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  • How have you saved green by going green?

    - by Bob
    For the purpose of this question, I am interested in server/datacenter related hardware. Have you had any measureable amount of ROI by swapping existing hardware to more "green" or energy efficient hardware? For example, VMWare says you can reduce energy consumption by up to 80% by using virtualization. I have also heard of a cooling solution from HP which is suppose to reduce a small amount of engery usage (<25% I think). Google has also done something by integrating a UPS into their power supplies to reduce energy consumption. Any real world experiences would be great, but if you have any details on initial cost, savings and pay off time about what changes were make that would fantastic. I am not only interested in virtualization, I am interested in anything.

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  • About the External Graphics Card and CPU usage

    - by Balaji
    Hi, We are Rendering 16 live Streams at our client machine through one of our applications and the resolution of the video streams are as 4CIF/MPEG4/25FPS/4000Kbits. The configuration fo the client machine is below. HP Desktop Machine: Microsoft Windows XP Intel (R) Core2 Duo CPU E8400 @ 3.00 GHz 2.99 GHz, 1.94 GB of RAM Intel (R) Q45/Q43 Series Express Chipset (Inbuild) The CPU usage of the machine peaks 99% for 16 straems. After some discussion, we had decided to install external graphics card to reduce the CPU usage. So that, we have tried following graphics cards. NVIDIA Quadro NVS 440 - 128 MB Radeon HD 4350 - 512 MB GDDR2 Redeon HD 4350 - 1GB DDR2 ASUS EAH 4350 Silent 1GB DDR2 But the performance wise no difference, even worst. So, what is the pupuse of these external graphics cards? Really it will reduce the CPU usage? What parameters have to check, if we want to reduce the CPU usage? Please do the needful as soon as possible. Regards Balaji

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  • About the External Graphics Card and CPU usage

    - by Balaji
    We are Rendering 16 live Streams at our client machine through one of our applications and the resolution of the video streams are as 4CIF/MPEG4/25FPS/4000Kbits. The configuration of the client machine is below. HP Desktop Machine: Microsoft Windows XP Intel (R) Core2 Duo CPU E8400 @ 3.00 GHz 2.99 GHz, 1.94 GB of RAM Intel (R) Q45/Q43 Series Express Chipset (Inbuild) The CPU usage of the machine peaks 99% for 16 streams. After some discussion, we had decided to install external graphics card to reduce the CPU usage. So that, we have tried following graphics cards. NVIDIA Quadro NVS 440 - 128 MB Radeon HD 4350 - 512 MB GDDR2 Redeon HD 4350 - 1GB DDR2 ASUS EAH 4350 Silent 1GB DDR2 But the performance wise there has been no difference - even a drop in performance. So, what is the purpose of these external graphics cards? Really it will reduce the CPU usage? What parameters have to check, if we want to reduce the CPU usage?

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  • Why is HTML/Javascript minification beneficial

    - by Channel72
    Why is HTML/Javascript minification beneficial when the HTTP protocol already supports gzip data compression? I realize that Javascript/HTML minification has the potential to significantly reduce the size of Javascript/HTML files by removing unnecessary whitespace, and perhaps renaming variables to a few letters each, but doesn't the LZW algorithm do especially well when there are many repeated characters (e.g. lots of whitespace?) I realize that some Javascript minification tools do more than just reduce size. Google's closure compiler, for example, also tries to improve code performance by inlining functions and doing other analyses. But the primary purpose of Javascript minification is usually to reduce file size. I also realize there are other reasons you might want to minify aside from performace, such as code obfuscation. But again, that reason is not usually emphasized as much as performance gain and file size reduction. For example, Closure Compiler is not advertised as an obfuscation tool, but as a code size reducer and download-speed enhancer. So, how much performance do you really gain from Javascript/HTML minification when you're already significantly reducing file size with gzip compression?

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  • SAP Applications Certified for Oracle SPARC SuperCluster

    - by Javier Puerta
    SAP applications are now certified for use with the Oracle SPARC SuperCluster T4-4, a general-purpose engineered system designed for maximum simplicity, efficiency, reliability, and performance. "The Oracle SPARC SuperCluster is an ideal platform for consolidating SAP applications and infrastructure," says Ganesh Ramamurthy, vice president of engineering, Oracle. "Because the SPARC SuperCluster is a pre-integrated engineered system, it enables data center managers to dramatically reduce their time to production for SAP applications to a fraction of what a build-it-yourself approach requires and radically cuts operating and maintenance costs." SAP infrastructure and applications based on the SAP NetWeaver technology platform 6.4 and above and certified with Oracle Database 11g Release 2, such as the SAP ERP application and SAP NetWeaver Business Warehouse, can now be deployed using the SPARC SuperCluster T4 4. The SPARC SuperCluster T4-4 provides an optimized platform for SAP environments that can reduce configuration times by up to 75 percent, reduce operating costs up to 50 percent, can improve query performance by up to 10x, and can improve daily data loading up to 4x. The Oracle SPARC SuperCluster T4-4 is the world's fastest general purpose engineered system, delivering high performance, availability, scalability, and security to support and consolidate multi-tier enterprise applications with Web, database, and application components. The SPARC SuperCluster T4-4 combines Oracle's SPARC T4-4 servers running Oracle Solaris 11 with the database optimization of Oracle Exadata, the accelerated processing of Oracle Exalogic Elastic Cloud software, and the high throughput and availability of Oracle's Sun ZFS Storage Appliance all on a high-speed InfiniBand backplane. Part of Oracle's engineered systems family, the SPARC SuperCluster T4-4 demonstrates Oracle's unique ability to innovate and optimize at every layer of technology to simplify data center operations, drive down costs, and accelerate business innovation. For more details, refer to Our press release Datasheet: Oracle's SPARC SuperCluster T4-4 (PDF) Datasheet: Oracle's SPARC SuperCluster Now Supported by SAP (PDF) Video Podcast: Oracle's SPARC SuperCluster (MP4)

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  • MapReduce

    - by kaleidoscope
    MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a map function that processes a key/value pair to generate a set of  intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data,  scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter-machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Example: A process to count the appearances of each different word in a set of documents void map(String name, String document):   // name: document name   // document: document contents   for each word w in document:     EmitIntermediate(w, 1); void reduce(String word, Iterator partialCounts):   // word: a word   // partialCounts: a list of aggregated partial counts   int result = 0;   for each pc in partialCounts:     result += ParseInt(pc);   Emit(result); Here, each document is split in words, and each word is counted initially with a "1" value by the Map function, using the word as the result key. The framework puts together all the pairs with the same key and feeds them to the same call to Reduce, thus this function just needs to sum all of its input values to find the total appearances of that word.   Sarang, K

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  • Is there a low carbon future for the retail industry?

    - by user801960
    Recently Oracle published a report in conjunction with The Future Laboratory and a global panel of experts to highlight the issue of energy use in modern industry and the serious need to reduce carbon emissions radically by 2050.  Emissions must be cut by 80-95% below the levels in 1990 – but what can the retail industry do to keep up with this? There are three key aspects to the retail industry where carbon emissions can be cut:  manufacturing, transport and IT.  Manufacturing Naturally, manufacturing is going to be a big area where businesses across all industries will be forced to make considerable savings in carbon emissions as well as other forms of pollution.  Many retailers of all sizes will use third party factories and will have little control over specific environmental impacts from the factory, but retailers can reduce environmental impact at the factories by managing orders more efficiently – better planning for stock requirements means economies of scale both in terms of finance and the environment. The John Lewis Partnership has made detailed commitments to reducing manufacturing and packaging waste on both its own-brand products and products it sources from third party suppliers. It aims to divert 95 percent of its operational waste from landfill by 2013, which is a huge logistics challenge.  The John Lewis Partnership’s website provides a large amount of information on its responsibilities towards the environment. Transport Similarly to manufacturing, tightening up on logistical planning for stock distribution will make savings on carbon emissions from haulage.  More accurate supply and demand analysis will mean less stock re-allocation after initial distribution, and better warehouse management will mean more efficient stock distribution.  UK grocery retailer Morrisons has introduced double-decked trailers to its haulage fleet and adjusted distribution logistics accordingly to reduce the number of kilometers travelled by the fleet.  Morrisons measures route planning efficiency in terms of cases moved per kilometre and has, over the last two years, increased the number of cases per kilometre by 12.7%.  See Morrisons Corporate Responsibility report for more information. IT IT infrastructure is often initially overlooked by businesses when considering environmental efficiency.  Datacentres and web servers often need to run 24/7 to handle both consumer orders and internal logistics, and this both requires a lot of energy and puts out a lot of heat.  Many businesses are lowering environmental impact by reducing IT system fragmentation in their offices, while an increasing number of businesses are outsourcing their datacenters to cloud-based services.  Using centralised datacenters reduces the power usage at smaller offices, while using cloud based services means the datacenters can be based in a more environmentally friendly location.  For example, Facebook is opening a massive datacentre in Sweden – close to the Arctic Circle – to reduce the need for artificial cooling methods.  In addition, moving to a cloud-based solution makes IT services more easily scaleable, reducing redundant IT systems that would still use energy.  In store, the UK’s Carbon Trust reports that on average, lighting accounts for 25% of a retailer’s electricity costs, and for grocery retailers, up to 50% of their electricity bill comes from refrigeration units.  On a smaller scale, retailers can invest in greener technologies in store and in their offices.  The report concludes that widely shared objectives of energy security, reduced emissions and continued economic growth are dependent on the development of a smart grid capable of delivering energy efficiency and demand response, as well as integrating renewable and variable sources of energy. The report is available to download from http://emeapressoffice.oracle.com/imagelibrary/detail.aspx?MediaDetailsID=1766I’d be interested to hear your thoughts on the report.   

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  • Recent improvements in Console Performance

    - by loren.konkus
    Recently, the WebLogic Server development and support organizations have worked with a number of customers to quantify and improve the performance of the Administration Console in large, distributed configurations where there is significant latency in the communications between the administration server and managed servers. These improvements fall into two categories: Constraining the amount of time that the Console stalls waiting for communication Reducing and streamlining the amount of data required for an update A few releases ago, we added support for a configurable domain-wide mbean "Invocation Timeout" value on the Console's configuration: general, advanced section for a domain. The default value for this setting is 0, which means wait indefinitely and was chosen for compatibility with the behavior of previous releases. This configuration setting applies to all mbean communications between the admin server and managed servers, and is the first line of defense against being blocked by a stalled or completely overloaded managed server. Each site should choose an appropriate timeout value for their environment and network latency. In the next release of WebLogic Server, we've added an additional console preference, "Management Operation Timeout", to the Console's shared preference page. This setting further constrains how long certain console pages will wait for slowly responding servers before returning partial results. While not all Console pages support this yet, key pages such as the Servers Configuration and Control table pages and the Deployments Control pages have been updated to support this. For example, if a user requests a Servers Table page and a Management Operation Timeout occurs, the table is displayed with both local configuration and remote runtime information from the responding managed servers and only local configuration information for servers that did not yet respond. This means that a troublesome managed server does not impede your ability to manage your domain using the Console. To support these changes, these Console pages have been re-written to use the Work Management feature of WebLogic Server to interact with each server or deployment concurrently, which further improves the responsiveness of these pages. The basic algorithm for these pages is: For each configuration mbean (ie, Servers) populate rows with configuration attributes from the fast, local mbean server Find a WorkManager For each server, Create a Work instance to obtain runtime mbean attributes for the server Schedule Work instance in the WorkManager Call WorkManager.waitForAll to wait WorkItems to finish, constrained by Management Operation Timeout For each WorkItem, if the runtime information obtained was not complete, add a message indicating which server has incomplete data Display collected data in table In addition to these changes to constrain how long the console waits for communication, a number of other changes have been made to reduce the amount and scope of managed server interactions for key pages. For example, in previous releases the Deployments Control table looked at the status of a deployment on every managed server, even those servers that the deployment was not currently targeted on. (This was done to handle an edge case where a deployment's target configuration was changed while it remained running on previously targeted servers.) We decided supporting that edge case did not warrant the performance impact for all, and instead only look at the status of a deployment on the servers it is targeted to. Comprehensive status continues to be available if a user clicks on the 'status' field for a deployment. Finally, changes have been made to the System Status portlet to reduce its impact on Console page display times. Obtaining health information for this display requires several mbean interactions with managed servers. In previous releases, this mbean interaction occurred with every display, and any delay or impediment in these interactions was reflected in the display time for every page. To reduce this impact, we've made several changes in this portlet: Using Work Management to obtain health concurrently Applying the operation timeout configuration to constrain how long we will wait Caching health information to reduce the cost during rapid navigation from page to page and only obtaining new health information if the previous information is over 30 seconds old. Eliminating heath collection if this portlet is minimized. Together, these Console changes have resulted in significant performance improvements for the customers with large configurations and high latency that we have worked with during their development, and some lesser performance improvements for those with small configurations and very fast networks. These changes will be included in the 11g Rel 1 patch set 2 (10.3.3.0) release of WebLogic Server.

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  • Proving What You are Worth

    - by Ted Henson
    Here is a challenge for everyone. Just about everyone has been asked to provide or calculate the Return on Investment (ROI), so I will assume everyone has a method they use. The problem with stopping once you have an ROI is that those in the C-Suite probably do not care about the ROI as much as Return on Equity (ROE). Shareholders are mostly concerned with their return on the money the invested. Warren Buffett looks at ROE when deciding whether to make a deal or not. This article will outline how you can add more meaning to your ROI and show how you can potentially enhance the ROE of the company.   First I want to start with a base definition I am using for ROI and ROE. Return on investment (ROI) and return on equity (ROE) are ways to measure management effectiveness, parts of a system of measures that also includes profit margins for profitability, price-to-earnings ratio for valuation, and various debt-to-equity ratios for financial strength. Without a set of evaluation metrics, a company's financial performance cannot be fully examined by investors. ROI and ROE calculate the rate of return on a specific investment and the equity capital respectively, assessing how efficient financial resources have been used. Typically, the best way to improve financial efficiency is to reduce production cost, so that will be the focus. Now that the challenge has been made and items have been defined, let’s go deeper. Most research about implementation stops short at system start-up and seldom addresses post-implementation issues. However, we know implementation is a continuous improvement effort, and continued efforts after system start-up will influence the ultimate success of a system.   Most UPK ROI’s I have seen only include the cost savings in developing the training material. Some will also include savings based on reduced Help Desk calls. Using just those values you get a good ROI. To get an ROE you need to go a little deeper. Typically, the best way to improve financial efficiency is to reduce production cost, which is the purpose of implementing/upgrading an enterprise application. Let’s assume the new system is up and running and all users have been properly trained and are comfortable using the system. You provide senior management with your ROI that justifies the original cost. What you want to do now is develop a good base value to a measure the current efficiency. Using usage tracking you can look for various patterns. For example, you may find that users that are accessing UPK assistance are processing a procedure, such as entering an order, 5 minutes faster than those that don’t.  You do some research and discover each minute saved in processing a claim saves the company one dollar. That translates to the company saving five dollars on every transaction. Assuming 100,000 transactions are performed a year, and all users improve their performance, the company will be saving $500,000 a year. That $500,000 can be re-invested, used to reduce debt or paid to the shareholders.   With continued refinement during the life cycle, you should be able to find ways to reduce cost. These are the type of numbers and productivity gains that senior management and shareholders want to see. Being able to quantify savings and increase productivity may also help when seeking a raise or promotion.

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  • How to setup Hadoop cluster so that it accepts mapreduce jobs from remote computers?

    - by drasto
    There is a computer I use for Hadoop map/reduce testing. This computer runs 4 Linux virtual machines (using Oracle virtual box). Each of them has Cloudera with Hadoop (distribution c3u4) installed and serves as a node of Hadoop cluster. One of those 4 nodes is master node running namenode and jobtracker, others are slave nodes. Normally I use this cluster from local network for testing. However when I try to access it from another network I cannot send any jobs to it. The computer running Hadoop cluster has public IP and can be reached over internet for another services. For example I am able to get HDFS (namenode) administration site and map/reduce (jobtracker) administration site (on ports 50070 and 50030 respectively) from remote network. Also it is possible to use Hue. Ports 8020 and 8021 are both allowed. What is blocking my map/reduce job submits from reaching the cluster? Is there some setting that I must change first in order to be able to submit map/reduce jobs remotely? Here is my mapred-site.xml file: <configuration> <property> <name>mapred.job.tracker</name> <value>master:8021</value> </property> <!-- Enable Hue plugins --> <property> <name>mapred.jobtracker.plugins</name> <value>org.apache.hadoop.thriftfs.ThriftJobTrackerPlugin</value> <description>Comma-separated list of jobtracker plug-ins to be activated. </description> </property> <property> <name>jobtracker.thrift.address</name> <value>0.0.0.0:9290</value> </property> </configuration> And this is in /etc/hosts file: 192.168.1.15 master 192.168.1.14 slave1 192.168.1.13 slave2 192.168.1.9 slave3

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  • Best video codec for filmed powerpoint presentation

    - by rslite
    I have some presentations that are filmed. The audio is the presenter and the video is all the Powerpoint slides (size 1024x768, video codec H264, audio codec AAC). I would like to reduce their final file size since a 1 hour presentation is about 800 MB. Most of it is the video part which as I said is mostly powerpoint slides that don't change much over a matter of several seconds. Which codec would be better suited to encode this images and reduce the size of the end file?

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  • HTTP headers: Last-Modified - how can it mimimize server load?

    - by gotts
    Imagine the following use case: I use an AJAX request for getting some info about Item and use this URL: http://domain/items/show/1 In my database all items have a field called modified_at where we store the moment when this item was previously modified. How can Last-Modified server HTTP header in response can minimize load/reduce requests/increase responsiveness if we need to process this request every time on the server side? It looks like we don't reduce the number of HTTP requests with that response and we don't reduce the load on server. Who needs this anyway?

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  • Global variables in hadoop.

    - by Deepak Konidena
    Hi, My program follows a iterative map/reduce approach. And it needs to stop if certain conditions are met. Is there anyway i can set a global variable that can be distributed across all map/reduce tasks and check if the global variable reaches the condition for completion. Something like this. While(Condition != true){ Configuration conf = getConf(); Job job = new Job(conf, "Dijkstra Graph Search"); job.setJarByClass(GraphSearch.class); job.setMapperClass(DijkstraMap.class); job.setReducerClass(DijkstraReduce.class); job.setOutputKeyClass(IntWritable.class); job.setOutputValueClass(Text.class); } Where condition is a global variable that is modified during/after each map/reduce execution.

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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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  • Oracle Open World starts on Sunday, Sept 30

    - by Mike Dietrich
    Oracle Open World 2012 starts on Sunday this week - and we are really looking forward to see you in one of our presentations, especially theDatabase Upgrade on SteriodsReal Speed, Real Customers, Real Secretson Monday, Oct 1, 12:15pm in Moscone South 307(just skip the lunch - the boxed food is not healthy at all): Monday, Oct 1, 12:15 PM - 1:15 PM - Moscone South - 307 Database Upgrade on Steroids:Real Speed, Real Customers, Real Secrets Mike Dietrich - Consulting Member Technical Staff, Oracle Georg Winkens - Technical Manager, Amadeus Data Processing Carol Tagliaferri - Senior Development Manager, Oracle  Looking to improve the performance of your database upgrade and learn about other ways to reduce upgrade time? Isn’t everyone? In this session, you will learn directly from Oracle’s Upgrade Development team about what you can do to speed things up. Find out about ways to reduce upgrade downtime such as using a transient logical standby database and/or Oracle GoldenGate, and get other hints and tips. Learn about new features that improve upgrade performance and reduce downtime. Hear Georg Winkens, DB Services technical manager from Amadeus, speak about his upgrade experience, and get real-life performance measurements and advice for a successful upgrade. . And don't forget: we already start on Sunday so if you'd like to learn about the SAP database upgrades at Deutsche Messe: Sunday, Sep 30, 11:15 AM - 12:00 PM - Moscone West - 2001Oracle Database Upgrade to 11g Release 2 with SAP Applications Andreas Ellerhoff - DBA, Deutsche Messe AG Mike Dietrich - Consulting Member Technical Staff, Oracle Jan Klokkers - Sr.Director SAP Development, Oracle Deutsche Messe began to use Oracle6 Database at the end of the 1980s and has been using Oracle Database technology together with SAP applications successfully since 2002. At the end of 2010, it took the first steps of an upgrade to Oracle Database 11g Release 2 (11.2), and since mid-2011, all SAP production systems there run successfully with Oracle Database 11g. This presentation explains why Deutsche Messe uses Oracle Database together with SAP applications, discusses the many reasons for the upgrade to Release 11g, and focuses on the operational top aspects from a DBA perspective. . And unfortunately the Hands-On-Lab is sold out already ... We would like to apologize but we have absolutely ZERO influence on either the number of runs or the number of available seats.  Tuesday, Oct 2, 10:15 AM - 12:45 PM - Marriott Marquis - Salon 12/13 Hands On Lab:Upgrading an Oracle Database Instance, Using Best Practices Roy Swonger - Senior Director, Software Development, Oracle Carol Tagliaferri - Senior Development Manager, Oracle Mike Dietrich - Consulting Member Technical Staff, Oracle Cindy Lim - PMTS, Oracle Carol Palmer - Principal Product Manager, Oracle This hands-on lab gives participants the opportunity to work through a database upgrade from an older release of Oracle Database to the very latest Oracle Database release available. Participants will learn how the improved automation of the upgrade process and the generation of fix-up scripts can quickly help fix database issues prior to upgrading. The lab also uses the new parallel upgrade feature to improve performance of the upgrade, resulting in less downtime. Come get inside information about database upgrades from the Database Upgrade development team. . See you soon

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  • Friday Spotlight: The Value of Oracle Linux

    - by Chris Kawalek
    Happy Friday! Our spotlight this week is on a brand new white paper, chock full of fantastic information about Oracle Linux. From the intro to Oracle Linux - Maximize Value, Minimize Cost: "This paper describes the savings and efficiencies that an IT department can realize by choosing Oracle Linux as their enterprise standard. It highlights sample deployments and explains how deploying Oracle Linux can reduce operational costs and result in less downtime, improved productivity, and greater opportunities for revenue generation.?" The paper explains exactly how Oracle Linux can reduce costs, and goes into some of the features of Oracle Linux that can make it more valuable for your organization. Read the paper now. Have a great week! -Chris 

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  • How can I become more agile?

    - by dough
    The definition of an agile approach I've adopted is: working to reduce feedback loops, everywhere. I'd describe my Personal Development Process (PDP) as "not very agile" or "not agile enough"! I've adopted TDD, automated building, and time-boxing (using the Pomodoro Technique) as part of my PDP. I find these practices really help me get feedback, review my direction, and catch yak shaving earlier! However, what still escapes me is the ability to reduce feedback time in the ultimate feedback loop; regularly getting working software in front of the end user. Aside from team-oriented practices, what can I do to personally become more agile?

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  • Ideas for reducing storage needs and/or costs (lots of images)

    - by James P.
    Hi, I'm the webmaster for a small social network and have noticed that images uploaded by users are taking a big portion of the capacity available. These are mostly JPEGs. What solutions could I apply to reduce storage needs? Is there a way to reduce the size of images without affecting quality too much? Is there a service out there that could be used to store static files at a cheaper price (< 1GB/0.04 eurocents)? Edit: Updated the question.

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  • Bunny Inc. Season 2: Optimize Your Enterprise Content

    - by kellsey.ruppel
    In a business environment largely driven by informal exchanges, digital assets and peer-to-peer interactions, turning unstructured content into an enterprise-wide resource is the key to gain organizational agility and reduce IT costs. To get their work done, business users demand a unified, consolidated and secure repository to manage the entire life cycle of content and deliver it in the proper format.At Hare Inc., finding information turns to be a daunting and error-prone task. On the contrary, at Bunny Inc., Mr. CIO knows the secret to reach the right carrot! Have a look at the third episode of the Social Bunnies Season 2 to discover how to reduce resource bottlenecks, maximize content accessibility and mitigate risk.

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  • Webcast: Optimize Accounts Payable Through Automated Invoice Processing

    - by kellsey.ruppel(at)oracle.com
    Is your accounts payable process still very labor-intensive? Then discover how Oracle can help you eliminate paper, automate data entry and reduce costs by up to 90% - while saving valuable time through fewer errors and faster lookups. Join us on Tuesday, March 22 at 10 a.m. PT for this informative Webcast where Jamie Rancourt and Brian Dirking will show how you can easily integrate capture, forms recognition and content management into your PeopleSoft and Oracle E-Business Suite accounts payable systems. You will also see how The Home Depot, Costco and American Express have achieved tremendous savings and productivity gains by switching to automated solutions. Learn how you can automate invoice scanning, indexing and data extraction to:Improve speed and reduce errors Eliminate time-consuming searches Utilize vendor discounts through faster processing Improve visibility and ensure compliance Save costs in accounts payable and other business processesRegister today!

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  • Zoom out recursively for all the folders

    - by Chaitanya
    I want to reduce the icons size in all the folders. In ubuntu 11.10, version, there is an option to decrease icons size and I changed there, and all the icons size is reduced recursively. I am using 11.10 version now. Here, whichever folder I open every time I need to zoom out.(I want zoom out). If I have 20 recursive folders, I open each and every folder and right click there and zoom out. Do we have any global command or grapical tool to reduce the icons size in all the folders. I was told that unity will do this work but I am confused how to use it. If unity is the only solution, please guide me where to change the size. Thanks a ton, Thanks, Chaitanya.

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  • The Business Case for a Platform Approach

    - by Naresh Persaud
    Most customers have assembled a collection of Identity Management products over time, as they have reacted to industry regulations, compliance mandates and security threats, typically selecting best of breed products.  The resulting infrastructure is a patchwork of systems that has served the short term IDM goals, but is overly complex, hard to manage and cannot scale to meets the needs of the future social/mobile enterprise. The solution is to rethink Identity Management as a Platform, rather than individual products. Aberdeen Research has shown that taking a vendor integrated platform approach to Identity Management can reduce cost, make your IT organization more responsive to the needs of a changing business environment, and reduce audit deficiencies.  View the slide show below to see how companies like Agilent, Cisco, ING Bank and Toyota have all built the business case and embraced the Oracle Identity Management Platform approach. Biz case-keynote-final copy View more PowerPoint from OracleIDM

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  • Best practices of texture size

    - by psal
    I wanted to know how should I determine a good texture size ? Currently, I always create UV texture that are 1024x1024px but if I create for example, a big house with a 1024px texture size, it will looks pretty bad. So, should I create different texture size (512, 1024, ...) for different mesh size like this ? : or is it better to always do high-resolution texture and then reduce it in the software (ie : increase the LODBias settings in UDK reduce the size of the texture) ? Thanks for your answer. ps : sorry for my english !

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