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  • Computer Games Technolgy or Software Engineering?

    - by Suleman Anwar
    I'm in the last year of my college and going to university next year. Could you tell me what the difference between Software Engineering and Computer Games Technology is? I know a bit of both but don't know the actual difference. I'm kind off in a dilemma between these two. I want to be a programmer, I'd love to go into gaming but I heard getting a job within a computer games company is really hard.

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  • Enterprise VS Regular corporate developer

    - by Rick Ratayczak
    Ok, I "almost" lost a job offer because I "didn't have enough experience as an enterprise software engineer". I've been a programmer for over 16 years, and the last 12-14 professionally, at companies big and small. So this made me think of this question: What's the difference between a software engineer and an enterprise software engineer? Is there really a difference between software architecture and enterprise architecture? BTW: I try to do what every other GOOD software programmer does, like architecture, tdd, SDLC, etc.

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  • Java Terms that are Plagued with Vaguery

    - by Adam Tannon
    What's the difference between a Java Process (what your OS sees) and a JVM? Are they one in the same or are they actually different? How are the JRE and JDK different (in purpose and file content), and which one contains the libraries for Java SE? What's the difference between the Java "Runtime" and a JVM? These are questions I've been asking myself (and colleagues) for years and everybody seems to have very different answers.

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  • Importance of Keywords in Anchor Text or Title Text

    Keywords are indisputably, the single most important element of an anchor text. Keywords or keyphrases placed properly on the webpage can make all the difference when it comes to search engine positioning of any website. It has been seen that mere tweaking of keywords or keyphrases has made a remarkable difference in the ranking of the website in major search engines.

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  • GZipped Images - Is It Worth?

    - by charlie
    Most image formats are already compressed. But in fact, if I take an image and compress it [gzipping it], and then I compare the compressed one to the uncompressed one, there is a difference in size, even though not such a dramatic difference. The question is: is it worth gzipping images? the content size flushed down to the client's browser will be smaller, but there will be some client overhead when de-gzipping it. Please advise.

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  • Div vs span in html !

    - by Anirudha
    Originally posted on: http://geekswithblogs.net/anirugu/archive/2013/07/24/div-vs-span-in-html.aspxThere is many difference between Div and html.   Div is block element and span is inline. If you give padding to div it will work but not to span without set display :block or :inline-block You can use span inside div but not div inside span. If you tried it you can see code is invalid. Read this discussion on SO http://stackoverflow.com/questions/183532/what-is-the-difference-between-html-tags-div-and-span

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  • 5 Common Questions About SEO Web Hosting

    Few marketers realize how much of a difference a good SEO web host can make on your rankings. Here are five of the most commonly asked questions when it comes to SEO Web Hosting. Read these carefully, as they may make the difference between a front page ranking and not showing up at all.

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  • Developer's PC - worth getting more than 8GB RAM?

    - by Borek
    I'm building a developer PC and am wondering whether to get 8GB or 12GB. It's a Core-i7 860 system, i.e., 1156 motherboards with 4 slots for RAM sticks, dual channel, usually up 16GB (as opposed to 1366 sockets where 6 banks / triple-channel are used). 8GB would be cheaper to get especially because price per GB is lower with 4x2GB compared to 2x4GB. Also the availability is worse for 4GB DIMMs here where I live; those are the main practical advantages of 8GB. (Edit: I should have stressed the price difference more - in the eshop I'm buying from, the difference between 12GB and 8GB is so big that I could almost buy a whole new netbook for it.) However, I understand that more RAM can never do harm which is the point of this question - how much of a difference will 12GB make as opposed to 8GB? Honestly, I've always been on 3.2GB systems (4GB but 32bit system) and never felt much pain from having too little memory - of course there could be more but for instance compiler's performance was usually held back by slow I/O or not utilizing multiple cores on my CPU. Still, I'm not questioning that 8GB will be useful, however, I'm not sure about the additional 4GB difference between 8 and 12 gig. Anyone has experience with 8GB / 12GB systems? The software I usually run all the time: Visual Studio or Eclipse (both should be fine with ~2GB RAM, after that I feel their performance is I/O bound) Firefox (it can never have enough RAM can it? :) Office (~500MB RAM should be enough) ... and then some smaller apps like Skype, other browsers, some background services etc.

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  • New build won't boot: some fans turning, no beep or video

    - by Dave
    When my new system is powered up, the case fan and power supply fans turn fine. The CPU fan twitches, but never gets going. Although I've heard that with AMDs and Gigabyte motherboards that is not necessary a problem. Hard drive is spinning. However, there is absolutely no indication that anything else is happening. The motherboard, as far as I can tell, does not have an internal speaker, but I harvested one from another machine and plugged it in and still no beeps at all. The monitor screen stays black, on both the integrated VGA and DVI. This is a brand new build, and has never successfully booted. My parts are: AMD Athlon II X2 245 Regor 2.9GHz Socket AM3 65W Dual-Core Processor Model ADX245OCGQBOX - includes CPU cooler) GIGABYTE GA-MA785GPMT-UD2H AM3 AMD 785G HDMI Micro ATX AMD Motherboard - Retail G.SKILL Ripjaws Series 4GB (2 x 2GB) 240-Pin DDR3 SDRAM DDR3 1333 (PC3 10666) Desktop Memory Model F3-10666CL8D-4GBRM - Retail CORSAIR CMPSU-400CX 400W ATX12V V2.2 80 PLUS Certified Compatible with Core i7 Power Supply - Retail SAMSUNG Spinpoint F3 HD502HJ 500GB 7200 RPM SATA 3.0Gb/s 3.5" Internal Hard Drive -Bare Drive COOLER MASTER Elite 341 RC-341C-KKN1-GP Black Steel MicroATX Mid Tower Computer Case - Retail I also have a DVD burner, but it acts the same whether that is plugged in or not. I'm using the on board video. What I've tried so far: I've switched power supplies, with no difference. I've tried different monitors (of which all are working on other machines) with no difference. I have tried putting it one memory module at a time, with no difference. I have tried the absolute minimum I can think of (power supply into motherboard, power button ONLY plugged into front panel, CPU fan plugged in), with no difference. I appreciate any ideas anyone might have. Do I need to RMA the motherboard? This is my first build, so there might be something obvious. I was very careful in assembly with static; I'm confident nothing was zapped during assembly.

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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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  • Hopping/Tumbling Windows Could Introduce Latency.

    This is a pre-article to one I am going to be writing on adjusting an event’s time and duration to satisfy business process requirements but it is one that I think is really useful when understanding the way that Hopping/Tumbling windows work within StreamInsight.  A Tumbling window is just a special shortcut version of  a Hopping window where the width of the window is equal to the size of the hop Here is the simplest and often used definition for a Hopping Window.  You can find them all here public static CepWindowStream<CepWindow<TPayload>> HoppingWindow<TPayload>(     this CepStream<TPayload> source,     TimeSpan windowSize,     TimeSpan hopSize,     WindowInputPolicy inputPolicy,     HoppingWindowOutputPolicy outputPolicy )   And here is the definition for a Tumbling Window public static CepWindowStream<CepWindow<TPayload>> TumblingWindow<TPayload>(     this CepStream<TPayload> source,     TimeSpan windowSize,     WindowInputPolicy inputPolicy,     HoppingWindowOutputPolicy outputPolicy )   These methods allow you to group events into windows of a temporal size.  It is a really useful and simple feature in StreamInsight.  One of the downsides though is that the windows cannot be flushed until an event in a following window occurs.  This means that you will potentially never see some events or see them with a delay.  Let me explain. Remember that a stream is a potentially unbounded sequence of events. Events in StreamInsight are given a StartTime.  It is this StartTime that is used to calculate into which temporal window an event falls.  It is best practice to assign a timestamp from the source system and not one from the system clock on the processing server.  StreamInsight cannot know when a window is over.  It cannot tell whether you have received all events in the window or whether some events have been delayed which means that StreamInsight cannot flush the stream for you.   Imagine you have events with the following Timestamps 12:10:10 PM 12:10:20 PM 12:10:35 PM 12:10:45 PM 11:59:59 PM And imagine that you have defined a 1 minute Tumbling Window over this stream using the following syntax var HoppingStream = from shift in inputStream.TumblingWindow(TimeSpan.FromMinutes(1),HoppingWindowOutputPolicy.ClipToWindowEnd) select new WindowCountPayload { CountInWindow = (Int32)shift.Count() };   The events between 12:10:10 PM and 12:10:45 PM will not be seen until the event at 11:59:59 PM arrives.  This could be a real problem if you need to react to windows promptly This can always be worked around by using a different design pattern but a lot of the examples I see assume there is a constant, very frequent stream of events resulting in windows always being flushed. Further examples of using windowing in StreamInsight can be found here

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  • What exactly is a property in Objective C ? What is the difference between a property ans an instance variable?

    - by tek3
    I am very much confused between instance variables and property. I have read number of posts regarding this but still i am not clear about it. I am from JAVA background and what i infer from objective C documentation is that a property is similar to JAVA BEAN CLASS (one having getter and setter of instance varibles). A property can accessed from other classes through its getter and setter methods while an instance variable is private and cannot be accessed from other classes. Am i right in thinking in this direction ?

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  • .NET interview, code structure and the design

    - by j_lewis
    I have been given the below .NET question in an interview. I don’t know why I got low marks. Unfortunately I did not get a feedback. Question: The file hockey.csv contains the results from the Hockey Premier League. The columns ‘For’ and ‘Against’ contain the total number of goals scored for and against each team in that season (so Alabama scored 79 goals against opponents, and had 36 goals scored against them). Write a program to print the name of the team with the smallest difference in ‘for’ and ‘against’ goals. the structure of the hockey.csv looks like this (it is a valid csv file, but I just copied the values here to get an idea) Team - For - Against Alabama 79 36 Washinton 67 30 Indiana 87 45 Newcastle 74 52 Florida 53 37 New York 46 47 Sunderland 29 51 Lova 41 64 Nevada 33 63 Boston 30 64 Nevada 33 63 Boston 30 64 Solution: class Program { static void Main(string[] args) { string path = @"C:\Users\<valid csv path>"; var resultEvaluator = new ResultEvaluator(string.Format(@"{0}\{1}",path, "hockey.csv")); var team = resultEvaluator.GetTeamSmallestDifferenceForAgainst(); Console.WriteLine( string.Format("Smallest difference in ‘For’ and ‘Against’ goals > TEAM: {0}, GOALS DIF: {1}", team.Name, team.Difference )); Console.ReadLine(); } } public interface IResultEvaluator { Team GetTeamSmallestDifferenceForAgainst(); } public class ResultEvaluator : IResultEvaluator { private static DataTable leagueDataTable; private readonly string filePath; private readonly ICsvExtractor csvExtractor; public ResultEvaluator(string filePath){ this.filePath = filePath; csvExtractor = new CsvExtractor(); } private DataTable LeagueDataTable{ get { if (leagueDataTable == null) { leagueDataTable = csvExtractor.GetDataTable(filePath); } return leagueDataTable; } } public Team GetTeamSmallestDifferenceForAgainst() { var teams = GetTeams(); var lowestTeam = teams.OrderBy(p => p.Difference).First(); return lowestTeam; } private IEnumerable<Team> GetTeams() { IList<Team> list = new List<Team>(); foreach (DataRow row in LeagueDataTable.Rows) { var name = row["Team"].ToString(); var @for = int.Parse(row["For"].ToString()); var against = int.Parse(row["Against"].ToString()); var team = new Team(name, against, @for); list.Add(team); } return list; } } public interface ICsvExtractor { DataTable GetDataTable(string csvFilePath); } public class CsvExtractor : ICsvExtractor { public DataTable GetDataTable(string csvFilePath) { var lines = File.ReadAllLines(csvFilePath); string[] fields; fields = lines[0].Split(new[] { ',' }); int columns = fields.GetLength(0); var dt = new DataTable(); //always assume 1st row is the column name. for (int i = 0; i < columns; i++) { dt.Columns.Add(fields[i].ToLower(), typeof(string)); } DataRow row; for (int i = 1; i < lines.GetLength(0); i++) { fields = lines[i].Split(new char[] { ',' }); row = dt.NewRow(); for (int f = 0; f < columns; f++) row[f] = fields[f]; dt.Rows.Add(row); } return dt; } } public class Team { public Team(string name, int against, int @for) { Name = name; Against = against; For = @for; } public string Name { get; private set; } public int Against { get; private set; } public int For { get; private set; } public int Difference { get { return (For - Against); } } } Output: Smallest difference in for' andagainst' goals TEAM: Boston, GOALS DIF: -34 Can someone please review my code and see anything obviously wrong here? They were only interested in the structure/design of the code and whether the program produces the correct result (i.e lowest difference). Much appreciated. "P.S - Please correct me if the ".net-interview" tag is not the right tag to use"

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  • JPA 2 Criteria API: why is isNull being ignored when in conjunction with equal?

    - by Vítor Souza
    I have the following entity class (ID inherited from PersistentObjectSupport class): @Entity public class AmbulanceDeactivation extends PersistentObjectSupport implements Serializable { private static final long serialVersionUID = 1L; @Temporal(TemporalType.DATE) @NotNull private Date beginDate; @Temporal(TemporalType.DATE) private Date endDate; @Size(max = 250) private String reason; @ManyToOne @NotNull private Ambulance ambulance; /* Get/set methods, etc. */ } If I do the following query using the Criteria API: CriteriaBuilder cb = em.getCriteriaBuilder(); CriteriaQuery<AmbulanceDeactivation> cq = cb.createQuery(AmbulanceDeactivation.class); Root<AmbulanceDeactivation> root = cq.from(AmbulanceDeactivation.class); EntityType<AmbulanceDeactivation> model = root.getModel(); cq.where(cb.isNull(root.get(model.getSingularAttribute("endDate", Date.class)))); return em.createQuery(cq).getResultList(); I get the following SQL printed in the log: FINE: SELECT ID, REASON, ENDDATE, UUID, BEGINDATE, VERSION, AMBULANCE_ID FROM AMBULANCEDEACTIVATION WHERE (ENDDATE IS NULL) However, if I change the where() line in the previous code to this one: cq.where(cb.isNull(root.get(model.getSingularAttribute("endDate", Date.class))), cb.equal(root.get(model.getSingularAttribute("ambulance", Ambulance.class)), ambulance)); I get the following SQL: FINE: SELECT ID, REASON, ENDDATE, UUID, BEGINDATE, VERSION, AMBULANCE_ID FROM AMBULANCEDEACTIVATION WHERE (AMBULANCE_ID = ?) That is, the isNull criterion is totally ignored. It is as if it wasn't even there (if I provide only the equal criterion to the where() method I get the same SQL printed). Why is that? Is it a bug or am I missing something?

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  • self referencing object in JPA

    - by geoaxis
    Hello, I am trying to save a SystemUser entity in JPA. I also want to save certain things like who created the SystemUser and who last modified the system User as well. @ManyToOne(targetEntity = SystemUser.class) @JoinColumn private SystemUser userWhoCreated; @Temporal(TemporalType.TIMESTAMP) @DateTimeFormat(iso=ISO.DATE_TIME) private Date timeCreated; @ManyToOne(targetEntity = SystemUser.class) @JoinColumn private SystemUser userWhoLastModified; @Temporal(TemporalType.TIMESTAMP) @DateTimeFormat(iso=ISO.DATE_TIME) private Date timeLastModified; I also want to ensure that these values are not null when persisted. So If I use the NotNull JPA annotation, that is easily solved (along with reference to another entity) The problem description is simple, I cannot save rootuser without having rootuser in the system if I am to use a DataLoader class to persist JPA entity. Every other later user can be easily persisted with userWhoModified as the "systemuser" , but systemuser it's self cannot be added in this scheme. Is there a way so persist this first system user (I am thinking with SQL). This is a typical bootstrap (chicken or the egg) problem i suppose.

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  • @PrePersist with entity inheritance

    - by gerry
    I'm having some problems with inheritance and the @PrePersist annotation. My source code looks like the following: _the 'base' class with the annotated updateDates() method: @javax.persistence.Entity @Inheritance(strategy = InheritanceType.TABLE_PER_CLASS) public class Base implements Serializable{ ... @Id @GeneratedValue protected Long id; ... @Column(nullable=false) @Temporal(TemporalType.TIMESTAMP) private Date creationDate; @Column(nullable=false) @Temporal(TemporalType.TIMESTAMP) private Date lastModificationDate; ... public Date getCreationDate() { return creationDate; } public void setCreationDate(Date creationDate) { this.creationDate = creationDate; } public Date getLastModificationDate() { return lastModificationDate; } public void setLastModificationDate(Date lastModificationDate) { this.lastModificationDate = lastModificationDate; } ... @PrePersist protected void updateDates() { if (creationDate == null) { creationDate = new Date(); } lastModificationDate = new Date(); } } _ now the 'Child' class that should inherit all methods "and annotations" from the base class: @javax.persistence.Entity @NamedQueries({ @NamedQuery(name=Sensor.QUERY_FIND_ALL, query="SELECT s FROM Sensor s") }) public class Sensor extends Entity { ... // additional attributes @Column(nullable=false) protected String value; ... // additional getters, setters ... } If I store/persist instances of the Base class to the database, everything works fine. The dates are getting updated. But now, if I want to persist a child instance, the database throws the following exception: MySQLIntegrityConstraintViolationException: Column 'CREATIONDATE' cannot be null So, in my opinion, this is caused because in Child the method "@PrePersist protected void updateDates()" is not called/invoked before persisting the instances to the database. What is wrong with my code?

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  • "tar -cfz" versus "tar cf - | gzip": are they different? (or how to improve a backup)

    - by I'm Dario
    I want to speed up my backup done with "tar -cfz", the common way to do it. But day by day my backed up files grow so it becomes slower. I was thinking to take advantage of the several cores available in my server and I was wondering if there is any difference between doing the backup with "tar -cfz" or piping tar to gzip ("tar cf - | gzip"). I guess that there isn't any difference, because the first spawns two processes (tar and gzip), in a similar way like piping it. If there is not difference, do you know any good alternative to do this, without going incremental? I'm looking at pigz too and it looks fine.

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  • I hyperlinked a cell in excel 2003, formula issues?

    - by joseinsomniac
    I have a budget spreadsheet using excel 2003. I have My deposit, then all of my bills, the total, then a cell that has the difference(between the amount of deposit and the total of the bills). The difference cell numbers turn red when I dont have enough money (deposit vs bill total). I hyperlinked the difference cell to a checkbook register spreadsheet so I can track where all my extra money went(reconsile receipts daily). When hyperlinked the numbers are blue. I need the numbers to stay black(when above 0.00) and stay red (when the numbers are below 0.00) and not change after the link has been clicked on. Also if the link has not been clicked on, and the numbers are red, the font is smaller, even though the toolbar shows the font size hasnt changed. After I click on it and go back to the budget sheet, its the size it should be. Any Ideas? Thanks!

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  • /usr/lib/cups/backend/hp has failed with an HP LaserJet p3005

    - by edtechdev
    Ever since 10.04, I can't print to an HP laserjet p3005. I'm even using an entirely different computer now with a fresh install of 10.10. I've tried with and without the latest hplip. Recently, sometimes I can get it to print a few things, but eventually it always fails (usually when printing a pdf from the document viewer (also doesn't work with adobe pdf reader)). Sometimes it fails so bad the printer gives an error saying it needs to be turned off and on again. I can't seem to find a fix anywhere, I've googled all over the past year and tried every fix I could find. It does say that the /usr/lib/cups/backend/hp has failed. It also doesn't make a difference if I create the printer using hp-setup or ubuntu's own printing control panel. I delete and re-create the printer, no difference eventually. I use the default printer settings or custom settings, no difference. I can print perfectly find to a networked printer at home - an HP officejet 6310. It seems to be networked HP printers at work that I can't print to anymore (except occasionally right after re-installing the printer driver). What's the recommended way to install HP printer drivers and reset or clean out everything from before. Or where are the right logs to read or debug commands to do to find out what may be the real cause of the printing problems?

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  • /usr/lib/cups/backend/hp has failed

    - by edtechdev
    Ever since 10.04, I can't print to an HP laserjet p3005. I'm even using an entirely different computer now with a fresh install of 10.10. I've tried with and without the latest hplip. Recently, sometimes I can get it to print a few things, but eventually it always fails (usually when printing a pdf from the document viewer (also doesn't work with adobe pdf reader)). Sometimes it fails so bad the printer gives an error saying it needs to be turned off and on again. I can't seem to find a fix anywhere, I've googled all over the past year and tried every fix I could find. It does say that the /usr/lib/cups/backend/hp has failed. It also doesn't make a difference if I create the printer using hp-setup or ubuntu's own printing control panel. I delete and re-create the printer, no difference eventually. I use the default printer settings or custom settings, no difference. I can print perfectly find to a networked printer at home - an HP officejet 6310. It seems to be networked HP printers at work that I can't print to anymore (except occasionally right after re-installing the printer driver). What's the recommended way to install HP printer drivers and reset or clean out everything from before. Or where are the right logs to read or debug commands to do to find out what may be the real cause of the printing problems?

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  • /usr/lib/cups/backend/hp has failed with an HP LaserJet p3005

    - by edtechdev
    Ever since 10.04, I can't print to an HP laserjet p3005. I'm even using an entirely different computer now with a fresh install of 10.10. I've tried with and without the latest hplip. Recently, sometimes I can get it to print a few things, but eventually it always fails (usually when printing a pdf from the document viewer (also doesn't work with adobe pdf reader)). Sometimes it fails so bad the printer gives an error saying it needs to be turned off and on again. I can't seem to find a fix anywhere, I've googled all over the past year and tried every fix I could find. It does say that the /usr/lib/cups/backend/hp has failed. It also doesn't make a difference if I create the printer using hp-setup or ubuntu's own printing control panel. I delete and re-create the printer, no difference eventually. I use the default printer settings or custom settings, no difference. I can print perfectly find to a networked printer at home - an HP officejet 6310. It seems to be networked HP printers at work that I can't print to anymore (except occasionally right after re-installing the printer driver). What's the recommended way to install HP printer drivers and reset or clean out everything from before. Or where are the right logs to read or debug commands to do to find out what may be the real cause of the printing problems?

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