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  • VirtualService for ESB

    This article describes the design, implementation, and usage of VirtualService for the Enterprise Service Bus, using the Microsoft .NET FX 3.5 technology.

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  • Benefits of TOGAF or similar?

    - by Lunatik
    I can read the website blurb and be impressed by the alleged benefits, but I haven't worked anywhere or with anyone who followed the TOGAF (or any alternative) architecture framework. Our organisation has declared itself dedicated to moving from what is currently a fairly shambolic design & development model towards something approaching a modern structured process. Things like TOGAF have been mentioned as helping achieve a world-class enterprise development environment (!) but I'm convinced that no-one here really understands the real-world benefits that wholesale adoption might bring and, perhaps more importantly, the effort/pain required to achieve the same. Do you have experience in using TOGAF or similar to wrestle control in an organisation? Do you think that use of the framework brought any benefit? Edit: For clarification TOGAF is "The Open Group Architecture Framework", a detailed method and set of tools for developing an enterprise architecture. See: http://www.opengroup.org/architecture/togaf8-doc/arch/

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  • Red Hat Entreprise Edition 5.9 disponible en version Beta, cette nouvelle version inclut un driver Microsoft Hyper-V

    RHEL 5.9 disponible en version Beta Cette nouvelle version inclut un driver Microsoft Hyper-V Red Hat a dévoilé la disponibilité de la Beta de la prochaine version mineure de Red Hat Enterprise Linux 5, numérotée 5.9. Celle-ci intègre notamment des pilotes pour Microsoft Hyper-V comme technologie de virtualisation. [IMG]http://idelways.developpez.com/news/images/Red_hat_logo.png[/IMG] Grâce à la version RHEL5, les clients peuvent exécuter Red Hat Enterprise Linux 5.9 en tant qu'invité virtuel Hyper-V avec des performances élevées. Cette bêta de RHEL 5.9 inclut la version 5 de l'utilitaire rsyslog, utilisé pour transférer en réseau les messages des journau...

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  • How to gain understanding of large systems? [closed]

    - by vonolsson
    Possible Duplicate: How do you dive into large code bases? I have worked as a developer developing C/C++ applications for mobile platforms (Windows Mobile and Symbian) for about six years. About a year ago, however, I changed job and currently work with large(!) enterprise systems with high security and availability requirements, all developed in Java. My problem is that I am having a hard time getting a grip on the architecture of the systems, even after a year working with them, and understanding systems other people have built has never been my strong side. The fact that I haven't worked with enterprise systems before doesn't exactly help. Does anyone have a good approach on how to learn and understand large systems? Are there any particular techniques and/or patterns I should read up on?

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  • SQL – Quick Start with Admin Sections of NuoDB – Manage NuoDB Database

    - by Pinal Dave
    In the yesterday’s blog post we have seen that it is extremely easy to install the NuoDB database on your local machine. Now that the application is properly set up, let us explore NuoDB a bit more and get you familiar with the how it works and what the important areas of the NuoDB are that you should learn. As we have already installed NuoDB, now we will quickly start with two of the important areas in NuoDB: 1) Admin and 2) Explorer. In this blog post I will explore how the Admin Section of the NuoDB Console works.  In the next blog post we will learn how the Explorer Section works. Let us go to the NuoDB Console by typing the following URL in your browser: http://localhost:8080/ It will bring you to the following screen: On this screen you can see a big Start QuickStart button. Click on the button and it will bring you to following screen. On this screen you will find very important information about Domain and Database Settings. It is our habit that we do not read what is written on the screen and keep on clicking on continue without reading. While we are familiar with most wizards, we can often miss the very important message on the screen. Please note the information of Domain Settings and Database Settings from the following screen before clicking on Create Database. Domain Settings User: quickstart Password: quickstart Database Settings User: dba Password: goalie Database: test Schema: HOCKEY Once you click on the Create Database button it will immediately start creating sample database. First, it will start a Storage Manager and right after that it will start a Transaction Engine. Once the engine is up, it will Create a Schema and Sample Data. On the success of the creating the sample database it will show the following screen. Now is the time where we can explore the NuoDB Admin or NuoDB Explorer. If you click on Admin, it will first show following login screen. Enter for the username “domain” and for the password “bird”. Alternatively you can enter “quickstart”  twice for username and password.  It works as too. Once you enter into the Admin Section, on the left side you can see information about NuoDB and Admin Console and on the right side you can see the domain overview area. From this Administrative section you can do any of the following tasks: Create a view of the entire domain Add and remove databases Start and stop NuoDB Transaction Engines and Storage Managers Monitor transaction across all the NuoDB databases On the right side of the Admin Section we can see various information about a particular NuoDB domain. You can quickly view various alerts, find out information about the number of host machines that are provisioned for the domain, and see the number of databases and processes that are running in the domain. If you click on the “1 host” link you will be able to see various processes, CPU usage and other information. In the Processes Section you can see that there are two different types of processes. The first process (where you can see the floppy drive icon) represents a running Storage Manager process and the second process a running Transaction Engine process. You can click on the links for the Storage Manager and Transaction Engine to see further statistical details right down to the last byte of the data. There are various charts available for analysis as well. I think the product is quite mature and the user can add different monitor charts to the Admin section. Additionally, the Admin section is the place where you can create and manage new databases. I hope today’s tutorial gives you enough confidence that you can try out NuoDB and checkout various administrative activities with the database. I am personally impressed with their dashboard related to various counters. For more information about how the NuoDB architecture works and what a Storage Manager or Transaction Engine does, check out this short video with NuoDB CTO Seth Proctor:  In the next blog post, we will try out the Explorer section of NuoDB, which allows us to run SQL queries and write SQL code.  Meanwhile, I strongly suggest you download and install NuoDB and get yourself familiar with the product. Reference: Pinal Dave (http://blog.sqlauthority.com) Filed under: Big Data, PostADay, SQL, SQL Authority, SQL Query, SQL Server, SQL Tips and Tricks, T SQL, Technology Tagged: NuoDB

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  • A SharePoint Developer&rsquo;s Toolchest

    - by Sahil Malik
    Ad:: SharePoint 2007 Training in .NET 3.5 technologies (more information). When we develop for SharePoint, we end up using many tools, third party or Microsoft, to facilitate our development. What are some of your favorite tools? Mine are as below - 1. Reflector: When I saw reflector, I was pretty convinced that a tool better and more useful than it doesn’t exist. Well I was wrong! Redgate took over reflector and they still offer it as a free version, but they have a paid version called reflector pro. It lets you debug third party source code, as if you had the source code. Brilliant! Who needs documentation anymore when you have real code? 2. ULS Viewer: It is no secret, reading ULS logs is a pain in the rear. Well, not so with ULS Viewer, which does work with SharePoint 2007 as well. But it’s just way cooler with SharePoint 2010. You know when you get an error in SharePoint 2010 it shows you an error like as below: Well, the ULS Viewer will allow you to set filtering critereon, allowing you to immediately zero in, into an error, across multiple WFEs even. Also there are numerous other facilities built into the tool, such as advanced filtering, critical error notifications, etc. A must have! You can read the documentation of the ULSViewer here. 3. SPDisposeCheck: Did you know that the MySite object is strange? What is strange about it? That you have to dispose it even if you didn’t create it!? Well who the hell remembers all that! Honestly I do! And you should too. But there is a tool to help you sanitize your code. And that is SPDisposeCheck. You run it against your DLL or EXE, and it will give you suggestions on where you might have missed calling dispose on an object. You still have to use your head, but having this tool helps. 4. DebugView: Debugging for SharePoint can be difficult sometimes. Sometimes your breakpoints don’t get hit. And while you can try and make them hit, it is sometimes easier to just write a bunch of Debug.WriteLines, and catch them from an external application such as DebugView. You simply use your code, and DebugView will catch all the Debug.WriteLine’s in your code like this - 5. BGInfo: One annoying thing about SharePoint projects, it causes the number of servers to multiply like bunnies. As I’m RDP’ing into many computers trying to diagnose a crazy issue, sometimes it becomes hard to remember which machine is which. BGInfo puts all that on the wallpaper, alongwith a bunch of other useful info. A bit like this - 5. WSPBuilder: SharePoint 2007 only, but I think there maybe a version for SP2010 coming later. I think the VS2010 tools for SP2010 development are quite nice, so WSPBuilder, well so far I don’t miss it. But lets see what WSPBuilder for 2010 brings – I haven’t seen it yet. However, I want to confidently assert that WSPBuilder for SP2007 is simply awesome. 6. SharePoint Manager: The SharePoint Manager 2010 is a SharePoint object model explorer. It enables you to browse every site on the local farm and view every property. It also enables you to change the properties. The VS2010 dev tools now include a server explorer, which show you a subset of properties in read-only. I would LOVE to see SharePoint manager like functionality built into VS2010. SharePoint Manager, a total must-have. Comment on the article ....

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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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  • SQL SERVER – ASYNC_IO_COMPLETION – Wait Type – Day 11 of 28

    - by pinaldave
    For any good system three things are vital: CPU, Memory and IO (disk). Among these three, IO is the most crucial factor of SQL Server. Looking at real-world cases, I do not see IT people upgrading CPU and Memory frequently. However, the disk is often upgraded for either improving the space, speed or throughput. Today we will look at another IO-related wait type. From Book On-Line: Occurs when a task is waiting for I/Os to finish. ASYNC_IO_COMPLETION Explanation: Any tasks are waiting for I/O to finish. If by any means your application that’s connected to SQL Server is processing the data very slowly, this type of wait can occur. Several long-running database operations like BACKUP, CREATE DATABASE, ALTER DATABASE or other operations can also create this wait type. Reducing ASYNC_IO_COMPLETION wait: When it is an issue related to IO, one should check for the following things associated to IO subsystem: Look at the programming and see if there is any application code which processes the data slowly (like inefficient loop, etc.). Note that it should be re-written to avoid this  wait type. Proper placing of the files is very important. We should check the file system for proper placement of the files – LDF and MDF on separate drive, TempDB on another separate drive, hot spot tables on separate filegroup (and on separate disk), etc. Check the File Statistics and see if there is a higher IO Read and IO Write Stall SQL SERVER – Get File Statistics Using fn_virtualfilestats. Check event log and error log for any errors or warnings related to IO. If you are using SAN (Storage Area Network), check the throughput of the SAN system as well as configuration of the HBA Queue Depth. In one of my recent projects, the SAN was performing really badly and so the SAN administrator did not accept it. After some investigations, he agreed to change the HBA Queue Depth on the development setup (test environment). As soon as we changed the HBA Queue Depth to quite a higher value, there was a sudden big improvement in the performance. It is very likely to happen that there are no proper indexes on the system and yet there are lots of table scans and heap scans. Creating proper index can reduce the IO bandwidth considerably. If SQL Server can use appropriate cover index instead of clustered index, it can effectively reduce lots of CPU, Memory and IO (considering cover index has lesser columns than cluster table and all other; it depends upon the situation). You can refer to the following two articles I wrote that talk about how to optimize indexes: Create Missing Indexes Drop Unused Indexes Checking Memory Related Perfmon Counters SQLServer: Memory Manager\Memory Grants Pending (Consistent higher value than 0-2) SQLServer: Memory Manager\Memory Grants Outstanding (Consistent higher value, Benchmark) SQLServer: Buffer Manager\Buffer Hit Cache Ratio (Higher is better, greater than 90% for usually smooth running system) SQLServer: Buffer Manager\Page Life Expectancy (Consistent lower value than 300 seconds) Memory: Available Mbytes (Information only) Memory: Page Faults/sec (Benchmark only) Memory: Pages/sec (Benchmark only) Checking Disk Related Perfmon Counters Average Disk sec/Read (Consistent higher value than 4-8 millisecond is not good) Average Disk sec/Write (Consistent higher value than 4-8 millisecond is not good) Average Disk Read/Write Queue Length (Consistent higher value than benchmark is not good) Read all the post in the Wait Types and Queue series. Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussions of Wait Stats in this blog are generic and vary from system to system. It is recommended that you test this on a development server before implementing it to a production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Stats, SQL Wait Types, T SQL, Technology

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  • SQL SERVER – IO_COMPLETION – Wait Type – Day 10 of 28

    - by pinaldave
    For any good system three things are vital: CPU, Memory and IO (disk). Among these three, IO is the most crucial factor of SQL Server. Looking at real-world cases, I do not see IT people upgrading CPU and Memory frequently. However, the disk is often upgraded for either improving the space, speed or throughput. Today we will look at an IO-related wait types. From Book On-Line: Occurs while waiting for I/O operations to complete. This wait type generally represents non-data page I/Os. Data page I/O completion waits appear as PAGEIOLATCH_* waits. IO_COMPLETION Explanation: Any tasks are waiting for I/O to finish. This is a good indication that IO needs to be looked over here. Reducing IO_COMPLETION wait: When it is an issue concerning the IO, one should look at the following things related to IO subsystem: Proper placing of the files is very important. We should check the file system for proper placement of files – LDF and MDF on a separate drive, TempDB on another separate drive, hot spot tables on separate filegroup (and on separate disk),etc. Check the File Statistics and see if there is higher IO Read and IO Write Stall SQL SERVER – Get File Statistics Using fn_virtualfilestats. Check event log and error log for any errors or warnings related to IO. If you are using SAN (Storage Area Network), check the throughput of the SAN system as well as the configuration of the HBA Queue Depth. In one of my recent projects, the SAN was performing really badly so the SAN administrator did not accept it. After some investigations, he agreed to change the HBA Queue Depth on development (test environment) set up and as soon as we changed the HBA Queue Depth to quite a higher value, there was a sudden big improvement in the performance. It is very possible that there are no proper indexes in the system and there are lots of table scans and heap scans. Creating proper index can reduce the IO bandwidth considerably. If SQL Server can use appropriate cover index instead of clustered index, it can effectively reduce lots of CPU, Memory and IO (considering cover index has lesser columns than cluster table and all other; it depends upon the situation). You can refer to the two articles that I wrote; they are about how to optimize indexes: Create Missing Indexes Drop Unused Indexes Checking Memory Related Perfmon Counters SQLServer: Memory Manager\Memory Grants Pending (Consistent higher value than 0-2) SQLServer: Memory Manager\Memory Grants Outstanding (Consistent higher value, Benchmark) SQLServer: Buffer Manager\Buffer Hit Cache Ratio (Higher is better, greater than 90% for usually smooth running system) SQLServer: Buffer Manager\Page Life Expectancy (Consistent lower value than 300 seconds) Memory: Available Mbytes (Information only) Memory: Page Faults/sec (Benchmark only) Memory: Pages/sec (Benchmark only) Checking Disk Related Perfmon Counters Average Disk sec/Read (Consistent higher value than 4-8 millisecond is not good) Average Disk sec/Write (Consistent higher value than 4-8 millisecond is not good) Average Disk Read/Write Queue Length (Consistent higher value than benchmark is not good) Note: The information presented here is from my experience and there is no way that I claim it to be accurate. I suggest reading Book OnLine for further clarification. All the discussions of Wait Stats in this blog are generic and vary from system to system. It is recommended that you test this on a development server before implementing it to a production server. Reference: Pinal Dave (http://blog.SQLAuthority.com) Filed under: Pinal Dave, PostADay, SQL, SQL Authority, SQL Query, SQL Scripts, SQL Server, SQL Tips and Tricks, SQL Wait Types, SQL White Papers, T SQL, Technology

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  • Using 3G/UMTS in Mauritius

    After some conversation, threads in online forum and mailing lists I thought about writing this article on how to setup, configure and use 3G/UMTS connections on Linux here in Mauritius. Personally, I can only share my experience with Emtel Ltd. but try to give some clues about how to configure Orange as well. Emtel 3G/UMTS surf stick Emtel provides different surf sticks from Huawei. Back in 2007, I started with an E220 that wouldn't run on Windows Vista either. Nowadays, you just plug in the surf stick (ie. E169) and usually the Network Manager will detect the new broadband modem. Nothing to worry about. The Linux Network Manager even provides a connection profile for Emtel here in Mauritius and establishing the Internet connection is done in less than 2 minutes... even quicker. Using wvdial Old-fashioned Linux users might not take Network Manager into consideration but feel comfortable with wvdial. Although that wvdial is primarily used with serial port attached modems, it can operate on USB ports as well. Following is my configuration from /etc/wvdial.conf: [Dialer Defaults]Phone = *99#Username = emtelPassword = emtelNew PPPD = yesStupid Mode = 1Dial Command = ATDT[Dialer emtel]Modem = /dev/ttyUSB0Baud = 3774000Init2 = ATZInit3 = ATQ0 V1 E1 S0=0 &C1 &D2 +FCLASS=0Init4 = AT+cgdcont=1,"ip","web"ISDN = 0Modem Type = Analog Modem The values of user name and password are optional and can be configured as you like. In case that your SIM card is protected by a pin - which is highly advised, you might another dialer section in your configuration file like so: [Dialer pin]Modem = /dev/ttyUSB0Init1 = AT+CPIN=0000 This way you can "daisy-chain" your command to establish your Internet connection like so: wvdial pin emtel And it works auto-magically. Depending on your group assignments (dialout), you might have to sudo the wvdial statement like so: sudo wvdial pin emtel Orange parameters As far as I could figure out without really testing it myself, it is also necessary to set the Access Point (AP) manually with Orange. Well, although it is pretty obvious a lot of people seem to struggle. The AP value is "orange". [Dialer orange]Modem = /dev/ttyUSB0Baud = 3774000Init2 = ATZInit3 = ATQ0 V1 E1 S0=0 &C1 &D2 +FCLASS=0Init4 = AT+cgdcont=1,"ip","orange"ISDN = 0Modem Type = Analog Modem And you are done. Official Linux support from providers It's just simple: Forget it! The people at the Emtel call center are completely focused on the hardware and Mobile Connect software application provided by Huawei and are totally lost in case that you confront them with other constellations. For example, my wife's netbook has an integrated 3G/UMTS modem from Ericsson. Therefore, no need to use the Huawei surf stick at all and of course we use the existing software named Wireless Manager instead of. Now, imagine to mention at the help desk: "Ehm, sorry but what's Mobile Connect?" And Linux after all might give the call operator sleepless nights... Who knows? Anyways, I hope that my article and configuration could give you a helping hand and that you will be able to connect your Linux box with 3G/UMTS surf sticks here in Mauritius.

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  • How to get faster graphics in KVM? VNC is painfully slow with Haiku OS guest, Spice won't install and SDL doesn't work

    - by Don Quixote
    I've been coming up to speed on the Haiku operating system, an Open Source clone of BeOS 5 Pro. I'm using an Apple MacBook Pro as my development machine. Apple's BootCamp BIOS does not support more than four partitions on the internal hard drive. While I can set up extended and logical partitions, doing so will prevent any of the installed operating systems from booting. To run Haiku directly on the iron, I boot it off a USB stick. Using external storage is also helpful because I am perpetually out of filesystem space. While VirtualBox is documented to allow access to physical drives, I could not actually get it to work. Also VirtualBox can only use one of the host CPU's cores. While VB guests can be configured for more than one CPU, they are only emulated. A full build of the Haiku OS takes 4.5 under VB. I had the hope of reducing build times by using KVM instead, but it's not working nearly as well as VirtualBox did. The Linux Kernel Virtual Machine is broken in all manner of fundamental ways as seen from Haiku. But I'm a coder; maybe I could contribute to fixing some of those problems. The first problem I've got is that Haiku's video in virt-manager is quite painfully slow. When I drag Haiku windows around the desktop, they lag quite far behind where my mouse is. It's quite difficult to move a window to a precise position on the screen. Just imagine that the mouse was connected to the window title bar with a really stretchy spring. Also Haiku's mouse lags quite far behind where I have moved it. I found lots of Personal Package Archives that enable Spice from QEMU / KVM at the Ubuntu Personal Package Arhives. I tried a few of the PPAs but none of them worked; with one of them, the command "add-apt-repository" crashed with a traceback. There is a Wiki page about Spice, but it says that it only works on 64-bit. My Early 2006 MacBook Pro is 32-bit. Its Apple Model Identifier is MacBookPro1,1; these use Core Duos NOT Core 2 Duos. I don't mind building a source deb for 32-bit if I can expect it to work. Is there some reason that Spice should be 64-bit only? Does it need features of the x86_64 Instruction Set Architecture that x86 does not have? When I try using SDL from virt-manager, the configuration for Local SDL Window says "Xauth: /home/mike/.Xauthority". When I try to start my guest, virt-manager emits an error. When I Googled the error message, the usual solution was to make ~/.Xauthority readible. However, .Xauthorty does not exist in my home directory. Instead I have a $XAUTHORITY environment variable. There is no way to configure SDL in virt-manager to use $XAUTHORITY instead of ~/.Xauthority. Neither does it work to copy the value of $XAUTHORITY into the file. I am ready to scream, because I've been five fscking days trying to make KVM work for Haiku development. There is a whole lot more that is broken than the slow video. All I really want to do for now is speed up my full builds of Haiku by using "jam -j2" to use both cores in my CPU. I may try Xen next, but the last time I monkeyed with Xen it was far, far more broken than I am finding KVM to be. Just for now, I would be satisfied if there were some way to use my USB stick as a drive in VirtualBox. VB does allow me to configure /dev/sdb as a drive, but it always causes a fatal error when I try to launch the guest. Thank You For Any Advice You Can Give Me. -

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