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  • Using HTML 5 SessionState to save rendered Page Content

    - by Rick Strahl
    HTML 5 SessionState and LocalStorage are very useful and super easy to use to manage client side state. For building rich client side or SPA style applications it's a vital feature to be able to cache user data as well as HTML content in order to swap pages in and out of the browser's DOM. What might not be so obvious is that you can also use the sessionState and localStorage objects even in classic server rendered HTML applications to provide caching features between pages. These APIs have been around for a long time and are supported by most relatively modern browsers and even all the way back to IE8, so you can use them safely in your Web applications. SessionState and LocalStorage are easy The APIs that make up sessionState and localStorage are very simple. Both object feature the same API interface which  is a simple, string based key value store that has getItem, setItem, removeitem, clear and  key methods. The objects are also pseudo array objects and so can be iterated like an array with  a length property and you have array indexers to set and get values with. Basic usage  for storing and retrieval looks like this (using sessionStorage, but the syntax is the same for localStorage - just switch the objects):// set var lastAccess = new Date().getTime(); if (sessionStorage) sessionStorage.setItem("myapp_time", lastAccess.toString()); // retrieve in another page or on a refresh var time = null; if (sessionStorage) time = sessionStorage.getItem("myapp_time"); if (time) time = new Date(time * 1); else time = new Date(); sessionState stores data that is browser session specific and that has a liftetime of the active browser session or window. Shut down the browser or tab and the storage goes away. localStorage uses the same API interface, but the lifetime of the data is permanently stored in the browsers storage area until deleted via code or by clearing out browser cookies (not the cache). Both sessionStorage and localStorage space is limited. The spec is ambiguous about this - supposedly sessionStorage should allow for unlimited size, but it appears that most WebKit browsers support only 2.5mb for either object. This means you have to be careful what you store especially since other applications might be running on the same domain and also use the storage mechanisms. That said 2.5mb worth of character data is quite a bit and would go a long way. The easiest way to get a feel for how sessionState and localStorage work is to look at a simple example. You can go check out the following example online in Plunker: http://plnkr.co/edit/0ICotzkoPjHaWa70GlRZ?p=preview which looks like this: Plunker is an online HTML/JavaScript editor that lets you write and run Javascript code and similar to JsFiddle, but a bit cleaner to work in IMHO (thanks to John Papa for turning me on to it). The sample has two text boxes with counts that update session/local storage every time you click the related button. The counts are 'cached' in Session and Local storage. The point of these examples is that both counters survive full page reloads, and the LocalStorage counter survives a complete browser shutdown and restart. Go ahead and try it out by clicking the Reload button after updating both counters and then shutting down the browser completely and going back to the same URL (with the same browser). What you should see is that reloads leave both counters intact at the counted values, while a browser restart will leave only the local storage counter intact. The code to deal with the SessionStorage (and LocalStorage not shown here) in the example is isolated into a couple of wrapper methods to simplify the code: function getSessionCount() { var count = 0; if (sessionStorage) { var count = sessionStorage.getItem("ss_count"); count = !count ? 0 : count * 1; } $("#txtSession").val(count); return count; } function setSessionCount(count) { if (sessionStorage) sessionStorage.setItem("ss_count", count.toString()); } These two functions essentially load and store a session counter value. The two key methods used here are: sessionStorage.getItem(key); sessionStorage.setItem(key,stringVal); Note that the value given to setItem and return by getItem has to be a string. If you pass another type you get an error. Don't let that limit you though - you can easily enough store JSON data in a variable so it's quite possible to pass complex objects and store them into a single sessionStorage value:var user = { name: "Rick", id="ricks", level=8 } sessionStorage.setItem("app_user",JSON.stringify(user)); to retrieve it:var user = sessionStorage.getItem("app_user"); if (user) user = JSON.parse(user); Simple! If you're using the Chrome Developer Tools (F12) you can also check out the session and local storage state on the Resource tab:   You can also use this tool to refresh or remove entries from storage. What we just looked at is a purely client side implementation where a couple of counters are stored. For rich client centric AJAX applications sessionStorage and localStorage provide a very nice and simple API to store application state while the application is running. But you can also use these storage mechanisms to manage server centric HTML applications when you combine server rendering with some JavaScript to perform client side data caching. You can both store some state information and data on the client (ie. store a JSON object and carry it forth between server rendered HTML requests) or you can use it for good old HTTP based caching where some rendered HTML is saved and then restored later. Let's look at the latter with a real life example. Why do I need Client-side Page Caching for Server Rendered HTML? I don't know about you, but in a lot of my existing server driven applications I have lists that display a fair amount of data. Typically these lists contain links to then drill down into more specific data either for viewing or editing. You can then click on a link and go off to a detail page that provides more concise content. So far so good. But now you're done with the detail page and need to get back to the list, so you click on a 'bread crumbs trail' or an application level 'back to list' button and… …you end up back at the top of the list - the scroll position, the current selection in some cases even filters conditions - all gone with the wind. You've left behind the state of the list and are starting from scratch in your browsing of the list from the top. Not cool! Sound familiar? This a pretty common scenario with server rendered HTML content where it's so common to display lists to drill into, only to lose state in the process of returning back to the original list. Look at just about any traditional forums application, or even StackOverFlow to see what I mean here. Scroll down a bit to look at a post or entry, drill in then use the bread crumbs or tab to go back… In some cases returning to the top of a list is not a big deal. On StackOverFlow that sort of works because content is turning around so quickly you probably want to actually look at the top posts. Not always though - if you're browsing through a list of search topics you're interested in and drill in there's no way back to that position. Essentially anytime you're actively browsing the items in the list, that's when state becomes important and if it's not handled the user experience can be really disrupting. Content Caching If you're building client centric SPA style applications this is a fairly easy to solve problem - you tend to render the list once and then update the page content to overlay the detail content, only hiding the list temporarily until it's used again later. It's relatively easy to accomplish this simply by hiding content on the page and later making it visible again. But if you use server rendered content, hanging on to all the detail like filters, selections and scroll position is not quite as easy. Or is it??? This is where sessionStorage comes in handy. What if we just save the rendered content of a previous page, and then restore it when we return to this page based on a special flag that tells us to use the cached version? Let's see how we can do this. A real World Use Case Recently my local ISP asked me to help out with updating an ancient classifieds application. They had a very busy, local classifieds app that was originally an ASP classic application. The old app was - wait for it: frames based - and even though I lobbied against it, the decision was made to keep the frames based layout to allow rapid browsing of the hundreds of posts that are made on a daily basis. The primary reason they wanted this was precisely for the ability to quickly browse content item by item. While I personally hate working with Frames, I have to admit that the UI actually works well with the frames layout as long as you're running on a large desktop screen. You can check out the frames based desktop site here: http://classifieds.gorge.net/ However when I rebuilt the app I also added a secondary view that doesn't use frames. The main reason for this of course was for mobile displays which work horribly with frames. So there's a somewhat mobile friendly interface to the interface, which ditches the frames and uses some responsive design tweaking for mobile capable operation: http://classifeds.gorge.net/mobile  (or browse the base url with your browser width under 800px)   Here's what the mobile, non-frames view looks like:   As you can see this means that the list of classifieds posts now is a list and there's a separate page for drilling down into the item. And of course… originally we ran into that usability issue I mentioned earlier where the browse, view detail, go back to the list cycle resulted in lost list state. Originally in mobile mode you scrolled through the list, found an item to look at and drilled in to display the item detail. Then you clicked back to the list and BAM - you've lost your place. Because there are so many items added on a daily basis the full list is never fully loaded, but rather there's a "Load Additional Listings"  entry at the button. Not only did we originally lose our place when coming back to the list, but any 'additionally loaded' items are no longer there because the list was now rendering  as if it was the first page hit. The additional listings, and any filters, the selection of an item all were lost. Major Suckage! Using Client SessionStorage to cache Server Rendered Content To work around this problem I decided to cache the rendered page content from the list in SessionStorage. Anytime the list renders or is updated with Load Additional Listings, the page HTML is cached and stored in Session Storage. Any back links from the detail page or the login or write entry forms then point back to the list page with a back=true query string parameter. If the server side sees this parameter it doesn't render the part of the page that is cached. Instead the client side code retrieves the data from the sessionState cache and simply inserts it into the page. It sounds pretty simple, and the overall the process is really easy, but there are a few gotchas that I'll discuss in a minute. But first let's look at the implementation. Let's start with the server side here because that'll give a quick idea of the doc structure. As I mentioned the server renders data from an ASP.NET MVC view. On the list page when returning to the list page from the display page (or a host of other pages) looks like this: https://classifieds.gorge.net/list?back=True The query string value is a flag, that indicates whether the server should render the HTML. Here's what the top level MVC Razor view for the list page looks like:@model MessageListViewModel @{ ViewBag.Title = "Classified Listing"; bool isBack = !string.IsNullOrEmpty(Request.QueryString["back"]); } <form method="post" action="@Url.Action("list")"> <div id="SizingContainer"> @if (!isBack) { @Html.Partial("List_CommandBar_Partial", Model) <div id="PostItemContainer" class="scrollbox" xstyle="-webkit-overflow-scrolling: touch;"> @Html.Partial("List_Items_Partial", Model) @if (Model.RequireLoadEntry) { <div class="postitem loadpostitems" style="padding: 15px;"> <div id="LoadProgress" class="smallprogressright"></div> <div class="control-progress"> Load additional listings... </div> </div> } </div> } </div> </form> As you can see the query string triggers a conditional block that if set is simply not rendered. The content inside of #SizingContainer basically holds  the entire page's HTML sans the headers and scripts, but including the filter options and menu at the top. In this case this makes good sense - in other situations the fact that the menu or filter options might be dynamically updated might make you only cache the list rather than essentially the entire page. In this particular instance all of the content works and produces the proper result as both the list along with any filter conditions in the form inputs are restored. Ok, let's move on to the client. On the client there are two page level functions that deal with saving and restoring state. Like the counter example I showed earlier, I like to wrap the logic to save and restore values from sessionState into a separate function because they are almost always used in several places.page.saveData = function(id) { if (!sessionStorage) return; var data = { id: id, scroll: $("#PostItemContainer").scrollTop(), html: $("#SizingContainer").html() }; sessionStorage.setItem("list_html",JSON.stringify(data)); }; page.restoreData = function() { if (!sessionStorage) return; var data = sessionStorage.getItem("list_html"); if (!data) return null; return JSON.parse(data); }; The data that is saved is an object which contains an ID which is the selected element when the user clicks and a scroll position. These two values are used to reset the scroll position when the data is used from the cache. Finally the html from the #SizingContainer element is stored, which makes for the bulk of the document's HTML. In this application the HTML captured could be a substantial bit of data. If you recall, I mentioned that the server side code renders a small chunk of data initially and then gets more data if the user reads through the first 50 or so items. The rest of the items retrieved can be rather sizable. Other than the JSON deserialization that's Ok. Since I'm using SessionStorage the storage space has no immediate limits. Next is the core logic to handle saving and restoring the page state. At first though this would seem pretty simple, and in some cases it might be, but as the following code demonstrates there are a few gotchas to watch out for. Here's the relevant code I use to save and restore:$( function() { … var isBack = getUrlEncodedKey("back", location.href); if (isBack) { // remove the back key from URL setUrlEncodedKey("back", "", location.href); var data = page.restoreData(); // restore from sessionState if (!data) { // no data - force redisplay of the server side default list window.location = "list"; return; } $("#SizingContainer").html(data.html); var el = $(".postitem[data-id=" + data.id + "]"); $(".postitem").removeClass("highlight"); el.addClass("highlight"); $("#PostItemContainer").scrollTop(data.scroll); setTimeout(function() { el.removeClass("highlight"); }, 2500); } else if (window.noFrames) page.saveData(null); // save when page loads $("#SizingContainer").on("click", ".postitem", function() { var id = $(this).attr("data-id"); if (!id) return true; if (window.noFrames) page.saveData(id); var contentFrame = window.parent.frames["Content"]; if (contentFrame) contentFrame.location.href = "show/" + id; else window.location.href = "show/" + id; return false; }); … The code starts out by checking for the back query string flag which triggers restoring from the client cache. If cached the cached data structure is read from sessionStorage. It's important here to check if data was returned. If the user had back=true on the querystring but there is no cached data, he likely bookmarked this page or otherwise shut down the browser and came back to this URL. In that case the server didn't render any detail and we have no cached data, so all we can do is redirect to the original default list view using window.location. If we continued the page would render no data - so make sure to always check the cache retrieval result. Always! If there is data the it's loaded and the data.html data is restored back into the document by simply injecting the HTML back into the document's #SizingContainer element:$("#SizingContainer").html(data.html); It's that simple and it's quite quick even with a fully loaded list of additional items and on a phone. The actual HTML data is stored to the cache on every page load initially and then again when the user clicks on an element to navigate to a particular listing. The former ensures that the client cache always has something in it, and the latter updates with additional information for the selected element. For the click handling I use a data-id attribute on the list item (.postitem) in the list and retrieve the id from that. That id is then used to navigate to the actual entry as well as storing that Id value in the saved cached data. The id is used to reset the selection by searching for the data-id value in the restored elements. The overall process of this save/restore process is pretty straight forward and it doesn't require a bunch of code, yet it yields a huge improvement in the usability of the site on mobile devices (or anybody who uses the non-frames view). Some things to watch out for As easy as it conceptually seems to simply store and retrieve cached content, you have to be quite aware what type of content you are caching. The code above is all that's specific to cache/restore cycle and it works, but it took a few tweaks to the rest of the script code and server code to make it all work. There were a few gotchas that weren't immediately obvious. Here are a few things to pay attention to: Event Handling Logic Timing of manipulating DOM events Inline Script Code Bookmarking to the Cache Url when no cache exists Do you have inline script code in your HTML? That script code isn't going to run if you restore from cache and simply assign or it may not run at the time you think it would normally in the DOM rendering cycle. JavaScript Event Hookups The biggest issue I ran into with this approach almost immediately is that originally I had various static event handlers hooked up to various UI elements that are now cached. If you have an event handler like:$("#btnSearch").click( function() {…}); that works fine when the page loads with server rendered HTML, but that code breaks when you now load the HTML from cache. Why? Because the elements you're trying to hook those events to may not actually be there - yet. Luckily there's an easy workaround for this by using deferred events. With jQuery you can use the .on() event handler instead:$("#SelectionContainer").on("click","#btnSearch", function() {…}); which monitors a parent element for the events and checks for the inner selector elements to handle events on. This effectively defers to runtime event binding, so as more items are added to the document bindings still work. For any cached content use deferred events. Timing of manipulating DOM Elements Along the same lines make sure that your DOM manipulation code follows the code that loads the cached content into the page so that you don't manipulate DOM elements that don't exist just yet. Ideally you'll want to check for the condition to restore cached content towards the top of your script code, but that can be tricky if you have components or other logic that might not all run in a straight line. Inline Script Code Here's another small problem I ran into: I use a DateTime Picker widget I built a while back that relies on the jQuery date time picker. I also created a helper function that allows keyboard date navigation into it that uses JavaScript logic. Because MVC's limited 'object model' the only way to embed widget content into the page is through inline script. This code broken when I inserted the cached HTML into the page because the script code was not available when the component actually got injected into the page. As the last bullet - it's a matter of timing. There's no good work around for this - in my case I pulled out the jQuery date picker and relied on native <input type="date" /> logic instead - a better choice these days anyway, especially since this view is meant to be primarily to serve mobile devices which actually support date input through the browser (unlike desktop browsers of which only WebKit seems to support it). Bookmarking Cached Urls When you cache HTML content you have to make a decision whether you cache on the client and also not render that same content on the server. In the Classifieds app I didn't render server side content so if the user comes to the page with back=True and there is no cached content I have to a have a Plan B. Typically this happens when somebody ends up bookmarking the back URL. The easiest and safest solution for this scenario is to ALWAYS check the cache result to make sure it exists and if not have a safe URL to go back to - in this case to the plain uncached list URL which amounts to effectively redirecting. This seems really obvious in hindsight, but it's easy to overlook and not see a problem until much later, when it's not obvious at all why the page is not rendering anything. Don't use <body> to replace Content Since we're practically replacing all the HTML in the page it may seem tempting to simply replace the HTML content of the <body> tag. Don't. The body tag usually contains key things that should stay in the page and be there when it loads. Specifically script tags and elements and possibly other embedded content. It's best to create a top level DOM element specifically as a placeholder container for your cached content and wrap just around the actual content you want to replace. In the app above the #SizingContainer is that container. Other Approaches The approach I've used for this application is kind of specific to the existing server rendered application we're running and so it's just one approach you can take with caching. However for server rendered content caching this is a pattern I've used in a few apps to retrofit some client caching into list displays. In this application I took the path of least resistance to the existing server rendering logic. Here are a few other ways that come to mind: Using Partial HTML Rendering via AJAXInstead of rendering the page initially on the server, the page would load empty and the client would render the UI by retrieving the respective HTML and embedding it into the page from a Partial View. This effectively makes the initial rendering and the cached rendering logic identical and removes the server having to decide whether this request needs to be rendered or not (ie. not checking for a back=true switch). All the logic related to caching is made on the client in this case. Using JSON Data and Client RenderingThe hardcore client option is to do the whole UI SPA style and pull data from the server and then use client rendering or databinding to pull the data down and render using templates or client side databinding with knockout/angular et al. As with the Partial Rendering approach the advantage is that there's no difference in the logic between pulling the data from cache or rendering from scratch other than the initial check for the cache request. Of course if the app is a  full on SPA app, then caching may not be required even - the list could just stay in memory and be hidden and reactivated. I'm sure there are a number of other ways this can be handled as well especially using  AJAX. AJAX rendering might simplify the logic, but it also complicates search engine optimization since there's no content loaded initially. So there are always tradeoffs and it's important to look at all angles before deciding on any sort of caching solution in general. State of the Session SessionState and LocalStorage are easy to use in client code and can be integrated even with server centric applications to provide nice caching features of content and data. In this post I've shown a very specific scenario of storing HTML content for the purpose of remembering list view data and state and making the browsing experience for lists a bit more friendly, especially if there's dynamically loaded content involved. If you haven't played with sessionStorage or localStorage I encourage you to give it a try. There's a lot of cool stuff that you can do with this beyond the specific scenario I've covered here… Resources Overview of localStorage (also applies to sessionStorage) Web Storage Compatibility Modernizr Test Suite© Rick Strahl, West Wind Technologies, 2005-2013Posted in JavaScript  HTML5  ASP.NET  MVC   Tweet !function(d,s,id){var js,fjs=d.getElementsByTagName(s)[0];if(!d.getElementById(id)){js=d.createElement(s);js.id=id;js.src="//platform.twitter.com/widgets.js";fjs.parentNode.insertBefore(js,fjs);}}(document,"script","twitter-wjs"); (function() { var po = document.createElement('script'); po.type = 'text/javascript'; po.async = true; po.src = 'https://apis.google.com/js/plusone.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(po, s); })();

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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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  • Windows Azure Use Case: New Development

    - by BuckWoody
    This is one in a series of posts on when and where to use a distributed architecture design in your organization's computing needs. You can find the main post here: http://blogs.msdn.com/b/buckwoody/archive/2011/01/18/windows-azure-and-sql-azure-use-cases.aspx Description: Computing platforms evolve over time. Originally computers were directed by hardware wiring - that, the “code” was the path of the wiring that directed an electrical signal from one component to another, or in some cases a physical switch controlled the path. From there software was developed, first in a very low machine language, then when compilers were created, computer languages could more closely mimic written statements. These language statements can be compiled into the lower-level machine language still used by computers today. Microprocessors replaced logic circuits, sometimes with fewer instructions (Reduced Instruction Set Computing, RISC) and sometimes with more instructions (Complex Instruction Set Computing, CISC). The reason this history is important is that along each technology advancement, computer code has adapted. Writing software for a RISC architecture is significantly different than developing for a CISC architecture. And moving to a Distributed Architecture like Windows Azure also has specific implementation details that our code must follow. But why make a change? As I’ve described, we need to make the change to our code to follow advances in technology. There’s no point in change for its own sake, but as a new paradigm offers benefits to our users, it’s important for us to leverage those benefits where it makes sense. That’s most often done in new development projects. It’s a far simpler task to take a new project and adapt it to Windows Azure than to try and retrofit older code designed in a previous computing environment. We can still use the same coding languages (.NET, Java, C++) to write code for Windows Azure, but we need to think about the architecture of that code on a new project so that it runs in the most efficient, cost-effective way in a Distributed Architecture. As we receive new requests from the organization for new projects, a distributed architecture paradigm belongs in the decision matrix for the platform target. Implementation: When you are designing new applications for Windows Azure (or any distributed architecture) there are many important details to consider. But at the risk of over-simplification, there are three main concepts to learn and architect within the new code: Stateless Programming - Stateless program is a prime concept within distributed architectures. Rather than each server owning the complete processing cycle, the information from an operation that needs to be retained (the “state”) should be persisted to another location c(like storage) common to all machines involved in the process.  An interesting learning process for Stateless Programming (although not unique to this language type) is to learn Functional Programming. Server-Side Processing - Along with developing using a Stateless Design, the closer you can locate the code processing to the data, the less expensive and faster the code will run. When you control the network layer, this is less important, since you can send vast amounts of data between the server and client, allowing the client to perform processing. In a distributed architecture, you don’t always own the network, so it’s performance is unpredictable. Also, you may not be able to control the platform the user is on (such as a smartphone, PC or tablet), so it’s imperative to deliver only results and graphical elements where possible.  Token-Based Authentication - Also called “Claims-Based Authorization”, this code practice means instead of allowing a user to log on once and then running code in that context, a more granular level of security is used. A “token” or “claim”, often represented as a Certificate, is sent along for a series or even one request. In other words, every call to the code is authenticated against the token, rather than allowing a user free reign within the code call. While this is more work initially, it can bring a greater level of security, and it is far more resilient to disconnections. Resources: See the references of “Nondistributed Deployment” and “Distributed Deployment” at the top of this article for more information with graphics:  http://msdn.microsoft.com/en-us/library/ee658120.aspx  Stack Overflow has a good thread on functional programming: http://stackoverflow.com/questions/844536/advantages-of-stateless-programming  Another good discussion on Stack Overflow on server-side processing is here: http://stackoverflow.com/questions/3064018/client-side-or-server-side-processing Claims Based Authorization is described here: http://msdn.microsoft.com/en-us/magazine/ee335707.aspx

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  • Windows Azure Use Case: New Development

    - by BuckWoody
    This is one in a series of posts on when and where to use a distributed architecture design in your organization's computing needs. You can find the main post here: http://blogs.msdn.com/b/buckwoody/archive/2011/01/18/windows-azure-and-sql-azure-use-cases.aspx Description: Computing platforms evolve over time. Originally computers were directed by hardware wiring - that, the “code” was the path of the wiring that directed an electrical signal from one component to another, or in some cases a physical switch controlled the path. From there software was developed, first in a very low machine language, then when compilers were created, computer languages could more closely mimic written statements. These language statements can be compiled into the lower-level machine language still used by computers today. Microprocessors replaced logic circuits, sometimes with fewer instructions (Reduced Instruction Set Computing, RISC) and sometimes with more instructions (Complex Instruction Set Computing, CISC). The reason this history is important is that along each technology advancement, computer code has adapted. Writing software for a RISC architecture is significantly different than developing for a CISC architecture. And moving to a Distributed Architecture like Windows Azure also has specific implementation details that our code must follow. But why make a change? As I’ve described, we need to make the change to our code to follow advances in technology. There’s no point in change for its own sake, but as a new paradigm offers benefits to our users, it’s important for us to leverage those benefits where it makes sense. That’s most often done in new development projects. It’s a far simpler task to take a new project and adapt it to Windows Azure than to try and retrofit older code designed in a previous computing environment. We can still use the same coding languages (.NET, Java, C++) to write code for Windows Azure, but we need to think about the architecture of that code on a new project so that it runs in the most efficient, cost-effective way in a Distributed Architecture. As we receive new requests from the organization for new projects, a distributed architecture paradigm belongs in the decision matrix for the platform target. Implementation: When you are designing new applications for Windows Azure (or any distributed architecture) there are many important details to consider. But at the risk of over-simplification, there are three main concepts to learn and architect within the new code: Stateless Programming - Stateless program is a prime concept within distributed architectures. Rather than each server owning the complete processing cycle, the information from an operation that needs to be retained (the “state”) should be persisted to another location c(like storage) common to all machines involved in the process.  An interesting learning process for Stateless Programming (although not unique to this language type) is to learn Functional Programming. Server-Side Processing - Along with developing using a Stateless Design, the closer you can locate the code processing to the data, the less expensive and faster the code will run. When you control the network layer, this is less important, since you can send vast amounts of data between the server and client, allowing the client to perform processing. In a distributed architecture, you don’t always own the network, so it’s performance is unpredictable. Also, you may not be able to control the platform the user is on (such as a smartphone, PC or tablet), so it’s imperative to deliver only results and graphical elements where possible.  Token-Based Authentication - Also called “Claims-Based Authorization”, this code practice means instead of allowing a user to log on once and then running code in that context, a more granular level of security is used. A “token” or “claim”, often represented as a Certificate, is sent along for a series or even one request. In other words, every call to the code is authenticated against the token, rather than allowing a user free reign within the code call. While this is more work initially, it can bring a greater level of security, and it is far more resilient to disconnections. Resources: See the references of “Nondistributed Deployment” and “Distributed Deployment” at the top of this article for more information with graphics:  http://msdn.microsoft.com/en-us/library/ee658120.aspx  Stack Overflow has a good thread on functional programming: http://stackoverflow.com/questions/844536/advantages-of-stateless-programming  Another good discussion on Stack Overflow on server-side processing is here: http://stackoverflow.com/questions/3064018/client-side-or-server-side-processing Claims Based Authorization is described here: http://msdn.microsoft.com/en-us/magazine/ee335707.aspx

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  • Is a university education really worth it for a good programmer?

    - by Jon Purdy
    The title says it all, but here's the personal side of it: I've been doing design and programming for about as long as I can remember. If there's a programming problem, I can figure it out. (Though admittedly StackOverflow has allowed me to skip the figuring out and get straight to the doing in many instances.) I've made games, esoteric programming languages, and widgets and gizmos galore. I'm currently working on a general-purpose programming language. There's nothing I do better than programming. However, I'm just as passionate about design. Thus when I felt leaving high school that my design skills were lacking, I decided to attend university for New Media Design and Imaging, a digital design-related major. For a year, I diligently studied art and programmed in my free time. As the next year progressed, however, I was obligated to take fewer art and design classes and more technical classes. The trouble was of course that these classes were geared toward non-technical students, and were far beneath my skill level at the time. No amount of petitioning could overcome the institution's reluctance to allow me to test out of such classes, and the major offered no promise for any greater challenge in the future, so I took the extreme route: I switched into the technical equivalent of the major, New Media Interactive Development. A lot of my credits moved over into the new major, but many didn't. It would have been infeasible to switch to a more rigorous technical major such as Computer Science, and having tutored Computer Science students at every level here, I doubt I would be exposed to anything that I haven't already or won't eventually find out on my own, since I'm so involved in the field. I'm now on track to graduate perhaps a year later than I had planned, which puts a significant financial strain on my family and my future self. My schedule continues to be bogged down with classes that are wholly unnecessary for me to take. I'm being re-introduced to subjects that I've covered a thousand times over, simply because I've always been interested in it all. And though I succeed in avoiding the cynical and immature tactic of failing to complete work out of some undeserved sense of superiority, I'm becoming increasingly disillusioned by the lack of intellectual stimulation. Further, my school requires students to complete a number of quarters of co-op work experience proportional to their major. My original major required two quarters, but my current requires three, delaying my graduation even more. To top it all off, college is putting a severe strain on my relationship with my very close partner of a few years, so I've searched diligently for co-op jobs in my area, alas to no avail. I'm now in my third year, and approaching that point past which I can no longer handle this. Either I keep my head down, get a degree no matter what it takes, and try to get a job with a company that will pay me enough to do what I love that I can eventually pay off my loans; or I cut my losses now, move wherever there is work, and in six months start paying off what debt I've accumulated thus far. So the real question is: is a university education really more than just a formality? It's a big decision, and one I can't make lightly. I think this is the appropriate venue for this kind of question, and I hope it sticks around for the sake of others who might someday find themselves in similar situations. My heartfelt thanks for reading, and in advance for your help.

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  • Why is there no service-oriented language?

    - by Wolfgang
    Edit: To avoid further confusion: I am not talking about web services and such. I am talking about structuring applications internally, it's not about how computers communicate. It's about programming languages, compilers and how the imperative programming paradigm is extended. Original: In the imperative programming field, we saw two paradigms in the past 20 years (or more): object-oriented (OO), and service-oriented (SO) aka. component-based (CB). Both paradigms extend the imperative programming paradigm by introducing their own notion of modules. OO calls them objects (and classes) and lets them encapsulates both data (fields) and procedures (methods) together. SO, in contrast, separates data (records, beans, ...) from code (components, services). However, only OO has programming languages which natively support its paradigm: Smalltalk, C++, Java and all other JVM-compatibles, C# and all other .NET-compatibles, Python etc. SO has no such native language. It only comes into existence on top of procedural languages or OO languages: COM/DCOM (binary, C, C++), CORBA, EJB, Spring, Guice (all Java), ... These SO frameworks clearly suffer from the missing native language support of their concepts. They start using OO classes to represent services and records. This leads to designs where there is a clear distinction between classes that have methods only (services) and those that have fields only (records). Inheritance between services or records is then simulated by inheritance of classes. Technically, its not kept so strictly but in general programmers are adviced to make classes to play only one of the two roles. They use additional, external languages to represent the missing parts: IDL's, XML configurations, Annotations in Java code, or even embedded DSL like in Guice. This is especially needed, but not limited to, since the composition of services is not part of the service code itself. In OO, objects create other objects so there is no need for such facilities but for SO there is because services don't instantiate or configure other services. They establish an inner-platform effect on top of OO (early EJB, CORBA) where the programmer has to write all the code that is needed to "drive" SO. Classes represent only a part of the nature of a service and lots of classes have to be written to form a service together. All that boiler plate is necessary because there is no SO compiler which would do it for the programmer. This is just like some people did it in C for OO when there was no C++. You just pass the record which holds the data of the object as a first parameter to the procedure which is the method. In a OO language this parameter is implicit and the compiler produces all the code that we need for virtual functions etc. For SO, this is clearly missing. Especially the newer frameworks extensively use AOP or introspection to add the missing parts to a OO language. This doesn't bring the necessary language expressiveness but avoids the boiler platform code described in the previous point. Some frameworks use code generation to produce the boiler plate code. Configuration files in XML or annotations in OO code is the source of information for this. Not all of the phenomena that I mentioned above can be attributed to SO but I hope it clearly shows that there is a need for a SO language. Since this paradigm is so popular: why isn't there one? Or maybe there are some academic ones but at least the industry doesn't use one.

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  • C# Winforms vs WPF

    - by m0s
    Hi pros, I am a student and I do freelance here and there when I have opportunity. I believe my strongest language is C#. I don't really know what is going on in real programming world, so I was wondering if WPF did take over WinForms? I know the differences between two and how two can be used simultaneously but, I just don't want to invest my time in learning dying technologies, I hope you understand. So, for windows desktop programming what would you recommend to master WinForms, WPF or maybe both? I also get a lot that desktop programming is dead already and one should only care about learning web programming. Thanks for attention, any comments are greatly appreciated.

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  • Emacs X11 autocompletion (intellisense)

    - by JC
    Hi everyone, I use visual studio for day to day programming (read putting food in my mouth) but for personal programming (read c/c++ hacking) I use Emacs. Right now I am doing a programming exercise involving the X11 API. I am continually referring to the programming API manual to find the signature of function calls. What would be really nice would be if there was an emacs alternative to the visual studio intellisense. I know there is autocompletion for the language specifics. Is there such an extension available to Emacs? Or if not, is there way of creating one, maybe using the language specifics mechanism already used for auto completion?

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  • 3G/Edge/GPRS IP addresses and geocoding

    - by LookitsPuck
    Hey all! So, we're looking to develop a mobile website. On this mobile website, we'd like to automatically populate a user's location (with proper fallback) based on their IP address. I'm aware of geocoding a location based on IP address (mapping to latitude, longitude and then getting the location with that information). However, I'm curious how accurate this information is? Are mobile devices assigned IP's when they utilize 3G, EDGE, and GPRS connections? I think so. If that is so, does it map to a relatively accurate location? It doesn't have to be spot on, but relatively accurate would be nice. Thanks! -Steve

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  • Tricks to avoid losing motivation?

    - by AareP
    Motivation is a tricky thing to upkeep. Once I thought that ambitious projects will keep programmer motivated, and too simple tasks will hinder his motivation. Now I have plenty of experience with small and large projects, desktop/web/database programming, c++/c#/java/php languages, oop/non-oop paradigms, day-job/free-time programming.. but I still can't answer the question of motivation. Which programming tasks I like, and which don't? It seems to depend on too many variables. One thing remains constant though. It's that starting everything from scratch is always more motivating than extending some existing system. Unfortunately it's hard to use this trick in productive programming. :) So my question is, what tricks programmer can use to stay motivated? For example should we use pen and paper as much as possible, in order not to get fed up with monitor and keyboard?

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  • good books on numerical computation with C

    - by yCalleecharan
    Hi, I've read the post "What is the best book on numerical methods?" and I wish to ask more or less the same question but in relation to C programming. Most of the time, C programming books on numerical methods are just another version of the author's previous Fortran book on the same subject. I've seen Applied numerical methods in C by Nakamura, Shoichiro and the C codes are not good programming practice. I've heard bad comments about Numerical Recipes by Press. Do you know good books on C that discusses numerical methods. It's seem better for me to ask about good books on C discussing numerical methods than rather asking books on numerical methods that discusses C. I've heard about Numerical Algorithms with C by Giesela Engeln-Müllges and A Numerical Library in C for Scientists and Engineers bu Lau but haven't read them. Good books will always have algorithms implemented in the programming language in a smart way. Thanks a lot...

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  • What should I learn after HTML and CSS?

    - by Ryan B
    I am 5 days into learning how to make my website, flying through my HTML & CSS book and having fun. I’m starting to consider what to order next. I’m not sure what to study next, so please give me some advice if you can. My end goal is to create a site that has a lot of the functionality that www.edufire.com and similar sites have, just for example. I think I’m learning well with the Head First Series, and the style will probably serve me well as an intro to programming. However, I don't think the books dive too deeply into any 1 subject. I could order: A: Head First Programming: A Learner’s Guide to Programming Using the Python Language B: Head First Javascript C: Head First PHP & MySQL D: a different programming book or E: another CSS or design book to solidify my basic HTML & CSS skills Any guidance would be appreciated. Thanks!

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  • Master thesis in software engineering

    - by maya
    Hi everyone, I will be Master student and I look for a topic in software engineering for my thesis , I want a topic which is less programming and more analysis. I mean a topic without programming because I'm not professional in programming. I'm thinking in UML tools but I really don't have specific topic. any suggestion please any one help me thanks in advance

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  • How do I program an AVR Raven with Linux or a Mac?

    - by Andrew McGregor
    This tutorial for programming these starts with programming the Ravens and Jackdaw with a Windows box. Can I do those initial steps with avrdude on a Linux or OS X machine instead? If so, how? Is there any risk of bricking the hardware if I just try? I have a USB JTAG ICE MKii clone, which is supposed to work for this. I'm totally new to AVR, but very experienced with C/C++ programming on Linux or OS X, up to and including kernel programming... so any hint at all would be appreciated, I can read man pages, but only if I know what I'm looking for.

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  • Commercial version of Freenet6

    - by grnbeagle
    I've been using Freenet6 from gogoNET to make my mobile device publicly accessible via IPv6. It works quite well except that the service is not as stable because it's non-commercial usage only as their servers are hosted for free by different operators around the world. Apparently gogoNET sells hardware called gogoSERVER which allows one to build a service similar to Freenet6. I've inquired their sales team, but they were unable to tell me which companies have a commercial, production-quality implementation of Freenet6. Specifically I'm looking for the following features in IPv6 service provider: Client-based IPv6 connectivity for mobile devices: gogoCLIENT (gw6c) is ideal for mobile devices since it allows a device to go online regardless of the device's location. API for account maintenance: so that we can create device accounts from our software Static IPv6 address: (or maybe I mean IPv4 address) by this I mean, just like gogoNET6 service (username.broker.freenet6.net), we want to provide our users with a permanent URL for their device Any info on commercial IPv6 service provider utilizing gogoSERVER is appreciated. Thanks.

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  • SMS gateway with a "from" parameter

    - by user37220
    Hello, I am programming a mobile app which send sms. I need a gateway that let me put a "from" parameter. On many gateways, I can only subscribe, put my mobile phone number as the sender after a verification process. The main problem is that the phone number verification is only available for my and not for my users. My customers can send an sms but the receiver does not see their mobile phone number in the "from" field. Do you know a good gateway which support that (in an automated way) ? Thank you for your help.

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  • Always use one slow connection in preference of a "faster" one

    - by billc.cn
    In Windows, there's this automatic metric thing where the metric is selected according to the declared speed of the link. I now have a gigabit LAN routed to a 2MB DSL service and a HSDPA mobile broadband connection. The former is always chosen for Internet packets even though the latter is actually faster. I tried setting the mobile broadband's interface metric to 1 and raising its priority in the advanced settings of the adapter settings, but this does not seem to affect the metric of the default route. The default route to the Ethernet interface always have a lower metric than the mobile broadband interface. Am I missing something here?

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  • Need help finding a good curriculum/methodology for self-teaching to program from scratch

    - by BrotherGA2
    My friend and I have both dedicated ourselves to learning the essentials of programming by June of this year from nearly no programming experience. I have done some research and have come to the conclusion that using the Python language will be the best for us, but I am open to suggestions with good reasoning behind them. My motives for learning programming are: Potential Career Path to be able to create programs that can: solve problems; entertain, i.e. useful applications and games. Online college lectures + book (which I am willing to purchase) sounds like a good combination, but I do not know which would be most suitable for me. tl;dr: What I would like to find from the excellent people here is the following: a good, potentially best, programming course and/or book that is well structured and uses good pedagogy so that a person dedicated to learn programming may do so by following its curriculum (or use it to develop a curriculum) over the course of a few months. Thanks! (I apologize if this type of question is not considered proper etiquette, but I haven't found a consensus on this, and would like some guidance beyond the research I've already done)

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  • Error loading page

    - by blay
    i have this script on my jquery mobile page that calls an insert script(untitled.asp) for data insertion, but when i press the submit buttom it tells me error loading page and it doesn't work <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml"> <head> <link rel="stylesheet" href="http://code.jquery.com/mobile/1.3.0-beta.1/jquery.mobile-1.3.0-beta.1.min.css" /> <script src="http://code.jquery.com/jquery-1.8.3.min.js"></script> <script src="http://code.jquery.com/mobile/1.3.0-beta.1/jquery.mobile-1.3.0-beta.1.min.js"></script> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <title>Untitled Document</title> </head> <body> <form action="" method="POST" name="form1" id="form1"> <table width="327" border="0"> <tr> <td width="88">Item</td> <td width="185"><label for="item_name"></label> <input type="text" name="item_name" id="item_name" /></td> </tr> <tr> <td>Quantity</td> <td><label for="quantity"></label> <input type="text" name="quantity" id="quantity" /></td> </tr> <tr> <td>Price</td> <td><label for="price"></label> <input type="text" name="price" id="price" /></td> </tr> <tr> <td colspan="2">&nbsp; <input type="submit" value="Submit" /></td> </tr> </table> </form> <script> $(document).ready(function(){ $("form").on('submit',function(event){ event.preventDefault(); data = $(this).serialize(); $.ajax({ type: "POST", url: "untitled.asp", data: data }).done(function( msg ) { alert( "Data Saved: " + msg ); }); }); }); </script> </body> </html> it works fine on the website but when putting it into a mobile app it pops up this error

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  • Problem with intel chipset 4 serie and centos dealing with dual head

    - by Antoine
    I've a fujitsu lifebook S7220, it's been a while since i try to configure it to use a dual head with centos 5.4 x86_64. Everytime I try, the xserver crash... I've got an intel chipset mobile 4 serie (GMA 4500MHD, if I recall good!) When I do an lspci -v i've got these : 00:02.0 VGA compatible controller: Intel Corporation Mobile 4 Series Chipset Integrated Graphics Controller (rev 07) (prog-if 00 [VGA controller]) Subsystem: Fujitsu Limited. Unknown device 1451 Flags: bus master, fast devsel, latency 0, IRQ 177 Memory at f2000000 (64-bit, non-prefetchable) [size=4M] Memory at d0000000 (64-bit, prefetchable) [size=256M] I/O ports at 1800 [size=8] Capabilities: [90] Message Signalled Interrupts: 64bit- Queue=0/0 Enable- Capabilities: [d0] Power Management version 3 00:02.1 Display controller: Intel Corporation Mobile 4 Series Chipset Integrated Graphics Controller (rev 07) Subsystem: Fujitsu Limited. Unknown device 1451 Flags: bus master, fast devsel, latency 0 Memory at f2400000 (64-bit, non-prefetchable) [size=1M] Capabilities: [d0] Power Management version 3 My question is, anyone already got this problem and how did you fix it? Thank you for your answer!

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  • Winforms vs WPF

    - by m0s
    I am a student and I do freelance here and there when I have opportunity. I believe my strongest language is C#. I don't really know what is going on in real programming world, so I was wondering if WPF did take over WinForms? I know the differences between two and how two can be used simultaneously but, I just don't want to invest my time in learning dying technologies, I hope you understand. So, for windows desktop programming what would you recommend to master WinForms, WPF or maybe both? I also get a lot that desktop programming is dead already and one should only care about learning web programming.

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