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  • It's Coming: Chalk Talk with John

    - by Tanu Sood
    ...John Brunswick that is. Who is this John Brunswick, you ask? John Brunswick is an Enterprise Architect with Oracle. As an Oracle Enterprise Architect, John focuses on the alignment of technical capabilities in support of business vision and objectives, as well as the overall business value of technology. What's more he is pretty handy with animation and digital videos as you will see shortly. Starting tomorrow, we will host a bi-weekly column with John called "Chalk Talk with John". Normal 0 false false false EN-US X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-family:"Calibri","sans-serif"; mso-ascii- mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi- mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} In our "Chalk Talk with John" series, John will leverage his skills, experience and expertise (& his passion in digital animation) to discuss technology in business terms or as he puts it "so my ma understands what I do for a living". Through this series, John will explore the practical value of Middleware in the context of two fictional communities, shared through analogies aligned to enterprise technology.  This format offers business stakeholders and IT a common language for understanding the benefits of technology in support of their business initiatives, regardless of their current level of technical knowledge. So, be sure to tune in tomorrow and every 2 weeks for "Chalk Talk with John".

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  • links for 2010-04-14

    - by Bob Rhubart
    Why business needs should shape IT architecture - McKinsey Quarterly - Business Technology - Organization "Too often, efforts to fix architecture issues remain rooted in a company’s IT practices, culture, and leadership. The reason, in part, is that the chief architect—the overall IT-architecture program leader—is frequently selected from within the technical ranks, bringing deep IT know-how but little direct experience or influence in leading a business-wide change program. A weak linkage to the business creates a void that limits the quality of the resulting IT architecture and the organization’s ability to enforce and sustain the benefits of implementation over time." -- Helge Buckow and Stéphane Rey (tags: architecture it technology enterprise mckinsey) Eric Maurice: April 2010 Critical Patch Update Released Eric Maurice offers the details on April 2010 Critical Patch Update (CPUApr2010), "the first one to include security fixes for Oracle Solaris" (tags: oracle otn database fusionmiddleware peoplesoft security) @shivmohan: Oracle – OAF – Oracle Application Framework – OA Framework "For all the PL/SQL and Oracle Forms developers out there, start planning your evolution. Sure PL/SQL and Forms will be around for some time, but you need to add more skills to your stack if you want to stay current (employable)." -- Shivmohan Purohit (tags: oracle otn application framework) @ORACLENERD: APEX Architecture Oracle ACE Chet Justice offer a "short list of potential architectures" for Oracle APEX, based on his experience with a client. (tags: oracle otn oracleace apex architecture) Luis Moreno Campos: Why is Exadata so fast? "You could find a lot of tech doc around oracle.com, but the bottom line is that the vision to even build a V2 and place it as an OLTP and DW (general purpose) machine is just pure genius." -- Luis Moreno Campos (tags: oracle otn exadata database) Edwin Biemond: Resetting Weblogic datasources with ANT Oracle ACE and Whitehorses architect Edwin Biemond shares an ANT script "to fire some WLST and Python commandos" to correct invalid database session states. (tags: oracle otn oracleace database ANT Python) @deltalounge: The future of MySQL with Oracle Peter Paul van de Beek has compiled an informative collection of Edward Scriven quotes, from various publications, on Oracle's plans for MySQL. (tags: oracle otn database mysql) Cristobal Soto: Coherence Special Interest Group: First Meeting in Toronto, Upcoming Events in New York and California Cameron Purdy, Patrick Peralta, and others are speaking at upcoming Coherence SIG events. Cristobal Soto shares the details. (tags: oracle otn coherence sig grid appserver)

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  • ArchBeat Link-o-Rama for 11/15/2011

    - by Bob Rhubart
    Java Magazine - November/December 2011 - by and for the Java Community Java Magazine is an essential source of knowledge about Java technology, the Java programming language, and Java-based applications for people who rely on them in their professional careers, or who aspire to. Enterprise 2.0 Conference: November 14-17 | Kellsey Ruppel "Oracle is proud to be a Gold sponsor of the Enterprise 2.0 West Conference, November 14-17, 2011 in Santa Clara, CA. You will see the latest collaboration tools and technologies, and learn from thought leaders in Enterprise 2.0's comprehensive conference." The Return of Oracle Wikis: Bigger and Better | @oracletechnet The Oracle Wikis are back - this time, with Oracle SSO on top and powered by Atlassian's Confluence technology. These wikis offer quite a bit more functionality than the old platform. Cloud Migration Lifecycle | Tom Laszewski Laszewski breaks down the four steps in the Set Up Phase of the Cloud Migration lifecycle. Architecture all day. Oracle Technology Network Architect Day - Phoenix, AZ - Dec14 Spend the day with your peers learning from Oracle experts in engineered systems, cloud computing, Oracle Coherence, Oracle WebLogic, and more. Registration is free, but seating is limited. SOA all the Time; Architects in AZ; Clearing Info Integration Hurdles This week on the Architect Home Page on OTN. Live Webcast: New Innovations in Oracle Linux Date: Tuesday, November 15, 2011 Time: 9:00 AM PT / Noon ET Speakers: Chris Mason, Elena Zannoni. People in glass futures should throw stones | Nicholas Carr "Remember that Microsoft video on our glassy future? Or that one from Corning? Or that one from Toyota?" asks Carr. "What they all suggest, and assume, is that our rich natural 'interface' with the world will steadily wither away as we become more reliant on software mediation." Integration of SABSA Security Architecture Approaches with TOGAF ADM | Jeevak Kasarkod Jeevak Kasarkod's overview of a new paper from the OpenGroup and the SABSA institute "which delves into the incorporatation of risk management and security architecture approaches into a well established enterprise architecture methodology - TOGAF." Cloud Computing at the Tactical Edge | Grace Lewis - SEI Lewis describes the SEI's work with Cloudlets, " lightweight servers running one or more virtual machines (VMs), [that] allow soldiers in the field to offload resource-consumptive and battery-draining computations from their handheld devices to nearby cloudlets." Simplicity Is Good | James Morle "When designing cluster and storage networking for database platforms, keep the architecture simple and avoid the complexities of multi-tier topologies," says Morle. "Complexity is the enemy of availability." Mainframe as the cloud? Tom Laszewski There's nothing new about using the mainframe in the cloud, says Laszewski. Let Devoxx 2011 begin! | The Aquarium The Aquarium marks the kick-off of Devoxx 2011 with "a quick rundown of the Java EE and GlassFish side of things."

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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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  • Adventures in MVVM &ndash; ViewModel Location and Creation

    - by Brian Genisio's House Of Bilz
    More Adventures in MVVM In this post, I am going to explore how I prefer to attach ViewModels to my Views.  I have published the code to my ViewModelSupport project on CodePlex in case you'd like to see how it works along with some examples.  Some History My approach to View-First ViewModel creation has evolved over time.  I have constructed ViewModels in code-behind.  I have instantiated ViewModels in the resources sectoin of the view. I have used Prism to resolve ViewModels via Dependency Injection. I have created attached properties that use Dependency Injection containers underneath.  Of all these approaches, I continue to find issues either in composability, blendability or maintainability.  Laurent Bugnion came up with a pretty good approach in MVVM Light Toolkit with his ViewModelLocator, but as John Papa points out, it has maintenance issues.  John paired up with Glen Block to make the ViewModelLocator more generic by using MEF to compose ViewModels.  It is a great approach, but I don’t like baking in specific resolution technologies into the ViewModelSupport project. I bring these people up, not to name drop, but to give them credit for the place I finally landed in my journey to resolve ViewModels.  I have come up with my own version of the ViewModelLocator that is both generic and container agnostic.  The solution is blendable, configurable and simple to use.  Use any resolution mechanism you want: MEF, Unity, Ninject, Activator.Create, Lookup Tables, new, whatever. How to use the locator 1. Create a class to contain your resolution configuration: public class YourViewModelResolver: IViewModelResolver { private YourFavoriteContainer container = new YourFavoriteContainer(); public YourViewModelResolver() { // Configure your container } public object Resolve(string viewModelName) { return container.Resolve(viewModelName); } } Examples of doing this are on CodePlex for MEF, Unity and Activator.CreateInstance. 2. Create your ViewModelLocator with your custom resolver in App.xaml: <VMS:ViewModelLocator x:Key="ViewModelLocator"> <VMS:ViewModelLocator.Resolver> <local:YourViewModelResolver /> </VMS:ViewModelLocator.Resolver> </VMS:ViewModelLocator> 3. Hook up your data context whenever you want a ViewModel (WPF): <Border DataContext="{Binding YourViewModelName, Source={StaticResource ViewModelLocator}}"> This example uses dynamic properties on the ViewModelLocator and passes the name to your resolver to figure out how to compose it. 4. What about Silverlight? Good question.  You can't bind to dynamic properties in Silverlight 4 (crossing my fingers for Silverlight 5), but you CAN use string indexing: <Border DataContext="{Binding [YourViewModelName], Source={StaticResource ViewModelLocator}}"> But, as John Papa points out in his article, there is a silly bug in Silverlight 4 (as of this writing) that will call into the indexer 6 times when it binds.  While this is little more than a nuisance when getting most properties, it can be much more of an issue when you are resolving ViewModels six times.  If this gets in your way, the solution (as pointed out by John), is to use an IndexConverter (instantiated in App.xaml and also included in the project): <Border DataContext="{Binding Source={StaticResource ViewModelLocator}, Converter={StaticResource IndexConverter}, ConverterParameter=YourViewModelName}"> It is a bit uglier than the WPF version (this method will also work in WPF if you prefer), but it is still not all that bad.  Conclusion This approach works really well (I suppose I am a bit biased).  It allows for composability from any mechanisim you choose.  It is blendable (consider serving up different objects in Design Mode if you wish... or different constructors… whatever makes sense to you).  It works in Cider.  It is configurable.  It is flexible.  It is the best way I have found to manage View-First ViewModel hookups.  Thanks to the guys mentioned in this article for getting me to something I love using.  Enjoy.

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  • links for 2010-05-05

    - by Bob Rhubart
    MASON Multiagent Simulation Toolkit on the NetBeans Platform Geertjan shares his recent work with George Mason University's Java-based multiagent simulation library core. (tags: java oracle netbeans) Slides: Oracle Virtualization: Making Software Easier to Deploy, Manage, and Support Slides from a presentation by Dean Samuels and Nirmal Grewal. (tags: oracle otn architect virtualization) @mayureshnirhali: Virtualizing Your Applications - Oracle Tech Days - Hyderbad 2010 Mayuresh Nirhali shares a video of his session describing support for various Virtualization technologies on the Oracle Solaris platform. (tags: oracle otn solaris virtualization)

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  • Algorithm to Find the Aggregate Mass of "Granola Bar"-Like Structures?

    - by Stuart Robbins
    I'm a planetary science researcher and one project I'm working on is N-body simulations of Saturn's rings. The goal of this particular study is to watch as particles clump together under their own self-gravity and measure the aggregate mass of the clumps versus the mean velocity of all particles in the cell. We're trying to figure out if this can explain some observations made by the Cassini spacecraft during the Saturnian summer solstice when large structures were seen casting shadows on the nearly edge-on rings. Below is a screenshot of what any given timestep looks like. (Each particle is 2 m in diameter and the simulation cell itself is around 700 m across.) The code I'm using already spits out the mean velocity at every timestep. What I need to do is figure out a way to determine the mass of particles in the clumps and NOT the stray particles between them. I know every particle's position, mass, size, etc., but I don't know easily that, say, particles 30,000-40,000 along with 102,000-105,000 make up one strand that to the human eye is obvious. So, the algorithm I need to write would need to be a code with as few user-entered parameters as possible (for replicability and objectivity) that would go through all the particle positions, figure out what particles belong to clumps, and then calculate the mass. It would be great if it could do it for "each" clump/strand as opposed to everything over the cell, but I don't think I actually need it to separate them out. The only thing I was thinking of was doing some sort of N2 distance calculation where I'd calculate the distance between every particle and if, say, the closest 100 particles were within a certain distance, then that particle would be considered part of a cluster. But that seems pretty sloppy and I was hoping that you CS folks and programmers might know of a more elegant solution? Edited with My Solution: What I did was to take a sort of nearest-neighbor / cluster approach and do the quick-n-dirty N2 implementation first. So, take every particle, calculate distance to all other particles, and the threshold for in a cluster or not was whether there were N particles within d distance (two parameters that have to be set a priori, unfortunately, but as was said by some responses/comments, I wasn't going to get away with not having some of those). I then sped it up by not sorting distances but simply doing an order N search and increment a counter for the particles within d, and that sped stuff up by a factor of 6. Then I added a "stupid programmer's tree" (because I know next to nothing about tree codes). I divide up the simulation cell into a set number of grids (best results when grid size ˜7 d) where the main grid lines up with the cell, one grid is offset by half in x and y, and the other two are offset by 1/4 in ±x and ±y. The code then divides particles into the grids, then each particle N only has to have distances calculated to the other particles in that cell. Theoretically, if this were a real tree, I should get order N*log(N) as opposed to N2 speeds. I got somewhere between the two, where for a 50,000-particle sub-set I got a 17x increase in speed, and for a 150,000-particle cell, I got a 38x increase in speed. 12 seconds for the first, 53 seconds for the second, 460 seconds for a 500,000-particle cell. Those are comparable speeds to how long the code takes to run the simulation 1 timestep forward, so that's reasonable at this point. Oh -- and it's fully threaded, so it'll take as many processors as I can throw at it.

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  • Going For Gold: AngloGold Ashanti and Oracle Spatial 11g

    - by stephen.garth
    Last chance - Register Now for Free Webinar Date and Time: Thursday May 6 at 11:00am PDT (2:00pm EDT) Check out this 1-hour Directions Media webinar to learn how the world's 3rd largest gold miner has implemented a unique geospatial data infrastructure based on Oracle Spatial 11g to streamline their business processes for gold exploration. Terry Harbort, Exploration Systems Architect with AngloGold Ashanti, will provide insights into the company's use of Oracle Spatial 11g GeoRaster, 3D visualization techniques, Real Application Clusters, and more. The presentation is followed by a live Q&A session. Register Here

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  • The busy developers guide to the Kinect SDK Beta

    - by mbcrump
    The Kinect is awesome. From day one, I’ve said this thing has got potential. After playing with several open-source Kinect projects, I am please to announce that Microsoft has released the official SDK beta on 6/16/2011. I’ve created this quick start guide to get you up to speed in no time flat. Let’s begin: What is it? The Kinect for Windows SDK beta is a starter kit for applications developers that includes APIs, sample code, and drivers. This SDK enables the academic research and enthusiast communities to create rich experiences by using Microsoft Xbox 360 Kinect sensor technology on computers running Windows 7. (defined by Microsoft) Links worth checking out: Download Kinect for Windows SDK beta – You can either download a 32 or 64 bit SDK depending on your OS. Readme for Kinect for Windows SDK Beta from Microsoft Research  Programming Guide: Getting Started with the Kinect for Windows SDK Beta Code Walkthroughs of the samples that ship with the Kinect for Windows SDK beta (Found in \Samples Folder) Coding4Fun Kinect Toolkit – Lots of extension methods and controls for WPF and WinForms. Kinect Mouse Cursor – Use your hands to control things like a mouse created by Brian Peek. Kinect Paint – Basically MS Paint but use your hands! Kinect for Windows SDK Quickstarts Installing and Using the Kinect Sensor Getting it installed: After downloading the Kinect SDK Beta, double click the installer to get the ball rolling. Hit the next button a few times and it should complete installing. Once you have everything installed then simply plug in your Kinect device into the USB Port on your computer and hopefully you will get the following screen: Once installed, you are going to want to check out the following folders: C:\Program Files (x86)\Microsoft Research KinectSDK – This contains the actual Kinect Sample Executables along with the documentation as a CHM file. Also check out the C:\Users\Public\Documents\Microsoft Research KinectSDK Samples directory: The main thing to note here is that these folders contain the source code to the applications where you can compile/build them yourself. Audio NUI DEMO Time Let’s get started with some demos. Navigate to the C:\Program Files (x86)\Microsoft Research KinectSDK folder and double click on ShapeGame.exe. Next up is SkeletalViewer.exe (image taken from http://www.i-programmer.info/news/91-hardware/2619-microsoft-launch-kinect-sdk-beta.html as I could not get a good image using SnagIt) At this point, you will have to download Kinect Mouse Cursor – This is really cool because you can use your hands to control the mouse cursor. I actually used this to resize itself. Last up is Kinect Paint – This is very cool, just make sure you read the instructions! MS Paint on steroids! A few tips for getting started building Kinect Applications. It appears WPF is the way to go with building Kinect Applications. You must also use a version of Visual Studio 2010.  Your going to need to reference Microsoft.Research.Kinect.dll when building a Kinect Application. Right click on References and then goto Browse and navigate to C:\Program Files (x86)\Microsoft Research KinectSDK and select Microsoft.Research.Kinect.dll. You are going to want to make sure your project has the Platform target set to x86. The Coding4Fun Kinect Toolkit really makes things easier with extension methods and controls. Just note that this is for WinForms or WPF. Conclusion It looks like we have a lot of fun in store with the Kinect SDK. I’m very excited about the release and have already been thinking about all the applications that I can begin building. It seems that development will be easier now that we have an official SDK and the great work from Coding4Fun. Please subscribe to my blog or follow me on twitter for more information about Kinect, Silverlight and other great technology.  Subscribe to my feed

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  • ArchBeat Top 10 for November 11-17, 2012

    - by Bob Rhubart
    The Top 10 most popular items shared on the OTN ArchBeat Facebook page for the week of November 11-17, 2012. Developing and Enforcing a BYOD Policy Darin Pendergraft's post includes links to a recent Mobile Access Policy Survey by SANS as well as registration information for a Nov 15 webcast featuring security expert Tony DeLaGrange from Secure Ideas, SANS instructor, attorney and technology law expert Ben Wright, and Oracle IDM product manager Lee Howarth. This Week on the OTN Architect Community Homepage Make time to check out this week's features on the OTN Solution Architect Homepage, including: SOA Practitioner Guide: Identifying and Discovering Services Technical article by Yuli Vasiliev on Setting Up, Configuring, and Using an Oracle WebLogic Server Cluster The conclusion of the 3-part OTN ArchBeat Podcast on Future-Proofing your career. WLST Starting and Stopping a WebLogic Environment | Rene van Wijk Oracle ACE Rene van Wijk explores how to start a server with as little input as possible. Cloud Integration White Paper | Bruce Tierney Bruce Tierney shares an overview of Cloud Integration - A Comprehensive Solution, a new white paper he co-authored with David Baum, Rajesh Raheja, Bruce Tierney, and Vijay Pawar. X.509 Certificate Revocation Checking Using OCSP protocol with Oracle WebLogic Server 12c | Abhijit Patil Abhijit Patil's article focuses on how to use X.509 Certificate Revocation Checking Functionality with the OCSP protocol to validate in-bound certificates. Although this article focuses on inbound OCSP validation using OCSP, Oracle WebLogic Server 12c also supports outbound OCSP validation. Update on My OBIEE / Exalytics Books | Mark Rittman Oracle ACE Director Mark Rittman shares several resources related to his books Oracle Business Intelligence 11g Developers Guide and Oracle Exalytics Revealed, including a podcast interview with Oracle's Paul Rodwick. E-Business Suite 12.1.3 Data Masking Certified with Enterprise Manager 12c | Elke Phelps "You can use the Oracle Data Masking Pack with Oracle Enterprise Manager Grid Control 12c to scramble sensitive data in cloned E-Business Suite environments," reports Elke Phelps. There's a lot more information about this announcement in Elke's post. WebLogic Application Server: free for developers! | Bruno Borges Java blogger Bruno Borges shares news about important changes in the license agreement for Oracle WebLogic Server. Agile Architecture | David Sprott "There is ample evidence that Agile Architecture is a primary contributor to business agility, yet we do not have a well understood architecture management system that integrates with Agile methods," observes David Sprott in this extensive post. My iPad & This Cloud Thing | Floyd Teter Oracle ACE Director Floyd Teter explains why the Cloud is making it possible for him to use his iPad for tasks previously relegated to his laptop, and why this same scenario is likely to play out for a great many people. Thought for the Day "In programming, the hard part isn't solving problems, but deciding what problems to solve." — Paul Graham Source: SoftwareQuotes.com

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  • Various roles in an Organization and their respective tasks.

    - by balu
    In various organizations(Software Company) there would be various designations having different roles. I would like to know the Industry accepted & followed trend in the organization hierarchy(..Like DBA,System Architect,Project Manager,Senior Developer,Developer,QA,Design Team,Delivery Manager etc..).And the various roles played by each of them in the various stages of the Software Development Life Cycle.Who all could possibly be sharing the responsibility mutually?

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  • Surface V2.0

    - by Dennis Vroegop
    It’s been quiet around here. And the reason for that is that it’s been quiet around Surface for a while. Now, a lot of people assume that when a product team isn’t making too much noise that must mean they stopped working on their product. Remember the PDC keynote in 2010? Just because they didn’t mention WPF there a lot of people had the idea that WPF was dead and abandoned for Silverlight. Of course, this couldn’t be farther from the truth. The same applies to Surface. While we didn’t hear much from the team in Redmond they were busy putting together the next version of the platform. And at the CES in January the world saw what they have been up to all along: Surface V2.0 as it’s commonly known. Of course, the product is still in development. It’s not here yet, we can’t buy one yet. However, more and more information comes available and I think this is a good time to share with you what it’s all about! The biggest change from an organizational point of view is that Microsoft decided to stop producing the hardware themselves. Instead, they have formed a partnership with Samsung who will manufacture the devices. This means that you as a buyer get the benefits of a large, worldwide supplier with all the services they can offer. Not that Microsoft didn’t do that before but since Surface wasn’t a ‘big’ product it was sometimes hard to get to the right people. The new device is officially called the “Samsung SUR 40 for Microsoft Surface” which is quite a mouthful. The software that runs the device is of course still coming from Microsoft. Let’s dive into the technical specs (note: all of this is preliminary, it’s still in the Alpha phase!): Audio out HDMI / StereoRCA / SPDIF / 2 times 3.5mm audio out jack Brightness 300 CD/m2 Communications 1GB Ethernet/802.11/Bluetooth Contrast Ratio 1:1000 CPU AMD Athlon X2 245e 2.9Ghz Dual Core Display Resolution Full HD 1080p 1920x1080 / 16:9 aspect ratio GPU AMD Radeon HD 6750 1GB GDDRS HDD 320 GB / 7200 RPM HDMI In / HDMI out Yes I/O Ports 4 USB, SD Card reader Operation System Embedded Windows 7 Professional 64 bits Panel Size 40” diagonal Protection Glass Gorilla Glass RAM 4 GB DD3 Weight / with standard legs 70.0 Kg / 154 lbs Weight / standalone 39.5 Kg / 87 lbs Height (without legs) 4 inch Contact points recognized > 50 Cool Factor Extremely   Ok, the last point is not official, but I do think it needs to be there. Let’s talk software. As noted, it runs Windows 7 Professional 64 bit, which means you can run Visual Studio 2010 on it. The software is going to be developed in WPF4.0 with the additional Surface SDK 2.0. It will contain all the things you’ve seen before plus some extra’s. They have taken some steps to align it more with the Surface Toolkit which you can download today, so if you do things right your software should be portable between a WPF4.0 Windows 7 Multi-touch app and the Surface v2 environment. It still uses infrared to detect contacts, so in that respect nothing much has changed conceptually. We still can differentiate between a finger, a tag or a blob. Of course, since the new platform has a much higher resolution (compared to the 1024x768 of the first version) you might need to look at your code again. I’ve seen a lot of applications on Surface that assume the old resolution and moving that to V2 is going to be some work. To be honest: as I am under NDA I cannot disclose much about the new software besides what I have told you here, but trust me: it’s going to blow people away. Now, the biggest question for me is: when can I get one? Until we can, have a look here: Tags van Technorati: surface,samsung,WPF

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  • Oracle's Fusion User Experience Raises the Bar

    Hear Jeremy Ashley, Oracle's Vice President of Applications User Experience, and Patanjali Venkatacharya, Applications User Experience Architect, speak with Cliff about Oracle's innovative user experience methodology and the benefits it provides customers.

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  • Webcast Replay Available: E-Business Suite Data Protection

    - by BillSawyer
    I am pleased to release the replay and presentation for the latest ATG Live Webcast: E-Business Suite Data Protection (Presentation)   Robert Armstrong, Product Strategy Security Architect and Eric Bing, Senior Director discussed the best practices and recommendations for securing your E-Business Suite data.Finding other recorded ATG webcasts The catalog of ATG Live Webcast replays, presentations, and all ATG training materials is available in this blog's Webcasts and Training section.

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  • Tellago keeps hiring

    - by gsusx
    Tellago keeps growing and hiring very aggressively. We were recently received the American Business Award to the best company in the United States, under a 100 people, in the computer services industry ( More details about that in a future post J ) We are currently looking for architects to join our SOA and SharePoint practices. If you are a brilliant developer or architect with expertise on technologies such as WCF, WF or BizTalk Server, you are passionate about technologies and crazy enough to...(read more)

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  • links for 2011-02-09

    - by Bob Rhubart
    Tech Cast Live - Java and Oracle, One Year Later - February 15th 10AM PST (Oracle Technology Network Blog (aka TechBlog)) (tags: ping.fm) The impact of IT decisions on organizational culture - O'Reilly Radar "While I believe we recognize the limiting qualities of IT decisions, I'd suggest we've insufficiently studied the degree to which those decisions in aggregate can have a large influence on organizational culture." - Jonathan Reichental, Ph.D. (tags: ITgovernance organizationalculture enterprisearchitecture) Women "computers" of World War II - Boing Boing "Before it came to mean laptops, PCs, or even room-sized machines, "computer" was what you called a person who did mathematical calculations for a living. That job was vitally important during World War II. And, like many vital jobs on the homefront, it was turned over to women..." (tags: computers history worldwar2) InfoQ: Book Excerpt and Interview: 100 SOA Questions Asked and Answered A new "100 SOA Questions Asked and Answered " book by Kerrie Holley and Ali Arsanjani provides a deep insight into SOA covering a wide spectrum of topics from SOA basics to its business and organizational impact, to SOA methods and architecture to SOA future. InfoQ spoke with Kerrie Holley and Ali Arsanjani about their book. (tags: ping.fm) @myfear: GlassFish City - Another view onto your favorite application server Oracle ACE Director Markus Eisele runs GlassFish through CodeCity. (tags: oracle otn oracleace glassfish codecity) The Ron Batra Blog: Technology Whispers: Upcoming Presentations Oracle ACE Director Ron Batra shares details on upcoming presentations at OAUG events in the US and Dubai. (tags: oaug c11 oracle otn oracleace) Free ADF Training Event in the UK (Grant Ronald's Blog) Gobsmack survivor Grant Ronald with the details on an Oracle ADF training session he'll conduct on 11 May 2011 at the UK Oracle office in Reading. (tags: oracle otn adf) Java Spotlight Episode 16 - Richar Bair - The Java Spotlight Podcast The latest Java Spotlight podcast features an interview with Java Client Architect Richar Bair. (tags: oracle java podcast) Stewart Bryson: OBIEE 11g Migrations "[Rittman Mead's] Mark and Venkat have covered OBIEE migration methodologies in the past (see here, here and here), but I decided to throw my hat in the ring on the subject, as I had to develop a methodology for a client recently and wanted to share my experiences." - Stewart Bryson (tags: oracle otn obiee businessintelligence) Dr. Chris Harding: The golden thread of interoperability | Open Group Blog "There are so many things going on at every Conference by The Open Group that it is impossible to keep track of all of them, and this week’s Conference in San Diego, California, is no exception. The main themes are Cybersecurity, Enterprise Architecture, SOA and Cloud Computing." - Dr. Chris Harding (tags: entarch soa interoperability cloud) Marc Kelderman: OSB: Creating an Asynchronous / Fire-Forget WebService Call Creating a fire-and-forget call via OSB is simple, according to solution architect Marc Kelderman. "The trick is to send NO response back to the caller, only an HTTP response code, 200 or any other." (tags: oracle otn servicebus)

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  • What’s coming up

    - by GavinPayneUK
    In the last couple of months my community activities list has had things leave it and new things join it, so I thought share, and promote, my future plans. Microsoft Certified Architect : SQL Server – Giving back Preparing for my MCA Board was the hardest, yet in hindsight the most rewarding and interesting, thing I’ve ever done.  The subjects it covers still interest me to the extent that I’m now contributing to the MCA programme itself, allowing the next people through the certification’s...(read more)

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  • April 2010 Chicago Architects Group Meeting

    - by Tim Murphy
    The Chicago Architects Group will be holding its next meeting on April 20th.  Please come and join us and get involved in our architect community. Register Presenter: Matt Hidinger Topic: Onion Architecture      Location: Illinois Technology Association 200 S. Wacker Dr., Suite 1500 Room A/B Chicago, IL 60606 Time: 5:30 - Doors open at 5:00 del.icio.us Tags: Chicago Architects Group,Data Integration Architecture,Mike Vogt

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  • NHibernate 3 Webcast - Open to Public – Thursday from Pluralsight

    This week for the very first time, we're giving all newsletter subscribers FREE access to our exclusive weekly webcast! Join us Thursday for a 45 minute presentation on NHibernate 3 presented by James Kovacs. James is an independent architect, developer, trainer and jack-of-all-trades. He also happens to be the instructor for our upcoming NHibernate virtual classroom course next week. LiveMeeting Login Add to outlook calendar Thursday 20 Jan 2011 - 09:30PM IST, 11:00 AM EST , 16:00 UTC span.fullpost {display:none;}

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  • ATG Live Webcast March 21 Reminder: Network, WAN, and PC Performance Tuning (Performance Series Part 3 of 3)

    - by BillSawyer
    A quick reminder about tomorrow's webcast:  Andy Tremayne, Senior Architect, Applications Performance, and co-author of Oracle Applications Performance Tuning Handbook from Oracle Press, and Uday Moogala, Senior Principal Engineer, Applications Performance, will discuss network performance for E-Business Suite. Andy and Uday will cover tuning the client and tuning the network. They will share real-life examples of network performance, and show you tools and techniques that you can use to estimate or simulate performance on your own network.The agenda for the Performance Tuning - Part 3 of 3 webcast includes the following topics: Tuning the Client Tuning the Network Date:               Thursday, March 21, 2012Time:              8:00 AM - 9:00 AM Pacific Standard TimePresenters:  Andy Tremayne, Senior Architect, Applications Performance                        Uday Moogala, Senior Principal Engineer, Applications PerformanceWebcast Registration Link (Preregistration is optional but encouraged)To hear the audio feed:   Domestic Participant Dial-In Number:           877-697-8128    International Participant Dial-In Number:      706-634-9568    Additional International Dial-In Numbers Link:    Dial-In Passcode:                                              99341To see the presentation:    The Direct Access Web Conference details are:    Website URL: https://ouweb.webex.com    Meeting Number:  591264961If you miss the webcast, or you have missed any webcast, don't worry -- we'll post links to the recording as soon as it's available from Oracle University.  You can monitor this blog for pointers to the replay. And, you can find our archive of our past webcasts and training here.

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  • Tab Sweep: Primefaces3, @DataSourceDefinition, JPA Extensions, EclipseLink, Typed Query, Ajax, ...

    - by arungupta
    Recent Tips and News on Java, Java EE 6, GlassFish & more : • JSF2 + Primefaces3 + EJB3 & JPA2 Integration Project (@henk53) • The state of @DataSourceDefinition in Java EE (@henk53) • Java Persistence API (JPA) Extensions Reference for EclipseLink (EclipseLink) • JavaFX 2.2 Pie Chart with JPA 2.0 (John Yeary) • Typed Query RESTful Service Example (John Yeary) • How to set environment variables in GlassFish admin console (Jelastic) • Architect Enterprise Applications with Java EE (Oracle University) • Glassfish – Basic authentication (Marco Ghisellini) • Solving GlassFish 3.1/JSF PWC4011 warning (Rafael Nadal) • PrimeFaces AJAX Enabled (John Yeary)

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