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  • Java Spotlight Episode 103: 2012 Duke Choice Award Winners

    - by Roger Brinkley
    Our annual interview with the 2012 Duke Choice Award Winners recorded live at the JavaOne 2012. Right-click or Control-click to download this MP3 file. You can also subscribe to the Java Spotlight Podcast Feed to get the latest podcast automatically. If you use iTunes you can open iTunes and subscribe with this link:  Java Spotlight Podcast in iTunes. Show Notes Events Oct 13, Devoxx 4 Kids Nederlands Oct 15-17, JAX London Oct 20, Devoxx 4 Kids Français Oct 22-23, Freescale Technology Forum - Japan, Tokyo Oct 30-Nov 1, Arm TechCon, Santa Clara Oct 31, JFall, Netherlands Nov 2-3, JMagreb, Morocco Nov 13-17, Devoxx, Belgium Feature Interview Duke Choice Award Winners 2012 - Show Presentation London Java CommunityThe second user group receiving a Duke’s Choice Award this year, the London Java Community (LJC) and its users have been active in the OpenJDK, the Java Community Process (JCP) and other efforts within the global Java community. Student Nokia Developer GroupThis year’s student winner, Ram Kashyap, is the founder and president of the Nokia Student Network, and was profiled in the “The New Java Developers” feature in the March/April 2012 issue of Java Magazine. Since then, Ram has maintained a hectic pace, graduating from the People’s Education Society Institute of Technology in Bangalore, India, while working on a Java mobile startup and training students on Java ME. Jelastic, Inc.Moving existing Java applications to the cloud can be a daunting task, but startup Jelastic, Inc. offers the first all-Java platform-as-a-service (PaaS) that enables existing Java applications to be deployed in the cloud without code changes or lock-in. NATOThe first-ever Community Choice Award goes to the MASE Integrated Console Environment (MICE) in use at NATO. Built in Java on the NetBeans platform, MICE provides a high-performance visualization environment for conducting air defense and battle-space operations. DuchessRather than focus on a specific geographic area like most Java User Groups (JUGs), Duchess fosters the participation of women in the Java community worldwide. The group has more than 500 members in 60 countries, and provides a platform through which women can connect with each other and get involved in all aspects of the Java community. AgroSense ProjectImproving farming methods to feed a hungry world is the goal of AgroSense, an open source farm information management system built in Java and the NetBeans platform. AgroSense enables farmers, agribusinesses, suppliers and others to develop modular applications that will easily exchange information through a common underlying NetBeans framework. Apache Software Foundation Hadoop ProjectThe Apache Software Foundation’s Hadoop project, written in Java, provides a framework for distributed processing of big data sets across clusters of computers, ranging from a few servers to thousands of machines. This harnessing of large data pools allows organizations to better understand and improve their business. Parleys.comE-learning specialist Parleys.com, based in Brussels, Belgium, uses Java technologies to bring online classes and full IT conferences to desktops, laptops, tablets and mobile devices. Parleys.com has hosted more than 1,700 conferences—including Devoxx and JavaOne—for more than 800,000 unique visitors. Winners not presenting at JavaOne 2012 Duke Choice Awards BOF Liquid RoboticsRobotics – Liquid Robotics is an ocean data services provider whose Wave Glider technology collects information from the world’s oceans for application in government, science and commercial applications. The organization features the “father of Java” James Gosling as its chief software architect.United Nations High Commissioner for RefugeesThe United Nations High Commissioner for Refugees (UNHCR) is on the front lines of crises around the world, from civil wars to natural disasters. To help facilitate its mission of humanitarian relief, the UNHCR has developed a light-client Java application on the NetBeans platform. The Level One registration tool enables the UNHCR to collect information on the number of refugees and their water, food, housing, health, and other needs in the field, and combines that with geocoding information from various sources. This enables the UNHCR to deliver the appropriate kind and amount of assistance where it is needed.

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  • Maryland Institute College of Art - The Art of Efficient ERP

    - by jay.richey
    Talent Management Magazine has published an article on the Maryland Institute College of Art's (MICA) upgrade to PeopleSoft Enterprise HCM 9.0. Ted Simpson, director of administrative systems at MICA, illustrates how ERP software has helped revolutionize the way academic instituitions do business and lower costs. http://bit.ly/arFRFN

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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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  • Consuming Hello World pagelet in WebCenter Spaces

    - by astemkov
    Introduction The goal of this exercise is to show you how can you use Hello World pagelet that you just created from your web space. Assumptions Let's assume the following: Pagelet Producer is running on http://pageletserver.company.com:8889/pagelets/ WebCenter is running on http://webcenter.company.com:8888/webcenter/ You created Hello_World pagelet as described here. For our exercise we will need a space created. So let's login into WebCenter Portal and create a space called "myspace" using "Portal Site" template: Registering Pagelet Producer with WebCenter portal In order to use our newly created pagelet from WebCenter Spaces, we first need to register Pagelet Producer: Click "Administraion" link on WebCenter toolbar Open the "Configuration" tab Click on "Services" link on the upper-left corner of the page Click on "Portlet Producers" link on the right hand pane of the screen Click on "Register" button Select "Pagelet Producer" radio button and type Producer Name = "MyPageletProducer" Server URL = http://pageletserver.company.com:8889/pagelets/ Click "Test" button If everything is succesful you will see the following screen: Now click "OK'. Pagelet producer is registered: Inserting Hello World pagelet to WebCenter Space Now let's insert Hello World pagelet into "myspace" page: Let's go back to "myspace", click on the icon in a upper-right corner of the page and select "Edit Page" Click on one of the "Add Content" buttons: Select "Mash-Ups": Select "Pagelet Producers: You will see the MyPageletProducer that we just registered: Click on it. You will see the library "MyLib" that contains our "Hello_World" pagelet. Click on "MyLib" and you will see "Hello_World" pagelet. Click on "Add" button, and then "Close" button. Click "Save" button, and then "Close". Now we see that our "Hello World" pagelet is inserted into "myspace" page:

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  • Optimizing Solaris 11 SHA-1 on Intel Processors

    - by danx
    SHA-1 is a "hash" or "digest" operation that produces a 160 bit (20 byte) checksum value on arbitrary data, such as a file. It is intended to uniquely identify text and to verify it hasn't been modified. Max Locktyukhin and others at Intel have improved the performance of the SHA-1 digest algorithm using multiple techniques. This code has been incorporated into Solaris 11 and is available in the Solaris Crypto Framework via the libmd(3LIB), the industry-standard libpkcs11(3LIB) library, and Solaris kernel module sha1. The optimized code is used automatically on systems with a x86 CPU supporting SSSE3 (Intel Supplemental SSSE3). Intel microprocessor architectures that support SSSE3 include Nehalem, Westmere, Sandy Bridge microprocessor families. Further optimizations are available for microprocessors that support AVX (such as Sandy Bridge). Although SHA-1 is considered obsolete because of weaknesses found in the SHA-1 algorithm—NIST recommends using at least SHA-256, SHA-1 is still widely used and will be with us for awhile more. Collisions (the same SHA-1 result for two different inputs) can be found with moderate effort. SHA-1 is used heavily though in SSL/TLS, for example. And SHA-1 is stronger than the older MD5 digest algorithm, another digest option defined in SSL/TLS. Optimizations Review SHA-1 operates by reading an arbitrary amount of data. The data is read in 512 bit (64 byte) blocks (the last block is padded in a specific way to ensure it's a full 64 bytes). Each 64 byte block has 80 "rounds" of calculations (consisting of a mixture of "ROTATE-LEFT", "AND", and "XOR") applied to the block. Each round produces a 32-bit intermediate result, called W[i]. Here's what each round operates: The first 16 rounds, rounds 0 to 15, read the 512 bit block 32 bits at-a-time. These 32 bits is used as input to the round. The remaining rounds, rounds 16 to 79, use the results from the previous rounds as input. Specifically for round i it XORs the results of rounds i-3, i-8, i-14, and i-16 and rotates the result left 1 bit. The remaining calculations for the round is a series of AND, XOR, and ROTATE-LEFT operators on the 32-bit input and some constants. The 32-bit result is saved as W[i] for round i. The 32-bit result of the final round, W[79], is the SHA-1 checksum. Optimization: Vectorization The first 16 rounds can be vectorized (computed in parallel) because they don't depend on the output of a previous round. As for the remaining rounds, because of step 2 above, computing round i depends on the results of round i-3, W[i-3], one can vectorize 3 rounds at-a-time. Max Locktyukhin found through simple factoring, explained in detail in his article referenced below, that the dependencies of round i on the results of rounds i-3, i-8, i-14, and i-16 can be replaced instead with dependencies on the results of rounds i-6, i-16, i-28, and i-32. That is, instead of initializing intermediate result W[i] with: W[i] = (W[i-3] XOR W[i-8] XOR W[i-14] XOR W[i-16]) ROTATE-LEFT 1 Initialize W[i] as follows: W[i] = (W[i-6] XOR W[i-16] XOR W[i-28] XOR W[i-32]) ROTATE-LEFT 2 That means that 6 rounds could be vectorized at once, with no additional calculations, instead of just 3! This optimization is independent of Intel or any other microprocessor architecture, although the microprocessor has to support vectorization to use it, and exploits one of the weaknesses of SHA-1. Optimization: SSSE3 Intel SSSE3 makes use of 16 %xmm registers, each 128 bits wide. The 4 32-bit inputs to a round, W[i-6], W[i-16], W[i-28], W[i-32], all fit in one %xmm register. The following code snippet, from Max Locktyukhin's article, converted to ATT assembly syntax, computes 4 rounds in parallel with just a dozen or so SSSE3 instructions: movdqa W_minus_04, W_TMP pxor W_minus_28, W // W equals W[i-32:i-29] before XOR // W = W[i-32:i-29] ^ W[i-28:i-25] palignr $8, W_minus_08, W_TMP // W_TMP = W[i-6:i-3], combined from // W[i-4:i-1] and W[i-8:i-5] vectors pxor W_minus_16, W // W = (W[i-32:i-29] ^ W[i-28:i-25]) ^ W[i-16:i-13] pxor W_TMP, W // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) movdqa W, W_TMP // 4 dwords in W are rotated left by 2 psrld $30, W // rotate left by 2 W = (W >> 30) | (W << 2) pslld $2, W_TMP por W, W_TMP movdqa W_TMP, W // four new W values W[i:i+3] are now calculated paddd (K_XMM), W_TMP // adding 4 current round's values of K movdqa W_TMP, (WK(i)) // storing for downstream GPR instructions to read A window of the 32 previous results, W[i-1] to W[i-32] is saved in memory on the stack. This is best illustrated with a chart. Without vectorization, computing the rounds is like this (each "R" represents 1 round of SHA-1 computation): RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR With vectorization, 4 rounds can be computed in parallel: RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR RRRRRRRRRRRRRRRRRRRR Optimization: AVX The new "Sandy Bridge" microprocessor architecture, which supports AVX, allows another interesting optimization. SSSE3 instructions have two operands, a input and an output. AVX allows three operands, two inputs and an output. In many cases two SSSE3 instructions can be combined into one AVX instruction. The difference is best illustrated with an example. Consider these two instructions from the snippet above: pxor W_minus_16, W // W = (W[i-32:i-29] ^ W[i-28:i-25]) ^ W[i-16:i-13] pxor W_TMP, W // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) With AVX they can be combined in one instruction: vpxor W_minus_16, W, W_TMP // W = (W[i-32:i-29] ^ W[i-28:i-25] ^ W[i-16:i-13]) ^ W[i-6:i-3]) This optimization is also in Solaris, although Sandy Bridge-based systems aren't widely available yet. As an exercise for the reader, AVX also has 256-bit media registers, %ymm0 - %ymm15 (a superset of 128-bit %xmm0 - %xmm15). Can %ymm registers be used to parallelize the code even more? Optimization: Solaris-specific In addition to using the Intel code described above, I performed other minor optimizations to the Solaris SHA-1 code: Increased the digest(1) and mac(1) command's buffer size from 4K to 64K, as previously done for decrypt(1) and encrypt(1). This size is well suited for ZFS file systems, but helps for other file systems as well. Optimized encode functions, which byte swap the input and output data, to copy/byte-swap 4 or 8 bytes at-a-time instead of 1 byte-at-a-time. Enhanced the Solaris mdb(1) and kmdb(1) debuggers to display all 16 %xmm and %ymm registers (mdb "$x" command). Previously they only displayed the first 8 that are available in 32-bit mode. Can't optimize if you can't debug :-). Changed the SHA-1 code to allow processing in "chunks" greater than 2 Gigabytes (64-bits) Performance I measured performance on a Sun Ultra 27 (which has a Nehalem-class Xeon 5500 Intel W3570 microprocessor @3.2GHz). Turbo mode is disabled for consistent performance measurement. Graphs are better than words and numbers, so here they are: The first graph shows the Solaris digest(1) command before and after the optimizations discussed here, contained in libmd(3LIB). I ran the digest command on a half GByte file in swapfs (/tmp) and execution time decreased from 1.35 seconds to 0.98 seconds. The second graph shows the the results of an internal microbenchmark that uses the Solaris libpkcs11(3LIB) library. The operations are on a 128 byte buffer with 10,000 iterations. The results show operations increased from 320,000 to 416,000 operations per second. Finally the third graph shows the results of an internal kernel microbenchmark that uses the Solaris /kernel/crypto/amd64/sha1 module. The operations are on a 64Kbyte buffer with 100 iterations. third graph shows the results of an internal kernel microbenchmark that uses the Solaris /kernel/crypto/amd64/sha1 module. The operations are on a 64Kbyte buffer with 100 iterations. The results show for 1 kernel thread, operations increased from 410 to 600 MBytes/second. For 8 kernel threads, operations increase from 1540 to 1940 MBytes/second. Availability This code is in Solaris 11 FCS. It is available in the 64-bit libmd(3LIB) library for 64-bit programs and is in the Solaris kernel. You must be running hardware that supports Intel's SSSE3 instructions (for example, Intel Nehalem, Westmere, or Sandy Bridge microprocessor architectures). The easiest way to determine if SSSE3 is available is with the isainfo(1) command. For example, nehalem $ isainfo -v $ isainfo -v 64-bit amd64 applications sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov amd_sysc cx8 tsc fpu 32-bit i386 applications sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov sep cx8 tsc fpu If the output also shows "avx", the Solaris executes the even-more optimized 3-operand AVX instructions for SHA-1 mentioned above: sandybridge $ isainfo -v 64-bit amd64 applications avx xsave pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov amd_sysc cx8 tsc fpu 32-bit i386 applications avx xsave pclmulqdq aes sse4.2 sse4.1 ssse3 popcnt tscp ahf cx16 sse3 sse2 sse fxsr mmx cmov sep cx8 tsc fpu No special configuration or setup is needed to take advantage of this code. Solaris libraries and kernel automatically determine if it's running on SSSE3 or AVX-capable machines and execute the correctly-tuned code for that microprocessor. Summary The Solaris 11 Crypto Framework, via the sha1 kernel module and libmd(3LIB) and libpkcs11(3LIB) libraries, incorporated a useful SHA-1 optimization from Intel for SSSE3-capable microprocessors. As with other Solaris optimizations, they come automatically "under the hood" with the current Solaris release. References "Improving the Performance of the Secure Hash Algorithm (SHA-1)" by Max Locktyukhin (Intel, March 2010). The source for these SHA-1 optimizations used in Solaris "SHA-1", Wikipedia Good overview of SHA-1 FIPS 180-1 SHA-1 standard (FIPS, 1995) NIST Comments on Cryptanalytic Attacks on SHA-1 (2005, revised 2006)

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  • iPad and User Assistance

    - by ultan o'broin
    What possibilities does the iPad over for user assistance in the enterprise space? We will research the possibilities but I can see a number of possibilities already for remote workers who need access to trouble-shooting information on-site, implementers who need reference information and diagrams, business analysts or technical users accessing reports and dashboards for metrics or issues, functional users who need org charts and other data visualizations, and so on. It could also open up more possibilities for collaborative problem solving. User assistance content can take advantage of the device's superb display, graphics capability, connectivity, and long battery life. The possibility of opening up more innovative user assistance solutions (such as comics) is an exciting one for everyone in the UX space. Aligned to this possibility we need to research how users would use the device as they work.

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  • GlassFish and Friends Party, 1st Edition at JavaOne Brasil

    - by Bruno.Borges
    Estamos muito contentes em anunciar que iremos realizar a primeira edição da tradicional  GlassFish and Friends Party neste JavaOne in Brasil.  O problema é que os ingressos já esgotaram! Então decidimos realizar um concurso para dar mais 5 ingressos para a comunidade! Aqui estão as regras: Escreva um post no seu blog sobre o GlassFish  Poste no Twitter o título e o link do seu post com a hashtag #GlassFish para que possamos saber do seu post Os 5 melhores posts serão selecionados e anunciados aqui no dia 3 de Dezembro às 19:00 (GMT-3) Selecionaremos um post de cada autor Cada autor receberá um ingresso para a festa Agora corre para a sua plataforma de blog e escreva sobre o GlassFish! ------------- en_US ---------------  We are very happy to announce that we are going to host the first edition of the traditional GlassFish and Friends Party at this JavaOne in Brasil.  The problem is: tickets are already SOLD OUT!  So we decided to run a simple contest to give away 5 more tickets to the community! Here are the rules: Blog about GlassFish Tweet the title and link of your blog post with the hashtag #GlassFish so we can know about your blog post The best 5 blog posts will be selected and announced here on December 3th at 7pm (GMT-3) We will select one blog post per author Each author will get one ticket Now run to your blog platform and write about GlassFish!

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  • Calling Web Services with HTTP Basic Authentication from BPEL 10.1.3.4

    - by Ramkumar Menon
    Are you using BPEL 10.1.3.4 and hunting for the property names in the partnerlinkBindings that will work for outbound HTTP Basic Authentication? Here's the answer. <partnerLinkBinding ...>  <property name="basicHeaders">credentials</property>  <property name="basicUsername">WhoAmI</property>  <property name="basicPassword">thatsASecret</property></partnerLinkBinding>The drop down options in JDeveloper dont seem to work.

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  • CIC 2010 - Ghost Stories and Model Based Design

    - by warren.baird
    I was lucky enough to attend the collaboration and interoperability congress recently. The location was very beautiful and interesting, it was held in the mountains about two hours outside Denver, at the Stanley hotel, famous both for inspiring Steven King's novel "The Shining" and for attracting a lot of attention from the "Ghost Hunters" TV show. My visit was prosaic - I didn't get to experience the ghosts the locals promised - but interesting, with some very informative sessions. I noticed one main theme - a lot of people were talking about Model Based Design (MBD), which is moving design and manufacturing away from 2d drawings and towards 3d models. 2d has some pretty deep roots in industrial manufacturing and there have been a lot of challenges encountered in making the leap to 3d. One of the challenges discussed in several sessions was how to get model information out to the non-engineers in the company, which is a topic near and dear to my heart. In the 2D space, people without access to CAD software (for example, people assembling a product on the shop floor) can be given printouts of the design - it's not particularly efficient, and it definitely isn't very green, but it tends to work. There's no direct equivalent in the 3D space. One of the ways that AutoVue is used in industrial manufacturing is to provide non-CAD users with an easy to use, interactive 3D view of their products - in some cases it's directly used by people on the shop floor, but in cases where paper is really ingrained in the process, AutoVue can be used by a technical publications person to create illustrative 2D views that can be printed that show all of the details necessary to complete the work. Are you making the move to model based design? Is AutoVue helping you with your challenges? Let us know in the comments below.

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  • JavaOne pictures and Community Commentary on JCP Awards

    - by heathervc
    We posted some pictures from JCP related events at JavaOne 2012 on the JCP Facebook page today.  The 2012 JCP Program Award winners and some of the nominees responded to the community recognition of their achievements during some of the JCP events last week.     “Our job on the EC is to balance the need of innovation – so we don’t standardize too early, or too late. We try to find that sweet spot that makes innovation and standardization work together, and not against each other.”- Ben Evans, CEO of jClarity and Executive Committee (EC) representative of the London Java Community, 2012 JCP Member/Participant of the Year Winner“SouJava has been evangelizing the Java platform, promoting the Java ecosystem in Brazil, and contributing to JSRs for several years. It’s very gratifying to have our work recognized, on behalf of many developers and Java User Groups around the world. This really is the work of a large group of people, represented by the few that can be here tonight.”- Michael Santos, representative of SouJava, 2012 JCP Member/Participant of the Year Winner "In the last years Credit Suisse has contributed to the development of Java EE specifications through participation in many customer advisory boards, through statements of requirements for extensions to the core Java related products in use, and active participation in JSRs. Winning the JCP Outstanding Spec Lead Award 2012 is very encouraging for our engagement and also demonstrates the level of expertise and commitment to drive the evolution of Java. Victor Grazi is happy and honored to receive this award." - Susanne Cech Previtali, Executive Committee (EC) representative of Credit Suisse, accepting award for 2012 JCP Outstanding Spec Lead Winner "Managing a JSR is difficult. There are so many decisions to be made and so many good and varied opinions, you never really know if you have decided correctly. The key to success is transparency and collaboration. I am truly humbled by receiving this award, there are so many other active JSRs.” Victor added that going forward in the JCP EC, they would like to simplify and open the process of participation – being addressed in the JCP.Next initiative of the JCP EC. "We would also like to encourage the engagement of universities, professors and students – as an important part of the Java community. While innovation is the lifeblood of our community and industry, without strong standards and compatibility requirements, we all end up in a maze of technology where everything is slightly different and doesn’t quite work with everything else." Victo Grazi, Executive Committee (EC) representative of Credit Suisse, 2012 JCP Outstanding Spec Lead Winner“I am very pleased, of course, to accept this award, but the credit really should go to all of those who have participated in the work of the JCP, while pushing for changes in the way it operates.  JCP.Next represents three JSRs. The first two are done, but the final step, JSR 358, is the complicated one, and it will bring in the lawyers. Just to give you an idea of what we’re dealing with, it affects licensing, intellectual property, patents, implementations not based on the Reference Implementation (RI), the role of the RI, compatibility policy, possible changes to the Technical Compatibility Kit (TCK), transparency, where do individuals fit in, open source, and more.”- Patrick Curran, JCP Chair, Spec Lead on JCP.Next JSRs (JSR 348, JSR 355 and JSR 358), 2012 JCP Most Significant JSR Winner“I’m especially glad to see the JCP community recognize JCP.Next for its importance. The governance work it represents is KEY to moving the Java platform forward and the success of the technology.”- John Rizzo, Executive Committee (EC) representative of Aplix Corporation, JSR Expert Group Member “I am deeply honored to be nominated. I had the privilege to receive two awards on behalf of Expert Groups and Spec Leads two years ago. But this time, I am nominated personally, which values my own contribution to the JCP, and of course, participation in JSRs and the EC work. I’m a fan of Agile Principles and Values Working. Being an Agile Coach and Consultant, I use it for some of the biggest EC Member companies and projects. It fuels my ability to help the JCP become more agile, lean and transparent as part of the JCP.Next effort.” - Werner Keil, Individual Executive Committee (EC) Member, a 2012 JCP Member/Participant of the Year Nominee, JSR Expert Group Member“The JCP ever has been some kind of institution for me,” Markus said. “If in technical doubt, I go there, look for the specifications of the implementation I work with at the moment and verify what I had observed. Since the beginning of my Java journey more than 12 years back now, I always had a strong relationship with the JCP. Shaping the future of a technology by joining the JCP – giving feedback and contributing to the road ahead through individual JSRs – that brings you to a whole new level.”Calling himself, “the new kid on the block,” he explained that for years he was afraid to join the JCP and contribute. But in reality, “Every single one of the big names I meet from the different Expert Groups is a nice person. People you can actually work with,” he says. “And nobody blames you for things you don't know. As long as you are committed and bring what is worth the most: passion, experiences and the desire to make a difference.” - Markus Eisele, a 2012 JCP Member of the Year Nominee, JSR Expert Group MemberCongratulations again to all of the nominees and winners of the JCP Program Awards.  Next year, we will add another award for the group of JUG members (not an entire JUG) that makes the best contribution to the Adopt-a-JSR program.  Let us know if you have other suggestions or improvements.

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  • GlassFish Back from Devoxx 2011 Mature Java EE 6 and EE 7 well on its way

    - by alexismp
    I'm back from my 8th (!) Devoxx conference (I don't think I've missed one since 2004) and this conference keeps delivering on the promise of a Java developer paradise week. GlassFish was covered in many different ways and I was not involved in a good number of them which can only be a good sign! Several folks asked me when my Java EE 6 session with Antonio Goncalves was scheduled (we've been covering this for the past two years in University sessions, hands-on labs and regular sessions). It turns out we didn't team up this year (Antonio was crazy busy preparing for Devoxx France) and I had a regular GlassFish session. Instead, this year, Bert Ertman and Paul Bakker covered the 3-hour Java EE 6 University session ("Duke’s Duct Tape Adventures") on the very first day (using GlassFish) with great success it seems. The Java EE 6 lab was also a hit with a full room of folks covering a lot of technical ground in 2.5 hours (with GlassFish of course). GlassFish was also mentioned during Cameron Purdy's keynote (pretty natural even if that surprised a number of folks that had not been closely following GlassFish) but also in Stephan Janssen's Keynote as the engine powering Parleys.com. In fact Stephan was a speaker in the GlassFish session describing how they went from a single-instance Tomcat setup to a clustered GlassFish + MQ environment. Also in the session was Johan Vos (of Mollom fame, along other things). Both of these customer testimonials were made possible because GlassFish has been delivering full Java EE 6 implementations for almost two years now which is plenty of time to see serious production deployments on it. The Java EE Gathering (BOF) was very well attended and very lively with many spec leads participating and discussing progress and also pain points with folks in the room. Thanks to all those attending this session, a good number of RFE's, and priority points came out of this. While this wasn't a GlassFish session by any means, it's great to have the current RESTful Admin and upcoming Java EE 7 planned features be a satisfactory answer to some of the requests from the attendance. Last but certainly not least, the GlassFish team is busy with Java EE 7 and version 4 of the product. This was discussed and shown during the Java EE keynote and in greater details in Jerome Dochez' session. If any indication, the tweets on his demo (virtualization, provisioning, etc...) were very encouraging. Java EE 6 adoption is doing great and GlassFish, being a production-quality reference implementation, is one of the first to benefit from this. And with GlassFish 4.0, we're looking at increasing the product and community adoption by offering a pragmatic technical solution to Java EE PaaS deployments. Stay tuned ! (the impatient in you is encouraged to grab a 4.0 build and provide feedback).

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  • Mix metrics for March 22, 2010

    - by tim.bonnemann
    Mix hit another major milestone this past week, surpassing 60,000 registered members. Registered Mix users (weekly growth) 60,662 (+0.8%) Active users (percent of total) Last 30 days: 4,571 (7.5%) Last 60 days: 8,945 (14.7%) Last 90 days: 11,479 (18.9%) Traffic (30-day) Visits: 12,371 Page views: 70,896 Twitter Followers: 3,117 List mentions: 146 User-generated content (30-day) New ideas: 32 New questions: 74 New comments: 378 Groups There are currently 1,394 Mix groups (requires login).

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  • Geek Bike Ride at JavaOne 2012 - Pictures

    - by arungupta
    Following the tradition of JavaOne Latin America 2011, a gorgeous day in San Francisco marked the beginning of JavaOne 2012 with another Geek Bike Ride. About 50 Java developers got together this morning at Fisherman's Wharf and rode a bike along Marina, Crissy Field, Fort Mason, Golden Gate Bridge, and ultimately finishing in Sausalito downtown. This is a beautiful biking trail, mostly flat with a couple of good hills. Some folks even continued to Tiburon for an extra challenge. Check out map by Blazing Saddles for the exact course. They provide excellent bike rentals and a good service too! Here are some pictures from the day: Credits: Yoshio Terada And check out a video of bikers rolling down the hill: Credits: Yoshio Terada Thank you OTN for sponsoring the t-shirts! And Kevin Nilson, fearless leader of Silicon Valley JUG, for hosting the event! And now to main the conference starting tomorrow! Here is the evolving album for JavaOne 2012 so far ... And don't forget, I'm still recruiting runners for the Community Run on Oct 1 at 6:17am PT :-)

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  • UPK 3.6.1 New Feature - Publish Presentation

    - by peter.maravelias
    UPK includes numerous options for deploying the content you have created. Most UPK users are familiar with the UPK Player and the various document outputs that have been available as publishing formats for some time now. In addition UPK provides the content developer the ability to publish content for use in specific environments, LMS, Test Director are two examples. UPK 3.6.1 adds the Presentation publishing type. The Presentation publishing type produces a slideshow presentation of screenshots and text of each topic as a separate Microsoft PowerPoint file. To publish to the presentation option just select the type under the documents category in the publishing wizard. Give this new publishing type a try and let us know what you think by posting a comment. The Presentation publishing type feature came from a customer request and given the ever growing methods and channels for communication we'd like to know what other output types or methods of using existing outputs you would like to see in a future release of UPK.

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  • Comunidades de pr&aacute;tica

    - by fernando.galdino
    Este ano eu comecei a fazer um mestrado em Gestão de Projetos na Uninove, aqui em São Paulo. E um dos temas de pesquisa que irei desenvolver é sobre comunidades de prática. Basicamente, são comunidades criadas pelas pessoas que objetivam a expandir o conhecimento sobre determinado assunto. Um exemplo desse tipo de comunidade seria, por exemplo, os grupos de usuários Java. Essas comunidades podem se desenvolver nas mais variadas formas: dentro de empresas, fora das empresas com profissionais de diversas companhias, dentro de empresas com colaboração com usuários de outras empresas. Atualmente, muitos desses grupos acabam usando recursos oferecidos na Internet (grupos, fóruns, emails) para se comunicarem. Eu, por exemplo, cuidei de um grupo desses, por cerca de um ano, na época em que trabalhei na IBM. Quem tiver conhecimento de comunidades desse tipo, e quiser colaborar com meu estudo, entre em contato. Tenho especial interesse em coletar experiências desses grupos, principalmente ajudando a desenvolver o conhecimento dentro das empresas.

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  • Mix metrics for April 5, 2010

    - by tim.bonnemann
    Our latest numbers... Registered Mix users (weekly growth) 61,374 (+0.6%) Active users (percent of total) Last 30 days: 4,317 (7.0%) Last 60 days: 8,638 (14.1%) Last 90 days: 12,481 (20.3%) Traffic (30-day) Visits: 11,893 Page views: 65,880 Twitter Followers: 3,169 List mentions: 146 User-generated content (30-day) New ideas: 36 New questions: 57 New comments: 394 Groups There are currently 1,402 Mix groups (requires login).

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  • Url Navigation

    - by russ.bishop
    One of the new features is URL-based navigation which is useful for creating intranet links or auto-generating email links (such as from workflow systems, etc). For IIS 6 and earlier, the format is as follows: http://machine/drm-client/Logon.aspx? app=<appname>&action=go&ver=<version name>&hier=<hier name>&node=<node name> Just replace the fields with their appropriate values (URL-encoded of course). <node name> is optional. If provided it will open the hierarchy and expand directly to the target node. Otherwise the hierarchy is opened to the top node. Note that if the specified version is not loaded it will be loaded automatically.

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  • SOA Governance Starts with People and Processes

    - by Jyothi Swaroop
    While we all agree that SOA Governance is about People, Processes and Technology. Some experts are of the opinion that SOA Governance begins with People and Processes but needs to be empowered with technology to achieve the best results. Here's an interesting piece from David Linthicum on eBizq: In the world of SOA, the concept of SOA governance is getting a lot of attention. However, how SOA governance is defined and implemented really depends on the SOA governance vendor who just left the building within most enterprises. Indeed, confusion is a huge issue when considering SOA governance, and the core issues are more about the fundamentals of people and processes, and not about the technology. SOA governance is a concept used for activities related to exercising control over services in an SOA, including tracking the services, monitoring the service, and controlling changes made to the services, simple put. The trouble comes in when SOA governance vendors attempt to define SOA governance around their technology, all with different approaches to SOA governance. Thus, it's important that those building SOAs within the enterprise take a step back and understand what really need to support the concept of SOA governance. The value of SOA governance is pretty simple. Since services make up the foundation of an SOA, and are at their essence the behavior and information from existing systems externalized, it's critical to make sure that those accessing, creating, and changing services do so using a well controlled and orderly mechanism. Those of you, who already have governance in place, typically around enterprise architecture efforts, will be happy to know that SOA governance does not replace those processes, but becomes a mechanism within the larger enterprise governance concept. People and processes are first thing on the list to get under control before you begin to toss technology at this problem. This means establishing an understanding of SOA governance within the team members, including why it's important, who's involved, and the core processes that are to be follow to make SOA governance work. Indeed, when creating the core SOA governance strategy should really be independent of the technology. The technology will change over the years, but the core processes and discipline should be relatively durable over time.

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  • JSR 360 and JSR 361: A Big Leap for Java ME 8

    - by terrencebarr
    It might have gone unnoticed to some, but Java ME took a big leap forward a couple of weeks ago with the filing of two new JSRs: JSR 360: “Connected Limited Device Configuration 8″ (aka CLDC 8) JSR 361: “Java ME Embedded Profile” (aka ME EP) Together, these two JSRs will significantly update, enhance, and modernize the Java ME platform, and specifically small embedded Java, with a host of new features and functionality. JSR 360 – Connected Limited Device Configuration 8 CLDC 8 is based on JSR 139 (CLDC 1.1) and updates the core Java ME VM, language support, libraries, and features to be aligned with Java SE 8. This will include: VM updated to comply with the JVM language specification version 2 Support for SE 7/8 language features like Generics, Assertions, Annotations, Try-with-Resources, and more New libraries such as Collections, NIO subset, Logging API subset A consolidated and enhanced Generic Connection Framework for multi-protocol I/O With CLDC 8, Java ME and Java SE are entering their next phase of alignment – making Java the only technology today that truly scales application development, code re-use, and tooling across the whole range of IT platforms, from small embedded to large enterprise. JSR 361 – Java ME Embedded Profile ME EP is based on JSR 228 (IMP-NG) and updates the specification in key areas to provide a powerful and flexible application environment for small embedded Java platforms, building on the features of CLDC 8:  A new, lightweight component and services model Shared libraries Multi-application concurrency, inter-application communication, and event system Application management API optionality, to address low-footprint use cases With ME EP, application developers will have a modern application environment which allows development and deployment of  modular, robust, sophisticated, and footprint-optimized solutions for a wide range of embedded use cases and devices. Summary While these JSRs are still under development, it’s clear that there are exciting new times ahead for Java ME – turning into a serious application platform while maintaining the focus on resource-constrained devices to address the expected explosion of small, smart, and connected embedded platforms. To learn more, click on the above links for JSR 360 and JSR 361. Or review the JavaOne 2012 online presentations on the topic: CON11300: Expanding the reach of the Java ME Platform CON5943: Java ME 8 Service Platform And stay tuned for more in this space! Cheers, – Terrence Filed under: Mobile & Embedded Tagged: "jsr 360", "jsr 361", "me 8", embedded, Embedded Java, JCP

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  • JSR 360 and JSR 361: A Big Leap for Java ME 8

    - by terrencebarr
    It might have gone unnoticed to some, but Java ME took a big leap forward a couple of weeks ago with the filing of two new JSRs: JSR 360: “Connected Limited Device Configuration 8″ (aka CLDC 8) JSR 361: “Java ME Embedded Profile” (aka ME EP) Together, these two JSRs will significantly update, enhance, and modernize the Java ME platform, and specifically small embedded Java, with a host of new features and functionality. JSR 360 – Connected Limited Device Configuration 8 CLDC 8 is based on JSR 139 (CLDC 1.1) and updates the core Java ME VM, language support, libraries, and features to be aligned with Java SE 8. This will include: VM updated to comply with the JVM language specification version 2 Support for SE 7/8 language features like Generics, Assertions, Annotations, Try-with-Resources, and more New libraries such as Collections, NIO subset, Logging API subset A consolidated and enhanced Generic Connection Framework for multi-protocol I/O With CLDC 8, Java ME and Java SE are entering their next phase of alignment – making Java the only technology today that truly scales application development, code re-use, and tooling across the whole range of IT platforms, from small embedded to large enterprise. JSR 361 – Java ME Embedded Profile ME EP is based on JSR 228 (IMP-NG) and updates the specification in key areas to provide a powerful and flexible application environment for small embedded Java platforms, building on the features of CLDC 8:  A new, lightweight component and services model Shared libraries Multi-application concurrency, inter-application communication, and event system Application management API optionality, to address low-footprint use cases With ME EP, application developers will have a modern application environment which allows development and deployment of  modular, robust, sophisticated, and footprint-optimized solutions for a wide range of embedded use cases and devices. Summary While these JSRs are still under development, it’s clear that there are exciting new times ahead for Java ME – turning into a serious application platform while maintaining the focus on resource-constrained devices to address the expected explosion of small, smart, and connected embedded platforms. To learn more, click on the above links for JSR 360 and JSR 361. Or review the JavaOne 2012 online presentations on the topic: CON11300: Expanding the reach of the Java ME Platform CON5943: Java ME 8 Service Platform And stay tuned for more in this space! Cheers, – Terrence Filed under: Mobile & Embedded Tagged: "jsr 360", "jsr 361", "me 8", embedded, Embedded Java, JCP

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  • ?Portal Content Personalization

    - by john.brunswick
    To make the most effective use of a portal and content management platform, personalization is a critical component of delivering the most value to end users. Regardless of what type of constituents you may be serving, content relevance is critical to support business goals like self-service, communication within a geographically distributed organization, lead generation and customer loyalty effectively. This especially holds true when serving external parties, as they generally have a lower threshold for digging through your site to locate a particular item of interest and are apt to leave or dial a helpdesk if their efforts cannot locate the relevant information. Optimal delivery of content can be achieved through a variety of methods, but it is generally a blend of security and filtering via meta data that can drive the most return with the least amount of upfront effort and ongoing upkeep. In a portal environment various platform components have their strong suits and by combining the capabilities of enterprise portal and content platforms much of the groundwork for personalization can be achieved in a configuration-based manner. In our discussion we will cover terminology and concepts, example scenarios and technical implementation strategies to help showcase how personalization of content can be achieved within a portal from a technical and strategic standpoint. Read on to better understand the chart below and the components at our disposal to personalize content delivery. Read on... click here to view a full size chart

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  • Tuning GlassFish for Production

    - by arungupta
    The GlassFish distribution is optimized for developers and need simple deployment and server configuration changes to provide the performance typically required for production usage. The formal Performance Tuning Guide provides an explanation of capacity planning and tuning tips for application, GlassFish, JVM, and the operating system. The GlassFish Server Control (only with the commercial edition) also comes with Performance Tuner that optimizes the runtime for optimal throughput and scalability. And then there are multiple blogs that provide more insights as well: • Optimizing GlassFish for Production (Diego Silva, Mar 2012) • GlassFish Production Tuning (Vegard Skjefstad, Nov 2011) • GlassFish in Production (Sunny Saxena, Jul 2011) • Putting GlassFish v3 in Production: Essential Surviving Guide (JeanFrancois, Nov 2009) • A GlassFish Tuning Primer (Scott Oaks, Dec 2007) What is your favorite source for GlassFish Performance Tuning ?

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  • Solving Null Entity Problems with JPA Data Controls in PS1

    - by shay.shmeltzer
    Turns out there is a slight bug that seems to prevent you from doing interactions (update, scroll) with the results of a JPA named query that you dropped on a page using ADF Binding. People are running into this when they are doing the EJB tutorial on OTN for example. The problem is that the way the binding is set up for you automatically doesn't allow you to actually access the iterator set of records to do follow up operations. When I last checked this was solved in the next release of JDeveloper, but in the meantime there is a quick simple way to resolve the issue by changing the refresh condition of the oiterator in your page binding. Here is a little demo that shows the problem and the solution:

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