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  • Implementing a Custom Coherence PartitionAssignmentStrategy

    - by jpurdy
    A recent A-Team engagement required the development of a custom PartitionAssignmentStrategy (PAS). By way of background, a PAS is an implementation of a Java interface that controls how a Coherence partitioned cache service assigns partitions (primary and backup copies) across the available set of storage-enabled members. While seemingly straightforward, this is actually a very difficult problem to solve. Traditionally, Coherence used a distributed algorithm spread across the cache servers (and as of Coherence 3.7, this is still the default implementation). With the introduction of the PAS interface, the model of operation was changed so that the logic would run solely in the cache service senior member. Obviously, this makes the development of a custom PAS vastly less complex, and in practice does not introduce a significant single point of failure/bottleneck. Note that Coherence ships with a default PAS implementation but it is not used by default. Further, custom PAS implementations are uncommon (this engagement was the first custom implementation that we know of). The particular implementation mentioned above also faced challenges related to managing multiple backup copies but that won't be discussed here. There were a few challenges that arose during design and implementation: Naive algorithms had an unreasonable upper bound of computational cost. There was significant complexity associated with configurations where the member count varied significantly between physical machines. Most of the complexity of a PAS is related to rebalancing, not initial assignment (which is usually fairly simple). A custom PAS may need to solve several problems simultaneously, such as: Ensuring that each member has a similar number of primary and backup partitions (e.g. each member has the same number of primary and backup partitions) Ensuring that each member carries similar responsibility (e.g. the most heavily loaded member has no more than one partition more than the least loaded). Ensuring that each partition is on the same member as a corresponding local resource (e.g. for applications that use partitioning across message queues, to ensure that each partition is collocated with its corresponding message queue). Ensuring that a given member holds no more than a given number of partitions (e.g. no member has more than 10 partitions) Ensuring that backups are placed far enough away from the primaries (e.g. on a different physical machine or a different blade enclosure) Achieving the above goals while ensuring that partition movement is minimized. These objectives can be even more complicated when the topology of the cluster is irregular. For example, if multiple cluster members may exist on each physical machine, then clearly the possibility exists that at certain points (e.g. following a member failure), the number of members on each machine may vary, in certain cases significantly so. Consider the case where there are three physical machines, with 3, 3 and 9 members each (respectively). This introduces complexity since the backups for the 9 members on the the largest machine must be spread across the other 6 members (to ensure placement on different physical machines), preventing an even distribution. For any given problem like this, there are usually reasonable compromises available, but the key point is that objectives may conflict under extreme (but not at all unlikely) circumstances. The most obvious general purpose partition assignment algorithm (possibly the only general purpose one) is to define a scoring function for a given mapping of partitions to members, and then apply that function to each possible permutation, selecting the most optimal permutation. This would result in N! (factorial) evaluations of the scoring function. This is clearly impractical for all but the smallest values of N (e.g. a partition count in the single digits). It's difficult to prove that more efficient general purpose algorithms don't exist, but the key take away from this is that algorithms will tend to either have exorbitant worst case performance or may fail to find optimal solutions (or both) -- it is very important to be able to show that worst case performance is acceptable. This quickly leads to the conclusion that the problem must be further constrained, perhaps by limiting functionality or by using domain-specific optimizations. Unfortunately, it can be very difficult to design these more focused algorithms. In the specific case mentioned, we constrained the solution space to very small clusters (in terms of machine count) with small partition counts and supported exactly two backup copies, and accepted the fact that partition movement could potentially be significant (preferring to solve that issue through brute force). We then used the out-of-the-box PAS implementation as a fallback, delegating to it for configurations that were not supported by our algorithm. Our experience was that the PAS interface is quite usable, but there are intrinsic challenges to designing PAS implementations that should be very carefully evaluated before committing to that approach.

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  • Getting started with object detection - Image segmentation algorithm

    - by Dev Kanchen
    Just getting started on a hobby object-detection project. My aim is to understand the underlying algorithms and to this end the overall accuracy of the results is (currently) more important than actual run-time. I'm starting with trying to find a good image segmentation algorithm that provide a good jump-off point for the object detection phase. The target images would be "real-world" scenes. I found two techniques which mirrored my thoughts on how to go about this: Graph-based Image Segmentation: http://www.cs.cornell.edu/~dph/papers/seg-ijcv.pdf Contour and Texture Analysis for Image Segmentation: http://www.eng.utah.edu/~bresee/compvision/files/MalikBLS.pdf The first one was really intuitive to understand and seems simple enough to implement, while the second was closer to my initial thoughts on how to go about this (combine color/intensity and texture information to find regions). But it's an order of magnitude more complex (at least for me). My question is - are there any other algorithms I should be looking at that provide the kind of results that these two, specific papers have arrived at. Are there updated versions of these techniques already floating around. Like I mentioned earlier, the goal is relative accuracy of image segmentation (with an eventual aim to achieve a degree of accuracy of object detection) over runtime, with the algorithm being able to segment an image into "naturally" or perceptually important components, as these two algorithms do (each to varying extents). Thanks! P.S.1: I found these two papers after a couple of days of refining my search terms and learning new ones relevant to the exact kind of techniques I was looking for. :) I have just about reached the end of my personal Google creativity, which is why I am finally here! Thanks for the help. P.S.2: I couldn't find good tags for this question. If some relevant ones exist, @mods please add them. P.S.3: I do not know if this is a better fit for cstheory.stackexchange (or even cs.stackexchange). I looked but cstheory seems more appropriate for intricate algorithmic discussions than a broad question like this. Also, I couldn't find any relevant tags there either! But please do move if appropriate.

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  • What is a useful pattern to maintaining an object state in a one to many relationship?

    - by ahenderson
    I am looking for a design for my application, here are the players(classes) involved. struct Transform { // Uses a matrix to transform the position. // Also acts acts as the state of a Dialog. Position transform(Position p); //other methods. }; struct Dialog { // There are multiple dialog for the user to transform the output. Transform& t; void ChangeTranformation(){t.rotate(360);} } struct Algorithm { //gives us a position based on an implementation. For example this can return points on a circle or line. Transform& t; Position m_p; Dialog& d; Position GetCurrentPosition(){ return t.transform(m_p);} //other methods. } Properties I need: Each algorithms has one dialog and each dialog can have many algorithms associated with it. When the user selects an algorithm a dialog associated with that algorithm is displayed. If the user selects a different algorithm then re-selects back the state is restored in the dialog. Basically I want a good design pattern to maintain the state of the dialog given that many algorithms use it and they can be switched back and forth. Does anyone have any suggestions? Here is a use case: Dialog1 has a single edit box to control the radius. Algorithm1 generates points on a unit circle. Algorithm2 is the same as Algorithm1. The user has selected Algorithm1 and entered 2 into the edit box. This will generate points on a circle of radius 2. The user then selects Algorithm2 and enters 10 into the edit box of Dialog1. This will generate points on a circle of radius 10. Finally Algorithm1 is selected again. The edit box of Dialog1 should show 2 and points on a circle of radius 2 should be generated.

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  • Design pattern: static function call with input/output containers?

    - by Pavlo Dyban
    I work for a company in software research department. We use algorithms from our real software and wrap them so that we can use them for prototyping. Every time an algorithm interface changes, we need to adapt our wrappers respectively. Recently all algorithms have been refactored in such a manner that instead of accepting many different inputs and returning outputs via referenced parameters, they now accept one input data container and one output data container (the latter is passed by reference). Algorithm interface is limited to a static function call like that: class MyAlgorithm{ static bool calculate(MyAlgorithmInput input, MyAlgorithmOutput &output); } This is actually a very powerful design, though I have never seen it in a C++ programming environment before. Changes in the number of parameters and their data types are now encapsulated and they don't change the algorithm callback. In the latest algorithm which I have developed I used the same scheme. Now I want to know if this is a popular design pattern and what it is called.

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  • entity set expansion python

    - by Nicolas M.
    Do you know of any existing implementation in any language (preferably python) of any entity set expansion algorithms, such that the one from Google sets ? ( http://labs.google.com/sets ) I couldn't find any library implementing such algorithms and I'd like to play with some of those to see how they would perform on some specific task I would like to implement. Any help is welcome ! Thanks a lot for your help, Regards, Nicolas.

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  • Algorithm for performing decentralized search in social networks

    - by Jack
    I want to find out all the existing decentralized algorithms that exploit the structural properties of social networks. So far I know the following algorithms - 1) Best connected search - Adamic et al 2) Random Walk (does not exploit any structural property but still it is decentralized) 3) Hamming distance search 4) Weak/Strong tie search Any help would be appreciated

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  • How do you solve the 15-puzzle with A-Star or Dijkstra's Algorithm?

    - by Sean
    I've read in one of my AI books that popular algorithms (A-Star, Dijkstra) for path-finding in simulation or games is also used to solve the well-known "15-puzzle". Can anyone give me some pointers on how I would reduce the 15-puzzle to a graph of nodes and edges so that I could apply one of these algorithms? If I were to treat each node in the graph as a game state then wouldn't that tree become quite large? Or is that just the way to do it?

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  • Travelling Salesman Problem

    - by Arjun Vasudevan
    I'm trying to solve the travelling salesman problem using the following algorithms - DFS, Hill Climbing and A*. I could write up a code for solving it using DFS. Can I have some help in solving it using the other 2 algorithms? I searched for it a lot, on the web.

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  • Practical Uses of Fractals in Programming

    - by Sami
    Fractals have always been a bit of a mystery for me. What practical uses (beyond rendering to beautiful images) are there for fractals in the various programming problem domains? And please, don't just list areas that use them. I'm interested in specific algorithms and how fractals are used with those algorithms to solve something in practice. Please at least give a short description of the algorithm.

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  • [LaTeX] Math symbols in listings

    - by Michal
    Hi, I have a problem with Latex -- I don't know how to put mathematical equations and symbols in listings. I use --listings-- package and it's offers great looking listings, but it doesn't allow math symbols in $ .. $. Another package --algorithms-- allows math, but listings doesn't look as good as in --listings-- (the problem is that --algorithms-- demands to get new line after every --if--, --then--, etc.) Thanks for reply Michal

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  • Reduce number of points in line

    - by culebrón
    I'm searching for algorithms to reduce the LOD of polylines, lines (looped or not) of nodes. In simple words, I want to take hi-resolution coastline data and be able to reduce its LOD hundred- or thousandfold to render it in small-scale. I found polygon reduction algorithms (but they require triangles) and Laplacian smoothing, but that doesn't seem exactly what I need.

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  • Motion detection information

    - by dotnetdev
    Hi, I know the AFORGE.NET API has motion detection algorithms, but what would be a good book to learn these algorithms with C# samples (the AFORGE.NET code is complex and not commented enough to help). Thanks

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  • How to generate and encode (for use in GA), random, strict, binary rooted trees with N leaves?

    - by Peter Simon
    First, I am an engineer, not a computer scientist, so I apologize in advance for any misuse of nomenclature and general ignorance of CS background. Here is the motivational background for my question: I am contemplating writing a genetic algorithm optimizer to aid in designing a power divider network (also called a beam forming network, or BFN for short). The BFN is intended to distribute power to each of N radiating elements in an array of antennas. The fraction of the total input power to be delivered to each radiating element has been specified. Topologically speaking, a BFN is a strictly binary, rooted tree. Each of the (N-1) interior nodes of the tree represents the input port of an unequal, binary power splitter. The N leaves of the tree are the power divider outputs. Given a particular power divider topology, one is still free to map the power divider outputs to the array inputs in an arbitrary order. There are N! such permutations of the outputs. There are several considerations in choosing the desired ordering: 1) The power ratio for each binary coupler should be within a specified range of values. 2) The ordering should be chosen to simplify the mechanical routing of the transmission lines connecting the power divider. The number of ouputs N of the BFN may range from, say, 6 to 22. I have already written a genetic algorithm optimizer that, given a particular BFN topology and desired array input power distribution, will search through the N! permutations of the BFN outputs to generate a design with compliant power ratios and good mechanical routing. I would now like to generalize my program to automatically generate and search through the space of possible BFN topologies. As I understand it, for N outputs (leaves of the binary tree), there are $C_{N-1}$ different topologies that can be constructed, where $C_N$ is the Catalan number. I would like to know how to encode an arbitrary tree having N leaves in a way that is consistent with a chromosomal description for use in a genetic algorithm. Also associated with this is the need to generate random instances for filling the initial population, and to implement crossover and mutations operators for this type of chromosome. Any suggestions will be welcome. Please minimize the amount of CS lingo in your reply, since I am not likely to be acquainted with it. Thanks in advance, Peter

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  • What Software Engineering Areas should be stressed upon while Interviewing Candidate for Fulltime So

    - by Rachel
    Hi, This question is somewhat related to other posts which I found on Stackoverflow but not exactly and so am prompted to ask about it. I know we must ask for Data-Structures and Algorithms but what specific data-structures or Algorithms or other CS Concepts should be asked while interviewing Sr. Software Engineering Fulltime Position as compared with Software Engineering Position. Thanks.

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  • Problem solving/ Algorithm Skill is a knack or can be developed with practice?

    - by KaluSingh Gabbar
    Every time I start a hard problem and if can not figure out the exact solution or can not get started, I get into this never ending discussion with myself, as below: That problem solving/mathematics/algorithms skills are gifted (not that you can learn by practicing, by practice, you only master the kind of problems that you already have solved before) only those who went to good schools can do it, as they learned it early. What are your thoughts, can one achieve awesomeness in problem solving/algorithms just by hard work or you need to have that extra-gene in you?

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  • Android dev platform supporting OpenGL ES 2.0: Where to buy?

    - by pixelpush
    I plan to port some camera and multimedia algorithms and functionality on a Qualcomm Snapdragon platform running Android. I need OpenGL ES 2.0 acceleration for many algorithms. Which platform is the right one? Also, where can I purchase this? The Android dev platform on Google's website supports on OpenGL ES 1.x Thanks for any input.

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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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  • How can I chose the depth of a quadtree?

    - by Evpok
    In a 2d world, using a quadtree to prune pairs in collision detection, how can I chose the depth of said quadtree? The world I am dealing with is mostly made of moving objects¹, so the cost of dispatching the objects between the quadtree cells matter. So what I am interested in is the balance between the gain from less collision checking and the loss from more dispatching. 1. To be completely explicit, autonomous self-replicating cells competing for food sources, in an attempt to show my pupils predator-prey dynamics and genetic evolution at work

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  • Finding the heaviest length-constrained path in a weighted Binary Tree

    - by Hristo
    UPDATE I worked out an algorithm that I think runs in O(n*k) running time. Below is the pseudo-code: routine heaviestKPath( T, k ) // create 2D matrix with n rows and k columns with each element = -8 // we make it size k+1 because the 0th column must be all 0s for a later // function to work properly and simplicity in our algorithm matrix = new array[ T.getVertexCount() ][ k + 1 ] (-8); // set all elements in the first column of this matrix = 0 matrix[ n ][ 0 ] = 0; // fill our matrix by traversing the tree traverseToFillMatrix( T.root, k ); // consider a path that would arc over a node globalMaxWeight = -8; findArcs( T.root, k ); return globalMaxWeight end routine // node = the current node; k = the path length; node.lc = node’s left child; // node.rc = node’s right child; node.idx = node’s index (row) in the matrix; // node.lc.wt/node.rc.wt = weight of the edge to left/right child; routine traverseToFillMatrix( node, k ) if (node == null) return; traverseToFillMatrix(node.lc, k ); // recurse left traverseToFillMatrix(node.rc, k ); // recurse right // in the case that a left/right child doesn’t exist, or both, // let’s assume the code is smart enough to handle these cases matrix[ node.idx ][ 1 ] = max( node.lc.wt, node.rc.wt ); for i = 2 to k { // max returns the heavier of the 2 paths matrix[node.idx][i] = max( matrix[node.lc.idx][i-1] + node.lc.wt, matrix[node.rc.idx][i-1] + node.rc.wt); } end routine // node = the current node, k = the path length routine findArcs( node, k ) if (node == null) return; nodeMax = matrix[node.idx][k]; longPath = path[node.idx][k]; i = 1; j = k-1; while ( i+j == k AND i < k ) { left = node.lc.wt + matrix[node.lc.idx][i-1]; right = node.rc.wt + matrix[node.rc.idx][j-1]; if ( left + right > nodeMax ) { nodeMax = left + right; } i++; j--; } // if this node’s max weight is larger than the global max weight, update if ( globalMaxWeight < nodeMax ) { globalMaxWeight = nodeMax; } findArcs( node.lc, k ); // recurse left findArcs( node.rc, k ); // recurse right end routine Let me know what you think. Feedback is welcome. I think have come up with two naive algorithms that find the heaviest length-constrained path in a weighted Binary Tree. Firstly, the description of the algorithm is as follows: given an n-vertex Binary Tree with weighted edges and some value k, find the heaviest path of length k. For both algorithms, I'll need a reference to all vertices so I'll just do a simple traversal of the Tree to have a reference to all vertices, with each vertex having a reference to its left, right, and parent nodes in the tree. Algorithm 1 For this algorithm, I'm basically planning on running DFS from each node in the Tree, with consideration to the fixed path length. In addition, since the path I'm looking for has the potential of going from left subtree to root to right subtree, I will have to consider 3 choices at each node. But this will result in a O(n*3^k) algorithm and I don't like that. Algorithm 2 I'm essentially thinking about using a modified version of Dijkstra's Algorithm in order to consider a fixed path length. Since I'm looking for heaviest and Dijkstra's Algorithm finds the lightest, I'm planning on negating all edge weights before starting the traversal. Actually... this doesn't make sense since I'd have to run Dijkstra's on each node and that doesn't seem very efficient much better than the above algorithm. So I guess my main questions are several. Firstly, do the algorithms I've described above solve the problem at hand? I'm not totally certain the Dijkstra's version will work as Dijkstra's is meant for positive edge values. Now, I am sure there exist more clever/efficient algorithms for this... what is a better algorithm? I've read about "Using spine decompositions to efficiently solve the length-constrained heaviest path problem for trees" but that is really complicated and I don't understand it at all. Are there other algorithms that tackle this problem, maybe not as efficiently as spine decomposition but easier to understand? Thanks.

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  • ASP.NET 2.0 RijndaelManaged encryption algorithm vs. FIPS

    - by R Rush
    I'm running into an issue with an ASP.NET 2.0 application. Our network folks just upped our security, and now I get the floowing error whenever I try to access the app: "This implementation is not part of the Windows Platform FIPS validated cryptographic algorithms." I've done a little research, and it sounds like ASP.NET uses the RijndaelManaged AES encryption algorithm to encrypt the ViewState of pages... and RijndaelManaged is on the list of algorithms that aren't FIPS compliant. We're certainly not explicitly calling any encryption algorithm... much less anything on the non-compliant list. This ViewState business makes sense to me, I guess. The thing I can't muddle out, though, is what to do about it. I've found a KB article that suggests using a web.config setting to specify a different algorithm... but either that didn't stick, or that algorithm isn't up to snuff, either. So: 1) Is the RijndaelManaged / ViewState thing actually the problem? Or am I barking up the wrong tree? 2) How to I specify what algorithm to use instead of RijndaelManaged? I've got a list of algorithms that are and aren't compliant; I'm just not sure where to plug that information in. Thanks! Richard

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  • Thread Local Memory, Using std::string's internal buffer for c-style Scratch Memory.

    - by Hassan Syed
    I am using Protocol Buffers and OpensSSL to generate, HMACs and then CBC encrypt the two fields to obfuscate the session cookies -- similar Kerberos tokens. Protocol Buffers' API communicates with std::strings and has a buffer caching mechanism; I exploit the caching mechanism, for successive calls in the the same thread, by placing it in thread local memory; additionally the OpenSSL HMAC and EVP CTX's are also placed in the same thread local memory structure ( see this question for some detail on why I use thread local memory and the massive amount of speedup it enables even with a single thread). The generation and deserialization, "my algorithms", of these cookie strings uses intermediary void *s and std::strings and since Protocol Buffers has an internal memory retention mechanism I want these characteristics for "my algorithms". So how do I implement a common scratch memory ? I don't know much about the rdbuf(streambuf - strinbuf ??) of the std::string object. I would presumeably need to grow it to the lowest common size ever encountered during the execution of "my algorithms". Thoughts ? My question I guess would be: " is the internal buffer of a string re-usable, and if so, how ?" Edit: See comments to Vlad's answer please.

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  • LINQ Joins - Performance

    - by Meiscooldude
    I am curious on how exactly LINQ (not LINQ to SQL) is performing is joins behind the scenes in relation to how Sql Server performs joins. Sql Server before executing a query, generates an Execution Plan. The Execution Plan is basically an Expression Tree on what it believes is the best way to execute the query. Each node provides information on whether to do a Sort, Scan, Select, Join, ect. On a 'Join' node in our execution plan, we can see three possible algorithms; Hash Join, Merge Join, and Nested Loops Join. Sql Server will choose which algorithm to for each Join operation based on expected number of rows in Inner and Outer tables, what type of join we are doing (some algorithms don't support all types of joins), whether we need data ordered, and probably many other factors. Join Algorithms: Nested Loop Join: Best for small inputs, can be optimized with ordered inner table. Merge Join: Best for medium to large inputs sorted inputs, or an output that needs to be ordered. Hash Join: Best for medium to large inputs, can be parallelized to scale linearly. LINQ Query: DataTable firstTable, secondTable; ... var rows = from firstRow in firstTable.AsEnumerable () join secondRow in secondTable.AsEnumerable () on firstRow.Field<object> (randomObject.Property) equals secondRow.Field<object> (randomObject.Property) select new {firstRow, secondRow}; SQL Query: SELECT * FROM firstTable fT INNER JOIN secondTable sT ON fT.Property = sT.Property Sql Server might use a Nested Loop Join if it knows there are a small number of rows from each table, a merge join if it knows one of the tables has an index, and Hash join if it knows there are a lot of rows on either table and neither has an index. Does Linq choose its algorithm for joins? or does it always use one?

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  • Endianness and C API's: Specifically OpenSSL.

    - by Hassan Syed
    I have an algorithm that uses the following OpenSSL calls: HMAC_update() / HMAC_final() // ripe160 EVP_CipherUpdate() / EVP_CipherFinal() // cbc_blowfish These algorithm take a unsigned char * into the "plain text". My input data is comes from a C++ std::string::c_str() which originate from a protocol buffer object as a encoded UTF-8 string. UTF-8 strings are meant to be endian neutrial. However I'm a bit paranoid about how OpenSSL may perform operations on the data. My understanding is that encryption algorithms work on 8-bit blocks of data, and if a unsigned char * is used for pointer arithmetic when the operations are performed the algorithms should be endian neutral and I do not need to worry about anything. My uncertainty is compounded by the fact that I am working on a little-endian machine and have never done any real cross-architecture programming. My beliefs/reasoning are/is based on the following two properties std::string (not wstring) internally uses a 8-bit ptr and a the resulting c_str() ptr will itterate the same way regardless of the CPU architecture. Encryption algorithms are either by design, or by implementation, endian neutral. I know the best way to get a definitive answer is to use QEMU and do some cross-platform unit tests (which I plan to do). My question is a request for comments on my reasoning, and perhaps will assist other programmers when faced with similar problems.

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  • What is better for a student programming in C++ to learn for writing GUI: C# vs QT?

    - by flashnik
    I'm a teacher(instructor) of CS in the university. The course is based on Cormen and Knuth and students program algorithms in C++. But sometimes it is good to show how an algorithm works or just a result of task through GUI. Also in my opinion it's very imporant to be able to write full programs. They will have courses concerning GUI but a three years, later, in fact, before graduatuion. I think that they should be able to write simple GUI applications earlier. So I want to teach them it. How do you think, what is more useful for them to learn: programming GUI with QT or writing GUI in C# and calling unmanaged C++ library? Update. For developing C++ applications students use MS Visual studio, so C# is already installed. But QT AFAIK also can be integrated into VS. I have following pros of C# (some were suggested there in answers): The need to make an additional layer. It's more work, but it forces you explicitly specify contract between GUI and processing data. The border between GUI and algorithms becomes very clear. It's more popular among employers. At least, in Russia where we live. It's rather common to write performance-critical algorithms in C++ and PInvoke them from well-looking C# application/ASP.Net website. Maybe it is not so widespread in the rest of the world but in Russia Windows is very popular, especially in companies and corporations due to some reasons, so most of b2b applications are Windows applications. Rapid development. It's much quicker to code in .Net then in C++ due to many reasons. And the con is that it's a new language with own specific for students. And the mess with invoking calls to library.

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