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  • Evolution Of Duplicate Content

    There are many techniques involved in SEO (search engine optimization) compared in the past. Although the on-page SEO techniques remains the same, techniques used outside the website have grown and r... [Author: Margarette Mcbride - Web Design and Development - May 17, 2010]

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  • What are some techniques to monitor multiple instances of a piece of software?

    - by Geo Ego
    I have a piece of self-serve kiosk software that will be running at multiple sites. I'd like to monitor their status remotely. The kiosk application itself is pretty much finished. I am now in the process of creating a piece of software that will monitor all of the kiosks from a central location so that the customer can view particular details remotely (for instance, how many bills are in the acceptor's cash cartridge, what customer is currently logged in, etc.). Because I am in such an early stage of development, my options are quite open. I understand that I'm not giving very many qualifications, but I'd like to try to get a good variety of potential solutions. Some details: Kiosk software is a VB6 app running on Windows Embedded Monitoring software will be run on a modern desktop version of Windows (either XP, Vista, or 7) Database is SQL Server 2008 My initial idea was to develop a .NET app that would simply report the last database transaction for each kiosk at a set interval (say every second or so) but I'd really like for the kiosk software to report its status directly. I'm not exactly sure where to begin in terms of what modifications may need to be made to the kiosk software, and what the monitoring software will require. Links to articles on these topics would be most welcome.

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  • SSMS Results to Grid - CRLF not preserved in copy/paste - any better techniques?

    - by Cade Roux
    When I have a result set in the grid like: SELECT 'line 1 line 2 line 3' or SELECT 'line 1' + CHAR(13) + CHAR(10) + 'line 2' + CHAR(13) + CHAR(10) + 'line 3' With embedded CRLF, the display in the grid appears to replace them with spaces (I guess so that they will display all the data). The problem is that if I am code-generating a script, I cannot simply cut and paste this. I have to convert the code to open a cursor and print the relevant columns so that I can copy and paste them from the text results. Is there any simpler workaround to preserve the CRLF in a copy/paste operation from the results grid? The reason that the grid is helpful is that I am currently generating a number of scripts for the same object in different columns - a bcp out in one column, an xml format file in another, a table create script in another, etc...

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  • Is there any algorithm that can solve ANY traditional sudoku puzzles, WITHOUT guessing (or similar techniques)?

    - by justin
    Is there any algorithm that solves ANY traditional sudoku puzzle, WITHOUT guessing? Here Guessing means trying an candidate and see how far it goes, if a contradiction is found with the guess, backtracking to the guessing step and try another candidate; when all candidates are exhausted without success, backtracking to the previous guessing step (if there is one; otherwise the puzzle proofs invalid.), etc. EDIT1: Thank you for your replies. traditional sudoku means 81-box sudoku, without any other constraints. Let us say the we know the solution is unique, is there any algorithm that can GUARANTEE to solve it without backtracking? Backtracking is a universal tool, I have nothing wrong with it but, using a universal tool to solve sudoku decreases the value and fun in deciphering (manually, or by computer) sudoku puzzles. How can a human being solve the so called "the hardest sudoku in the world", does he need to guess? I heard some researcher accidentally found that their algorithm for some data analysis can solve all sudoku. Is that true, do they have to guess too?

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  • Which are the best techniques to protect a 'homemade' framework from unlogged visitors?

    - by Hermet
    First of all, I would like to say that I have used the search box looking for a similar question unsuccessfully, maybe because of my poor english skills. The way I currently do this is checking in every single page that a session has been opened. If not, the user gets redirected to a 404 page, to seem like the file which has been requested doesn't exist. I really don't know if this is sure or there's a better and more safety way and I'm currently working with kind of confidential data that should never become public. Could you give me some tips? Or leave a link where I could find some? Thank you very much, and again excuse me for kicking the dictionary.

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  • Which are the most useful techniques for faster Bluetooth?

    - by Mike Howard
    Hi. I'm adding peer-to-peer bluetooth using GameKit to an iPhone shoot-em-up, so speed is vital. I'm sending about 40 messages a second each way, most of them with the faster GKSendDataUnreliable, all serializing with NSCoding. In testing between a 3G and 3GS, this is slowing the 3G down a lot more than I'd like. I'm wondering where I should concentrate my efforts to speed it up. How much slower is GKSendDataReliable? For the few packets that have to get there, would it be faster to send a GKSendDataUnreliable and have the peer send an acknowledgement so I can send again if I don't get the Ack within, say, 100ms? How much faster would it be to create the NSData instance using a regular C array rather than archiving with the NSCoding protocol? Is this serialization process (for about a dozen floats) just as slow as you'd expect from an object creation/deallocation overhead, or is something particularly slow happening? I heard that (for example) sending four seperate sets of data is much, much slower, than sending one piece of data four times the size. Would I make a significant saving by sending separate packets of data that wouldn't always go together in the same packet when they happen at the same time? Are there any other bluetooth performance secrets I've missed? Thanks for your help.

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  • iPhone: Which are the most useful techniques for faster Bluetooth?

    - by Mike Howard
    Hi. I'm adding peer-to-peer bluetooth using GameKit to an iPhone shoot-em-up, so speed is vital. I'm sending about 40 messages a second each way, most of them with the faster GKSendDataUnreliable, all serializing with NSCoding. In testing between a 3G and 3GS, this is slowing the 3G down a lot more than I'd like. I'm wondering where I should concentrate my efforts to speed it up. How much slower is GKSendDataReliable? For the few packets that have to get there, would it be faster to send a GKSendDataUnreliable and have the peer send an acknowledgement so I can send again if I don't get the Ack within, say, 100ms? How much faster would it be to create the NSData instance using a regular C array rather than archiving with the NSCoding protocol? Is this serialization process (for about a dozen floats) just as slow as you'd expect from an object creation/deallocation overhead, or is something particularly slow happening? I heard that (for example) sending four seperate sets of data is much, much slower, than sending one piece of data four times the size. Would I make a significant saving by sending separate packets of data that wouldn't always go together in the same packet when they happen at the same time? Are there any other bluetooth performance secrets I've missed? Thanks for your help.

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  • Any tools or techniques for validating constraints programmatically between databases?

    - by Brandon
    If you had two databases, that had two tables between them that would normally implement a one to one (or many to many) constraint but cannot since they are separate databases, how would you validate this relationship in an application or test? Is there a simple way to do this? For example, a tool or technique that can, given a constraint type, tables and fields, does the validation. I imagine that this isn't the first time this come up so I'm hoping people can share their solution. Thanks.

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  • What techniques can I employ to create a series of UI Elements from a collection of objects using WP

    - by elggarc
    I'm new to WPF and before I dive in solving a problem in completely the wrong way I was wondering if WPF is clever enough to handle something for me. Imagine I have a collection containing objects. Each object is of the same known type and has two parameters. Name (a string) and Picked (a boolean). The collection will be populated at run time. I would like to build up a UI element at run time that will represent this collection as a series of checkboxes. I want the Picked parameter of any given object in the collection updated if the user changes the selected state of the checkbox. To me, the answer is simple. I iterate accross the collection and create a new checkbox for each object, dynamically wiring up a ValueChanged event to capture when Picked should be changed. It has occured to me, however, that I may be able to harness some unknown feature of WPF to do this better (or "properly"). For example, could data binding be employed here? I would be very interested in anyone's thoughts. Thanks, E FootNote: The structure of the collection can be changed completely to better fit any chosen solution but ultimately I will always start from, and end with, some list of string and boolean pairs.

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  • Are there libraries or techniques for collecting and weighing keywords from a block of text?

    - by Soviut
    I have a field in my database that can contain large blocks of text. I need to make this searchable but don't have the ability to use full text searching. Instead, on update, I want my business layer to process the block of text and extract keywords from it which I can save as searchable metadata. Ideally, these keywords could then be weighed based on the number of times they appear in the block of text. Naturally, words like "the", "and", "of", etc. should be discarded as they just add a lot of noise to the search. Are there tools or libraries in Python that can do this filtering or should I roll my own?

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  • Good functions and techniques for dealing with haskell tuples?

    - by toofarsideways
    I've been doing a lot of work with tuples and lists of tuples recently and I've been wondering if I'm being sensible. Things feel awkward and clunky which for me signals that I'm doing something wrong. For example I've written three convenience functions for getting the first, second and third value in a tuple of 3 values. Is there a better way I'm missing? Are there more general functions that allow you to compose and manipulate tuple data? Here are some things I am trying to do that feel should be generalisable. Extracting values: Do I need to create a version of fst,snd,etc... for tuples of size two, three, four and five, etc...? fst3(x,_,_) = x fst4(x,_,_,_) = x Manipulating values: Can you increment the last value in a list of pairs and then use that same function to increment the last value in a list of triples? Zipping and Unzipping values: There is a zip and a zip3. Do I also need a zip4? or is there some way of creating a general zip function? Sorry if this seems subjective, I honestly don't know if this is even possible or if I'm wasting my time writing 3 extra functions every time I need a general solution. Thank you for any help you can give!

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  • What techniques do you use for emitting data from the server that will solely be used in client side scripts?

    - by chuck
    Hi all, I never found an optimal solution for this problem so I am hoping that some of you out there have a few solutions. Let's say I need to render out a list of checkboxes and each checkbox has a set of additional data that goes with it. This data will be used purely in the context of javascript and jquery. My usual strategy is to render this data in hidden fields that are grouped in the same container as the checkbox. My rendered HTML will look something like this: <div> <input type="checkbox" /> <input type="hidden" class="genreId" /> <input type="hidden" class="titleId" /> </div> My only problem with this is that the data in the hidden fields get posted to the server when the form is submitted. For small amounts of data, this is fine. However, I frequently work with large datasets and a large amount of data is needlessly transferred. UPDATE: Before submitting this post, I just saw that I can add a "DISABLED" attribute to my input element to suppress the submission of data. Is this pretty much the best approach that I can take? Thanks

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  • Tellago Devlabs: A RESTful API for BizTalk Server Business Rules

    - by gsusx
    Tellago DevLabs keeps growing as the primary example of our commitment to open source! Today, we are very happy to announce the availability of the BizTalk Business Rules Data Service API which extends our existing BizTalk Data Services solution with an OData API for the BizTalk Server Business Rules engine. Tellago’s Vishal Mody led the implementation of this version of the API with some input from other members of our technical staff. The motivation The fundamental motivation behind the BRE Data...(read more)

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  • Difference between the terms Material & Effect

    - by codey
    I'm making an effect system right now (I think, because it may be a material system... or both!). The effects system follows the common (e.g. COLLADA, DirectX) effect framework abstraction of Effects have Techniques, Techniques have Passes, Passes have States & Shader Programs. An effect, according to COLLADA, defines the equations necessary for the visual appearance of geometry and screen-space image processing. Keeping with the abstraction, effects contain techniques. Each effect can contain one or many techniques (i.e. ways to generate the effect), each of which describes a different method for rendering that effect. The technique could be relate to quality (e.g. high precision, high LOD, etc.), or in-game-situation (e.g. night/day, power-up-mode, etc.). Techniques hold a description of the textures, samplers, shaders, parameters, & passes necessary for rendering this effect using one method. Some algorithms require several passes to render the effect. Pipeline descriptions are broken into an ordered collection of Pass objects. A pass provides a static declaration of all the render states, shaders, & settings for "one rendering pipeline" (i.e. one pass). Meshes usually contain a series of materials that define the model. According to the COLLADA spec (again), a material instantiates an effect, fills its parameters with values, & selects a technique. But I see material defined differently in other places, such as just the Lambert, Blinn, Phong "material types/shaded surfaces", or as Metal, Plastic, Wood, etc. In game dev forums, people often talk about implementing a "material/effect system". Is the material not an instance of an effect? Ergo, if I had effect objects, stored in a collection, & each effect instance object with there own parameter setting, then there is no need for the concept of a material... Or am I interpreting it wrong? Please help by contributing your interpretations as I want to be clear on a distinction (if any), & don't want to miss out on the concept of a material if it should be implemented to follow the abstraction of the DirectX FX framework & COLLADA definitions closely.

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  • How John Got 15x Improvement Without Really Trying

    - by rchrd
    The following article was published on a Sun Microsystems website a number of years ago by John Feo. It is still useful and worth preserving. So I'm republishing it here.  How I Got 15x Improvement Without Really Trying John Feo, Sun Microsystems Taking ten "personal" program codes used in scientific and engineering research, the author was able to get from 2 to 15 times performance improvement easily by applying some simple general optimization techniques. Introduction Scientific research based on computer simulation depends on the simulation for advancement. The research can advance only as fast as the computational codes can execute. The codes' efficiency determines both the rate and quality of results. In the same amount of time, a faster program can generate more results and can carry out a more detailed simulation of physical phenomena than a slower program. Highly optimized programs help science advance quickly and insure that monies supporting scientific research are used as effectively as possible. Scientific computer codes divide into three broad categories: ISV, community, and personal. ISV codes are large, mature production codes developed and sold commercially. The codes improve slowly over time both in methods and capabilities, and they are well tuned for most vendor platforms. Since the codes are mature and complex, there are few opportunities to improve their performance solely through code optimization. Improvements of 10% to 15% are typical. Examples of ISV codes are DYNA3D, Gaussian, and Nastran. Community codes are non-commercial production codes used by a particular research field. Generally, they are developed and distributed by a single academic or research institution with assistance from the community. Most users just run the codes, but some develop new methods and extensions that feed back into the general release. The codes are available on most vendor platforms. Since these codes are younger than ISV codes, there are more opportunities to optimize the source code. Improvements of 50% are not unusual. Examples of community codes are AMBER, CHARM, BLAST, and FASTA. Personal codes are those written by single users or small research groups for their own use. These codes are not distributed, but may be passed from professor-to-student or student-to-student over several years. They form the primordial ocean of applications from which community and ISV codes emerge. Government research grants pay for the development of most personal codes. This paper reports on the nature and performance of this class of codes. Over the last year, I have looked at over two dozen personal codes from more than a dozen research institutions. The codes cover a variety of scientific fields, including astronomy, atmospheric sciences, bioinformatics, biology, chemistry, geology, and physics. The sources range from a few hundred lines to more than ten thousand lines, and are written in Fortran, Fortran 90, C, and C++. For the most part, the codes are modular, documented, and written in a clear, straightforward manner. They do not use complex language features, advanced data structures, programming tricks, or libraries. I had little trouble understanding what the codes did or how data structures were used. Most came with a makefile. Surprisingly, only one of the applications is parallel. All developers have access to parallel machines, so availability is not an issue. Several tried to parallelize their applications, but stopped after encountering difficulties. Lack of education and a perception that parallelism is difficult prevented most from trying. I parallelized several of the codes using OpenMP, and did not judge any of the codes as difficult to parallelize. Even more surprising than the lack of parallelism is the inefficiency of the codes. I was able to get large improvements in performance in a matter of a few days applying simple optimization techniques. Table 1 lists ten representative codes [names and affiliation are omitted to preserve anonymity]. Improvements on one processor range from 2x to 15.5x with a simple average of 4.75x. I did not use sophisticated performance tools or drill deep into the program's execution character as one would do when tuning ISV or community codes. Using only a profiler and source line timers, I identified inefficient sections of code and improved their performance by inspection. The changes were at a high level. I am sure there is another factor of 2 or 3 in each code, and more if the codes are parallelized. The study’s results show that personal scientific codes are running many times slower than they should and that the problem is pervasive. Computational scientists are not sloppy programmers; however, few are trained in the art of computer programming or code optimization. I found that most have a working knowledge of some programming language and standard software engineering practices; but they do not know, or think about, how to make their programs run faster. They simply do not know the standard techniques used to make codes run faster. In fact, they do not even perceive that such techniques exist. The case studies described in this paper show that applying simple, well known techniques can significantly increase the performance of personal codes. It is important that the scientific community and the Government agencies that support scientific research find ways to better educate academic scientific programmers. The inefficiency of their codes is so bad that it is retarding both the quality and progress of scientific research. # cacheperformance redundantoperations loopstructures performanceimprovement 1 x x 15.5 2 x 2.8 3 x x 2.5 4 x 2.1 5 x x 2.0 6 x 5.0 7 x 5.8 8 x 6.3 9 2.2 10 x x 3.3 Table 1 — Area of improvement and performance gains of 10 codes The remainder of the paper is organized as follows: sections 2, 3, and 4 discuss the three most common sources of inefficiencies in the codes studied. These are cache performance, redundant operations, and loop structures. Each section includes several examples. The last section summaries the work and suggests a possible solution to the issues raised. Optimizing cache performance Commodity microprocessor systems use caches to increase memory bandwidth and reduce memory latencies. Typical latencies from processor to L1, L2, local, and remote memory are 3, 10, 50, and 200 cycles, respectively. Moreover, bandwidth falls off dramatically as memory distances increase. Programs that do not use cache effectively run many times slower than programs that do. When optimizing for cache, the biggest performance gains are achieved by accessing data in cache order and reusing data to amortize the overhead of cache misses. Secondary considerations are prefetching, associativity, and replacement; however, the understanding and analysis required to optimize for the latter are probably beyond the capabilities of the non-expert. Much can be gained simply by accessing data in the correct order and maximizing data reuse. 6 out of the 10 codes studied here benefited from such high level optimizations. Array Accesses The most important cache optimization is the most basic: accessing Fortran array elements in column order and C array elements in row order. Four of the ten codes—1, 2, 4, and 10—got it wrong. Compilers will restructure nested loops to optimize cache performance, but may not do so if the loop structure is too complex, or the loop body includes conditionals, complex addressing, or function calls. In code 1, the compiler failed to invert a key loop because of complex addressing do I = 0, 1010, delta_x IM = I - delta_x IP = I + delta_x do J = 5, 995, delta_x JM = J - delta_x JP = J + delta_x T1 = CA1(IP, J) + CA1(I, JP) T2 = CA1(IM, J) + CA1(I, JM) S1 = T1 + T2 - 4 * CA1(I, J) CA(I, J) = CA1(I, J) + D * S1 end do end do In code 2, the culprit is conditionals do I = 1, N do J = 1, N If (IFLAG(I,J) .EQ. 0) then T1 = Value(I, J-1) T2 = Value(I-1, J) T3 = Value(I, J) T4 = Value(I+1, J) T5 = Value(I, J+1) Value(I,J) = 0.25 * (T1 + T2 + T5 + T4) Delta = ABS(T3 - Value(I,J)) If (Delta .GT. MaxDelta) MaxDelta = Delta endif enddo enddo I fixed both programs by inverting the loops by hand. Code 10 has three-dimensional arrays and triply nested loops. The structure of the most computationally intensive loops is too complex to invert automatically or by hand. The only practical solution is to transpose the arrays so that the dimension accessed by the innermost loop is in cache order. The arrays can be transposed at construction or prior to entering a computationally intensive section of code. The former requires all array references to be modified, while the latter is cost effective only if the cost of the transpose is amortized over many accesses. I used the second approach to optimize code 10. Code 5 has four-dimensional arrays and loops are nested four deep. For all of the reasons cited above the compiler is not able to restructure three key loops. Assume C arrays and let the four dimensions of the arrays be i, j, k, and l. In the original code, the index structure of the three loops is L1: for i L2: for i L3: for i for l for l for j for k for j for k for j for k for l So only L3 accesses array elements in cache order. L1 is a very complex loop—much too complex to invert. I brought the loop into cache alignment by transposing the second and fourth dimensions of the arrays. Since the code uses a macro to compute all array indexes, I effected the transpose at construction and changed the macro appropriately. The dimensions of the new arrays are now: i, l, k, and j. L3 is a simple loop and easily inverted. L2 has a loop-carried scalar dependence in k. By promoting the scalar name that carries the dependence to an array, I was able to invert the third and fourth subloops aligning the loop with cache. Code 5 is by far the most difficult of the four codes to optimize for array accesses; but the knowledge required to fix the problems is no more than that required for the other codes. I would judge this code at the limits of, but not beyond, the capabilities of appropriately trained computational scientists. Array Strides When a cache miss occurs, a line (64 bytes) rather than just one word is loaded into the cache. If data is accessed stride 1, than the cost of the miss is amortized over 8 words. Any stride other than one reduces the cost savings. Two of the ten codes studied suffered from non-unit strides. The codes represent two important classes of "strided" codes. Code 1 employs a multi-grid algorithm to reduce time to convergence. The grids are every tenth, fifth, second, and unit element. Since time to convergence is inversely proportional to the distance between elements, coarse grids converge quickly providing good starting values for finer grids. The better starting values further reduce the time to convergence. The downside is that grids of every nth element, n > 1, introduce non-unit strides into the computation. In the original code, much of the savings of the multi-grid algorithm were lost due to this problem. I eliminated the problem by compressing (copying) coarse grids into continuous memory, and rewriting the computation as a function of the compressed grid. On convergence, I copied the final values of the compressed grid back to the original grid. The savings gained from unit stride access of the compressed grid more than paid for the cost of copying. Using compressed grids, the loop from code 1 included in the previous section becomes do j = 1, GZ do i = 1, GZ T1 = CA(i+0, j-1) + CA(i-1, j+0) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) S1 = T1 + T4 - 4 * CA1(i+0, j+0) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 enddo enddo where CA and CA1 are compressed arrays of size GZ. Code 7 traverses a list of objects selecting objects for later processing. The labels of the selected objects are stored in an array. The selection step has unit stride, but the processing steps have irregular stride. A fix is to save the parameters of the selected objects in temporary arrays as they are selected, and pass the temporary arrays to the processing functions. The fix is practical if the same parameters are used in selection as in processing, or if processing comprises a series of distinct steps which use overlapping subsets of the parameters. Both conditions are true for code 7, so I achieved significant improvement by copying parameters to temporary arrays during selection. Data reuse In the previous sections, we optimized for spatial locality. It is also important to optimize for temporal locality. Once read, a datum should be used as much as possible before it is forced from cache. Loop fusion and loop unrolling are two techniques that increase temporal locality. Unfortunately, both techniques increase register pressure—as loop bodies become larger, the number of registers required to hold temporary values grows. Once register spilling occurs, any gains evaporate quickly. For multiprocessors with small register sets or small caches, the sweet spot can be very small. In the ten codes presented here, I found no opportunities for loop fusion and only two opportunities for loop unrolling (codes 1 and 3). In code 1, unrolling the outer and inner loop one iteration increases the number of result values computed by the loop body from 1 to 4, do J = 1, GZ-2, 2 do I = 1, GZ-2, 2 T1 = CA1(i+0, j-1) + CA1(i-1, j+0) T2 = CA1(i+1, j-1) + CA1(i+0, j+0) T3 = CA1(i+0, j+0) + CA1(i-1, j+1) T4 = CA1(i+1, j+0) + CA1(i+0, j+1) T5 = CA1(i+2, j+0) + CA1(i+1, j+1) T6 = CA1(i+1, j+1) + CA1(i+0, j+2) T7 = CA1(i+2, j+1) + CA1(i+1, j+2) S1 = T1 + T4 - 4 * CA1(i+0, j+0) S2 = T2 + T5 - 4 * CA1(i+1, j+0) S3 = T3 + T6 - 4 * CA1(i+0, j+1) S4 = T4 + T7 - 4 * CA1(i+1, j+1) CA(i+0, j+0) = CA1(i+0, j+0) + DD * S1 CA(i+1, j+0) = CA1(i+1, j+0) + DD * S2 CA(i+0, j+1) = CA1(i+0, j+1) + DD * S3 CA(i+1, j+1) = CA1(i+1, j+1) + DD * S4 enddo enddo The loop body executes 12 reads, whereas as the rolled loop shown in the previous section executes 20 reads to compute the same four values. In code 3, two loops are unrolled 8 times and one loop is unrolled 4 times. Here is the before for (k = 0; k < NK[u]; k++) { sum = 0.0; for (y = 0; y < NY; y++) { sum += W[y][u][k] * delta[y]; } backprop[i++]=sum; } and after code for (k = 0; k < KK - 8; k+=8) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (y = 0; y < NY; y++) { sum0 += W[y][0][k+0] * delta[y]; sum1 += W[y][0][k+1] * delta[y]; sum2 += W[y][0][k+2] * delta[y]; sum3 += W[y][0][k+3] * delta[y]; sum4 += W[y][0][k+4] * delta[y]; sum5 += W[y][0][k+5] * delta[y]; sum6 += W[y][0][k+6] * delta[y]; sum7 += W[y][0][k+7] * delta[y]; } backprop[k+0] = sum0; backprop[k+1] = sum1; backprop[k+2] = sum2; backprop[k+3] = sum3; backprop[k+4] = sum4; backprop[k+5] = sum5; backprop[k+6] = sum6; backprop[k+7] = sum7; } for one of the loops unrolled 8 times. Optimizing for temporal locality is the most difficult optimization considered in this paper. The concepts are not difficult, but the sweet spot is small. Identifying where the program can benefit from loop unrolling or loop fusion is not trivial. Moreover, it takes some effort to get it right. Still, educating scientific programmers about temporal locality and teaching them how to optimize for it will pay dividends. Reducing instruction count Execution time is a function of instruction count. Reduce the count and you usually reduce the time. The best solution is to use a more efficient algorithm; that is, an algorithm whose order of complexity is smaller, that converges quicker, or is more accurate. Optimizing source code without changing the algorithm yields smaller, but still significant, gains. This paper considers only the latter because the intent is to study how much better codes can run if written by programmers schooled in basic code optimization techniques. The ten codes studied benefited from three types of "instruction reducing" optimizations. The two most prevalent were hoisting invariant memory and data operations out of inner loops. The third was eliminating unnecessary data copying. The nature of these inefficiencies is language dependent. Memory operations The semantics of C make it difficult for the compiler to determine all the invariant memory operations in a loop. The problem is particularly acute for loops in functions since the compiler may not know the values of the function's parameters at every call site when compiling the function. Most compilers support pragmas to help resolve ambiguities; however, these pragmas are not comprehensive and there is no standard syntax. To guarantee that invariant memory operations are not executed repetitively, the user has little choice but to hoist the operations by hand. The problem is not as severe in Fortran programs because in the absence of equivalence statements, it is a violation of the language's semantics for two names to share memory. Codes 3 and 5 are C programs. In both cases, the compiler did not hoist all invariant memory operations from inner loops. Consider the following loop from code 3 for (y = 0; y < NY; y++) { i = 0; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += delta[y] * I1[i++]; } } } Since dW[y][u] can point to the same memory space as delta for one or more values of y and u, assignment to dW[y][u][k] may change the value of delta[y]. In reality, dW and delta do not overlap in memory, so I rewrote the loop as for (y = 0; y < NY; y++) { i = 0; Dy = delta[y]; for (u = 0; u < NU; u++) { for (k = 0; k < NK[u]; k++) { dW[y][u][k] += Dy * I1[i++]; } } } Failure to hoist invariant memory operations may be due to complex address calculations. If the compiler can not determine that the address calculation is invariant, then it can hoist neither the calculation nor the associated memory operations. As noted above, code 5 uses a macro to address four-dimensional arrays #define MAT4D(a,q,i,j,k) (double *)((a)->data + (q)*(a)->strides[0] + (i)*(a)->strides[3] + (j)*(a)->strides[2] + (k)*(a)->strides[1]) The macro is too complex for the compiler to understand and so, it does not identify any subexpressions as loop invariant. The simplest way to eliminate the address calculation from the innermost loop (over i) is to define a0 = MAT4D(a,q,0,j,k) before the loop and then replace all instances of *MAT4D(a,q,i,j,k) in the loop with a0[i] A similar problem appears in code 6, a Fortran program. The key loop in this program is do n1 = 1, nh nx1 = (n1 - 1) / nz + 1 nz1 = n1 - nz * (nx1 - 1) do n2 = 1, nh nx2 = (n2 - 1) / nz + 1 nz2 = n2 - nz * (nx2 - 1) ndx = nx2 - nx1 ndy = nz2 - nz1 gxx = grn(1,ndx,ndy) gyy = grn(2,ndx,ndy) gxy = grn(3,ndx,ndy) balance(n1,1) = balance(n1,1) + (force(n2,1) * gxx + force(n2,2) * gxy) * h1 balance(n1,2) = balance(n1,2) + (force(n2,1) * gxy + force(n2,2) * gyy)*h1 end do end do The programmer has written this loop well—there are no loop invariant operations with respect to n1 and n2. However, the loop resides within an iterative loop over time and the index calculations are independent with respect to time. Trading space for time, I precomputed the index values prior to the entering the time loop and stored the values in two arrays. I then replaced the index calculations with reads of the arrays. Data operations Ways to reduce data operations can appear in many forms. Implementing a more efficient algorithm produces the biggest gains. The closest I came to an algorithm change was in code 4. This code computes the inner product of K-vectors A(i) and B(j), 0 = i < N, 0 = j < M, for most values of i and j. Since the program computes most of the NM possible inner products, it is more efficient to compute all the inner products in one triply-nested loop rather than one at a time when needed. The savings accrue from reading A(i) once for all B(j) vectors and from loop unrolling. for (i = 0; i < N; i+=8) { for (j = 0; j < M; j++) { sum0 = 0.0; sum1 = 0.0; sum2 = 0.0; sum3 = 0.0; sum4 = 0.0; sum5 = 0.0; sum6 = 0.0; sum7 = 0.0; for (k = 0; k < K; k++) { sum0 += A[i+0][k] * B[j][k]; sum1 += A[i+1][k] * B[j][k]; sum2 += A[i+2][k] * B[j][k]; sum3 += A[i+3][k] * B[j][k]; sum4 += A[i+4][k] * B[j][k]; sum5 += A[i+5][k] * B[j][k]; sum6 += A[i+6][k] * B[j][k]; sum7 += A[i+7][k] * B[j][k]; } C[i+0][j] = sum0; C[i+1][j] = sum1; C[i+2][j] = sum2; C[i+3][j] = sum3; C[i+4][j] = sum4; C[i+5][j] = sum5; C[i+6][j] = sum6; C[i+7][j] = sum7; }} This change requires knowledge of a typical run; i.e., that most inner products are computed. The reasons for the change, however, derive from basic optimization concepts. It is the type of change easily made at development time by a knowledgeable programmer. In code 5, we have the data version of the index optimization in code 6. Here a very expensive computation is a function of the loop indices and so cannot be hoisted out of the loop; however, the computation is invariant with respect to an outer iterative loop over time. We can compute its value for each iteration of the computation loop prior to entering the time loop and save the values in an array. The increase in memory required to store the values is small in comparison to the large savings in time. The main loop in Code 8 is doubly nested. The inner loop includes a series of guarded computations; some are a function of the inner loop index but not the outer loop index while others are a function of the outer loop index but not the inner loop index for (j = 0; j < N; j++) { for (i = 0; i < M; i++) { r = i * hrmax; R = A[j]; temp = (PRM[3] == 0.0) ? 1.0 : pow(r, PRM[3]); high = temp * kcoeff * B[j] * PRM[2] * PRM[4]; low = high * PRM[6] * PRM[6] / (1.0 + pow(PRM[4] * PRM[6], 2.0)); kap = (R > PRM[6]) ? high * R * R / (1.0 + pow(PRM[4]*r, 2.0) : low * pow(R/PRM[6], PRM[5]); < rest of loop omitted > }} Note that the value of temp is invariant to j. Thus, we can hoist the computation for temp out of the loop and save its values in an array. for (i = 0; i < M; i++) { r = i * hrmax; TEMP[i] = pow(r, PRM[3]); } [N.B. – the case for PRM[3] = 0 is omitted and will be reintroduced later.] We now hoist out of the inner loop the computations invariant to i. Since the conditional guarding the value of kap is invariant to i, it behooves us to hoist the computation out of the inner loop, thereby executing the guard once rather than M times. The final version of the code is for (j = 0; j < N; j++) { R = rig[j] / 1000.; tmp1 = kcoeff * par[2] * beta[j] * par[4]; tmp2 = 1.0 + (par[4] * par[4] * par[6] * par[6]); tmp3 = 1.0 + (par[4] * par[4] * R * R); tmp4 = par[6] * par[6] / tmp2; tmp5 = R * R / tmp3; tmp6 = pow(R / par[6], par[5]); if ((par[3] == 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp5; } else if ((par[3] == 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * tmp4 * tmp6; } else if ((par[3] != 0.0) && (R > par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp5; } else if ((par[3] != 0.0) && (R <= par[6])) { for (i = 1; i <= imax1; i++) KAP[i] = tmp1 * TEMP[i] * tmp4 * tmp6; } for (i = 0; i < M; i++) { kap = KAP[i]; r = i * hrmax; < rest of loop omitted > } } Maybe not the prettiest piece of code, but certainly much more efficient than the original loop, Copy operations Several programs unnecessarily copy data from one data structure to another. This problem occurs in both Fortran and C programs, although it manifests itself differently in the two languages. Code 1 declares two arrays—one for old values and one for new values. At the end of each iteration, the array of new values is copied to the array of old values to reset the data structures for the next iteration. This problem occurs in Fortran programs not included in this study and in both Fortran 77 and Fortran 90 code. Introducing pointers to the arrays and swapping pointer values is an obvious way to eliminate the copying; but pointers is not a feature that many Fortran programmers know well or are comfortable using. An easy solution not involving pointers is to extend the dimension of the value array by 1 and use the last dimension to differentiate between arrays at different times. For example, if the data space is N x N, declare the array (N, N, 2). Then store the problem’s initial values in (_, _, 2) and define the scalar names new = 2 and old = 1. At the start of each iteration, swap old and new to reset the arrays. The old–new copy problem did not appear in any C program. In programs that had new and old values, the code swapped pointers to reset data structures. Where unnecessary coping did occur is in structure assignment and parameter passing. Structures in C are handled much like scalars. Assignment causes the data space of the right-hand name to be copied to the data space of the left-hand name. Similarly, when a structure is passed to a function, the data space of the actual parameter is copied to the data space of the formal parameter. If the structure is large and the assignment or function call is in an inner loop, then copying costs can grow quite large. While none of the ten programs considered here manifested this problem, it did occur in programs not included in the study. A simple fix is always to refer to structures via pointers. Optimizing loop structures Since scientific programs spend almost all their time in loops, efficient loops are the key to good performance. Conditionals, function calls, little instruction level parallelism, and large numbers of temporary values make it difficult for the compiler to generate tightly packed, highly efficient code. Conditionals and function calls introduce jumps that disrupt code flow. Users should eliminate or isolate conditionls to their own loops as much as possible. Often logical expressions can be substituted for if-then-else statements. For example, code 2 includes the following snippet MaxDelta = 0.0 do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) if (Delta > MaxDelta) MaxDelta = Delta enddo enddo if (MaxDelta .gt. 0.001) goto 200 Since the only use of MaxDelta is to control the jump to 200 and all that matters is whether or not it is greater than 0.001, I made MaxDelta a boolean and rewrote the snippet as MaxDelta = .false. do J = 1, N do I = 1, M < code omitted > Delta = abs(OldValue ? NewValue) MaxDelta = MaxDelta .or. (Delta .gt. 0.001) enddo enddo if (MaxDelta) goto 200 thereby, eliminating the conditional expression from the inner loop. A microprocessor can execute many instructions per instruction cycle. Typically, it can execute one or more memory, floating point, integer, and jump operations. To be executed simultaneously, the operations must be independent. Thick loops tend to have more instruction level parallelism than thin loops. Moreover, they reduce memory traffice by maximizing data reuse. Loop unrolling and loop fusion are two techniques to increase the size of loop bodies. Several of the codes studied benefitted from loop unrolling, but none benefitted from loop fusion. This observation is not too surpising since it is the general tendency of programmers to write thick loops. As loops become thicker, the number of temporary values grows, increasing register pressure. If registers spill, then memory traffic increases and code flow is disrupted. A thick loop with many temporary values may execute slower than an equivalent series of thin loops. The biggest gain will be achieved if the thick loop can be split into a series of independent loops eliminating the need to write and read temporary arrays. I found such an occasion in code 10 where I split the loop do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do into two disjoint loops do i = 1, n do j = 1, m A24(j,i)= S24(j,i) * T24(j,i) + S25(j,i) * U25(j,i) B24(j,i)= S24(j,i) * T25(j,i) + S25(j,i) * U24(j,i) A25(j,i)= S24(j,i) * C24(j,i) + S25(j,i) * V24(j,i) B25(j,i)= S24(j,i) * U25(j,i) + S25(j,i) * V25(j,i) end do end do do i = 1, n do j = 1, m C24(j,i)= S26(j,i) * T26(j,i) + S27(j,i) * U26(j,i) D24(j,i)= S26(j,i) * T27(j,i) + S27(j,i) * V26(j,i) C25(j,i)= S27(j,i) * S28(j,i) + S26(j,i) * U28(j,i) D25(j,i)= S27(j,i) * T28(j,i) + S26(j,i) * V28(j,i) end do end do Conclusions Over the course of the last year, I have had the opportunity to work with over two dozen academic scientific programmers at leading research universities. Their research interests span a broad range of scientific fields. Except for two programs that relied almost exclusively on library routines (matrix multiply and fast Fourier transform), I was able to improve significantly the single processor performance of all codes. Improvements range from 2x to 15.5x with a simple average of 4.75x. Changes to the source code were at a very high level. I did not use sophisticated techniques or programming tools to discover inefficiencies or effect the changes. Only one code was parallel despite the availability of parallel systems to all developers. Clearly, we have a problem—personal scientific research codes are highly inefficient and not running parallel. The developers are unaware of simple optimization techniques to make programs run faster. They lack education in the art of code optimization and parallel programming. I do not believe we can fix the problem by publishing additional books or training manuals. To date, the developers in questions have not studied the books or manual available, and are unlikely to do so in the future. Short courses are a possible solution, but I believe they are too concentrated to be much use. The general concepts can be taught in a three or four day course, but that is not enough time for students to practice what they learn and acquire the experience to apply and extend the concepts to their codes. Practice is the key to becoming proficient at optimization. I recommend that graduate students be required to take a semester length course in optimization and parallel programming. We would never give someone access to state-of-the-art scientific equipment costing hundreds of thousands of dollars without first requiring them to demonstrate that they know how to use the equipment. Yet the criterion for time on state-of-the-art supercomputers is at most an interesting project. Requestors are never asked to demonstrate that they know how to use the system, or can use the system effectively. A semester course would teach them the required skills. Government agencies that fund academic scientific research pay for most of the computer systems supporting scientific research as well as the development of most personal scientific codes. These agencies should require graduate schools to offer a course in optimization and parallel programming as a requirement for funding. About the Author John Feo received his Ph.D. in Computer Science from The University of Texas at Austin in 1986. After graduate school, Dr. Feo worked at Lawrence Livermore National Laboratory where he was the Group Leader of the Computer Research Group and principal investigator of the Sisal Language Project. In 1997, Dr. Feo joined Tera Computer Company where he was project manager for the MTA, and oversaw the programming and evaluation of the MTA at the San Diego Supercomputer Center. In 2000, Dr. Feo joined Sun Microsystems as an HPC application specialist. He works with university research groups to optimize and parallelize scientific codes. Dr. Feo has published over two dozen research articles in the areas of parallel parallel programming, parallel programming languages, and application performance.

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  • What are the most common AI systems implemented in Tower Defense Games

    - by the_Dan
    I'm currently in the middle of researching on the various types of AI techniques used in tower defense type games. If someone could be help me in understanding the different types of techniques and their associated advantages. Using Google I already found several techniques. Random Map traversal Path finding e.g. Cost based Traversing Algorithms i.e. A* I have already found a great answer to this type of question with the below link, but I feel that this answer is tailored to FPS. If anyone could add to this and make it specific to tower defense games then I would be truly great-full. How is AI most commonly implemented in popular games? Example of such games would be: Radiant Defense Plant Vs Zombies - Not truly Intelligent, but there must be an AI system used right? Field Runners Edit: After further research I found an interesting book that may be useful: http://www.amazon.com/dp/0123747317/?tag=stackoverfl08-20

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  • Why is cleverness considered harmful in programming by some people?

    - by Larry Coleman
    I've noticed a lot of questions lately relating to different abstraction techniques, and answers saying basically that the techniques in question are "too clever." I would think that part of our jobs as programmers is to determine the best solutions to the problems we are given to solve, and cleverness is helpful in doing that. So my question is: are the people who think certain abstraction techniques are too clever opposed to cleverness per se, or is there some other reason for the objection? EDIT: This parser combinator is an example of what I would consider to be clever code. I downloaded this and looked it over for about half an hour. Then I stepped through the macro expansion on paper and saw the light. Now that I understand it, it seems much more elegant than the Haskell parser combinator.

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