays for efficiency? Or is it better to use recursion and special values like "infinity" or Integer.MAX_VALUE to reduce the number of lines of code needed to write the algorithm? Interface: Make a very self-contained, bullet-proof interface, or sloppy and fast? On the one extreme, the array to be sorted might be a public static variable. On the other extreme, it might need to be passed to each method, allowing methods to be called individually from different threads for different purposes. Is it appropriate to use a linked-list data structure for items that are traversed in one direction vs. using arrays and doubling the size when the array is full? Implementing a singly-linked list during the interview is often much faster to code and easier remember for recursive algorithms like MergeSort. Thread safety - just document that it's unsafe, or say so verbally? How much should the interviewee be looking for opportunities for parallel processing? Is bit shifting appropriate? x / 2 or x >> 1 Polymorphism, type safety, and generics? Comments? Variable and method names: qs(a, p, q, r) vs: quickSort(theArray, minIdx, partIdx, maxIdx) How much should you use existing APIs? Obviously you can't use a java.util.HashMap to implement a hash-table, but what about using a java.util.List to accumulate your sorted results? Are there any guiding principals that would answer these and other questions, or is the guiding principal to ask the interviewer? Or maybe this should be the basis of a discussion while writing the code? If an interviewer can't or won't answer one of these questions, are there any tips for coaxing the information out of them?

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  • RiverTrail - JavaScript GPPGU Data Parallelism

    - by JoshReuben
    Where is WebCL ? The Khronos WebCL working group is working on a JavaScript binding to the OpenCL standard so that HTML 5 compliant browsers can host GPGPU web apps – e.g. for image processing or physics for WebGL games - http://www.khronos.org/webcl/ . While Nokia & Samsung have some protype WebCL APIs, Intel has one-upped them with a higher level of abstraction: RiverTrail. Intro to RiverTrail Intel Labs JavaScript RiverTrail provides GPU accelerated SIMD data-parallelism in web applications via a familiar JavaScript programming paradigm. It extends JavaScript with simple deterministic data-parallel constructs that are translated at runtime into a low-level hardware abstraction layer. With its high-level JS API, programmers do not have to learn a new language or explicitly manage threads, orchestrate shared data synchronization or scheduling. It has been proposed as a draft specification to ECMA a (known as ECMA strawman). RiverTrail runs in all popular browsers (except I.E. of course). To get started, download a prebuilt version https://github.com/downloads/RiverTrail/RiverTrail/rivertrail-0.17.xpi , install Intel's OpenCL SDK http://www.intel.com/go/opencl and try out the interactive River Trail shell http://rivertrail.github.com/interactive For a video overview, see  http://www.youtube.com/watch?v=jueg6zB5XaM . ParallelArray the ParallelArray type is the central component of this API & is a JS object that contains ordered collections of scalars – i.e. multidimensional uniform arrays. A shape property describes the dimensionality and size– e.g. a 2D RGBA image will have shape [height, width, 4]. ParallelArrays are immutable & fluent – they are manipulated by invoking methods on them which produce new ParallelArray objects. ParallelArray supports several constructors over arrays, functions & even the canvas. // Create an empty Parallel Array var pa = new ParallelArray(); // pa0 = <>   // Create a ParallelArray out of a nested JS array. // Note that the inner arrays are also ParallelArrays var pa = new ParallelArray([ [0,1], [2,3], [4,5] ]); // pa1 = <<0,1>, <2,3>, <4.5>>   // Create a two-dimensional ParallelArray with shape [3, 2] using the comprehension constructor var pa = new ParallelArray([3, 2], function(iv){return iv[0] * iv[1];}); // pa7 = <<0,0>, <0,1>, <0,2>>   // Create a ParallelArray from canvas.  This creates a PA with shape [w, h, 4], var pa = new ParallelArray(canvas); // pa8 = CanvasPixelArray   ParallelArray exposes fluent API functions that take an elemental JS function for data manipulation: map, combine, scan, filter, and scatter that return a new ParallelArray. Other functions are scalar - reduce  returns a scalar value & get returns the value located at a given index. The onus is on the developer to ensure that the elemental function does not defeat data parallelization optimization (avoid global var manipulation, recursion). For reduce & scan, order is not guaranteed - the onus is on the dev to provide an elemental function that is commutative and associative so that scan will be deterministic – E.g. Sum is associative, but Avg is not. map Applies a provided elemental function to each element of the source array and stores the result in the corresponding position in the result array. The map method is shape preserving & index free - can not inspect neighboring values. // Adding one to each element. var source = new ParallelArray([1,2,3,4,5]); var plusOne = source.map(function inc(v) {     return v+1; }); //<2,3,4,5,6> combine Combine is similar to map, except an index is provided. This allows elemental functions to access elements from the source array relative to the one at the current index position. While the map method operates on the outermost dimension only, combine, can choose how deep to traverse - it provides a depth argument to specify the number of dimensions it iterates over. The elemental function of combine accesses the source array & the current index within it - element is computed by calling the get method of the source ParallelArray object with index i as argument. It requires more code but is more expressive. var source = new ParallelArray([1,2,3,4,5]); var plusOne = source.combine(function inc(i) { return this.get(i)+1; }); reduce reduces the elements from an array to a single scalar result – e.g. Sum. // Calculate the sum of the elements var source = new ParallelArray([1,2,3,4,5]); var sum = source.reduce(function plus(a,b) { return a+b; }); scan Like reduce, but stores the intermediate results – return a ParallelArray whose ith elements is the results of using the elemental function to reduce the elements between 0 and I in the original ParallelArray. // do a partial sum var source = new ParallelArray([1,2,3,4,5]); var psum = source.scan(function plus(a,b) { return a+b; }); //<1, 3, 6, 10, 15> scatter a reordering function - specify for a certain source index where it should be stored in the result array. An optional conflict function can prevent an exception if two source values are assigned the same position of the result: var source = new ParallelArray([1,2,3,4,5]); var reorder = source.scatter([4,0,3,1,2]); // <2, 4, 5, 3, 1> // if there is a conflict use the max. use 33 as a default value. var reorder = source.scatter([4,0,3,4,2], 33, function max(a, b) {return a>b?a:b; }); //<2, 33, 5, 3, 4> filter // filter out values that are not even var source = new ParallelArray([1,2,3,4,5]); var even = source.filter(function even(iv) { return (this.get(iv) % 2) == 0; }); // <2,4> Flatten used to collapse the outer dimensions of an array into a single dimension. pa = new ParallelArray([ [1,2], [3,4] ]); // <<1,2>,<3,4>> pa.flatten(); // <1,2,3,4> Partition used to restore the original shape of the array. var pa = new ParallelArray([1,2,3,4]); // <1,2,3,4> pa.partition(2); // <<1,2>,<3,4>> Get return value found at the indices or undefined if no such value exists. var pa = new ParallelArray([0,1,2,3,4], [10,11,12,13,14], [20,21,22,23,24]) pa.get([1,1]); // 11 pa.get([1]); // <10,11,12,13,14>

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  • Searching for a key in a multi dimensional array and adding it to another array [migrated]

    - by Moha
    Let's say I have two multi dimensional arrays: array1 ( stuff1 = array ( data = 'abc' ) stuff2 = array ( something = '123' data = 'def' ) stuff3 = array ( stuff4 = array ( data = 'ghi' ) ) ) array2 ( stuff1 = array ( ) stuff3 = array ( anything = '456' ) ) What I want is to search the key 'data' in array1 and then insert the key and value to array2 regardless of the depth. So wherever key 'data' exists in array1 it gets added to array2 with the exact depth (and key names) as in array1 AND without modifying any other keys. How can I do this recursively?

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  • Microsoft Interview Preparation

    - by Manish
    I have 8 years of java background. Need help in identifying topics I need to prepare for Microsoft interview. I need to know how many rounds Microsoft will have and what all things these rounds consist of. I have identified the following topics. Please let me know if I need to prepare anything else as well. Arrays Linked Lists Recursion Stacks Queue Trees Graph -- What all I should prepare here Dynamic Programming -- again what all I need to prepare Sorting, Searching String Algos

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  • Replace a failed drive in Linux RAID

    <b>Tech Republic:</b> "A few weeks ago I had the distinct displeasure of waking up to a series of emails indicating that a series of RAID arrays on a remote system had degraded. The remote system was still running, but one of the hard drives was pretty much dead."

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  • When will java change to 64bit addressing and how can we get there faster?

    - by Ido Tamir
    Having to work with large files now, I would like to know when the java libraries will start switching to long for indexing in their methods. From Inputstreams read(byte[] b, int off, int len) - funnily there is long skip(long) also - to MappedByteBuffer to the basic indexing of arrays and lists, everything is adressed as int. Is there an official plan for enhancment of the libraries? Do initiatives exist to pressure oracle into enhancing the libraries, if there is no official plan yet?

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