Search Results

Search found 1774 results on 71 pages for 'parallel'.

Page 23/71 | < Previous Page | 19 20 21 22 23 24 25 26 27 28 29 30  | Next Page >

  • Much Ado About Nothing: Stub Objects

    - by user9154181
    The Solaris 11 link-editor (ld) contains support for a new type of object that we call a stub object. A stub object is a shared object, built entirely from mapfiles, that supplies the same linking interface as the real object, while containing no code or data. Stub objects cannot be executed — the runtime linker will kill any process that attempts to load one. However, you can link to a stub object as a dependency, allowing the stub to act as a proxy for the real version of the object. You may well wonder if there is a point to producing an object that contains nothing but linking interface. As it turns out, stub objects are very useful for building large bodies of code such as Solaris. In the last year, we've had considerable success in applying them to one of our oldest and thorniest build problems. In this discussion, I will describe how we came to invent these objects, and how we apply them to building Solaris. This posting explains where the idea for stub objects came from, and details our long and twisty journey from hallway idea to standard link-editor feature. I expect that these details are mainly of interest to those who work on Solaris and its makefiles, those who have done so in the past, and those who work with other similar bodies of code. A subsequent posting will omit the history and background details, and instead discuss how to build and use stub objects. If you are mainly interested in what stub objects are, and don't care about the underlying software war stories, I encourage you to skip ahead. The Long Road To Stubs This all started for me with an email discussion in May of 2008, regarding a change request that was filed in 2002, entitled: 4631488 lib/Makefile is too patient: .WAITs should be reduced This CR encapsulates a number of cronic issues with Solaris builds: We build Solaris with a parallel make (dmake) that tries to build as much of the code base in parallel as possible. There is a lot of code to build, and we've long made use of parallelized builds to get the job done quicker. This is even more important in today's world of massively multicore hardware. Solaris contains a large number of executables and shared objects. Executables depend on shared objects, and shared objects can depend on each other. Before you can build an object, you need to ensure that the objects it needs have been built. This implies a need for serialization, which is in direct opposition to the desire to build everying in parallel. To accurately build objects in the right order requires an accurate set of make rules defining the things that depend on each other. This sounds simple, but the reality is quite complex. In practice, having programmers explicitly specify these dependencies is a losing strategy: It's really hard to get right. It's really easy to get it wrong and never know it because things build anyway. Even if you get it right, it won't stay that way, because dependencies between objects can change over time, and make cannot help you detect such drifing. You won't know that you got it wrong until the builds break. That can be a long time after the change that triggered the breakage happened, making it hard to connect the cause and the effect. Usually this happens just before a release, when the pressure is on, its hard to think calmly, and there is no time for deep fixes. As a poor compromise, the libraries in core Solaris were built using a set of grossly incomplete hand written rules, supplemented with a number of dmake .WAIT directives used to group the libraries into sets of non-interacting groups that can be built in parallel because we think they don't depend on each other. From time to time, someone will suggest that we could analyze the built objects themselves to determine their dependencies and then generate make rules based on those relationships. This is possible, but but there are complications that limit the usefulness of that approach: To analyze an object, you have to build it first. This is a classic chicken and egg scenario. You could analyze the results of a previous build, but then you're not necessarily going to get accurate rules for the current code. It should be possible to build the code without having a built workspace available. The analysis will take time, and remember that we're constantly trying to make builds faster, not slower. By definition, such an approach will always be approximate, and therefore only incremantally more accurate than the hand written rules described above. The hand written rules are fast and cheap, while this idea is slow and complex, so we stayed with the hand written approach. Solaris was built that way, essentially forever, because these are genuinely difficult problems that had no easy answer. The makefiles were full of build races in which the right outcomes happened reliably for years until a new machine or a change in build server workload upset the accidental balance of things. After figuring out what had happened, you'd mutter "How did that ever work?", add another incomplete and soon to be inaccurate make dependency rule to the system, and move on. This was not a satisfying solution, as we tend to be perfectionists in the Solaris group, but we didn't have a better answer. It worked well enough, approximately. And so it went for years. We needed a different approach — a new idea to cut the Gordian Knot. In that discussion from May 2008, my fellow linker-alien Rod Evans had the initial spark that lead us to a game changing series of realizations: The link-editor is used to link objects together, but it only uses the ELF metadata in the object, consisting of symbol tables, ELF versioning sections, and similar data. Notably, it does not look at, or understand, the machine code that makes an object useful at runtime. If you had an object that only contained the ELF metadata for a dependency, but not the code or data, the link-editor would find it equally useful for linking, and would never know the difference. Call it a stub object. In the core Solaris OS, we require all objects to be built with a link-editor mapfile that describes all of its publically available functions and data. Could we build a stub object using the mapfile for the real object? It ought to be very fast to build stub objects, as there are no input objects to process. Unlike the real object, stub objects would not actually require any dependencies, and so, all of the stubs for the entire system could be built in parallel. When building the real objects, one could link against the stub objects instead of the real dependencies. This means that all the real objects can be built built in parallel too, without any serialization. We could replace a system that requires perfect makefile rules with a system that requires no ordering rules whatsoever. The results would be considerably more robust. We immediately realized that this idea had potential, but also that there were many details to sort out, lots of work to do, and that perhaps it wouldn't really pan out. As is often the case, it would be necessary to do the work and see how it turned out. Following that conversation, I set about trying to build a stub object. We determined that a faithful stub has to do the following: Present the same set of global symbols, with the same ELF versioning, as the real object. Functions are simple — it suffices to have a symbol of the right type, possibly, but not necessarily, referencing a null function in its text segment. Copy relocations make data more complicated to stub. The possibility of a copy relocation means that when you create a stub, the data symbols must have the actual size of the real data. Any error in this will go uncaught at link time, and will cause tragic failures at runtime that are very hard to diagnose. For reasons too obscure to go into here, involving tentative symbols, it is also important that the data reside in bss, or not, matching its placement in the real object. If the real object has more than one symbol pointing at the same data item, we call these aliased symbols. All data symbols in the stub object must exhibit the same aliasing as the real object. We imagined the stub library feature working as follows: A command line option to ld tells it to produce a stub rather than a real object. In this mode, only mapfiles are examined, and any object or shared libraries on the command line are are ignored. The extra information needed (function or data, size, and bss details) would be added to the mapfile. When building the real object instead of the stub, the extra information for building stubs would be validated against the resulting object to ensure that they match. In exploring these ideas, I immediately run headfirst into the reality of the original mapfile syntax, a subject that I would later write about as The Problem(s) With Solaris SVR4 Link-Editor Mapfiles. The idea of extending that poor language was a non-starter. Until a better mapfile syntax became available, which seemed unlikely in 2008, the solution could not involve extentions to the mapfile syntax. Instead, we cooked up the idea (hack) of augmenting mapfiles with stylized comments that would carry the necessary information. A typical definition might look like: # DATA(i386) __iob 0x3c0 # DATA(amd64,sparcv9) __iob 0xa00 # DATA(sparc) __iob 0x140 iob; A further problem then became clear: If we can't extend the mapfile syntax, then there's no good way to extend ld with an option to produce stub objects, and to validate them against the real objects. The idea of having ld read comments in a mapfile and parse them for content is an unacceptable hack. The entire point of comments is that they are strictly for the human reader, and explicitly ignored by the tool. Taking all of these speed bumps into account, I made a new plan: A perl script reads the mapfiles, generates some small C glue code to produce empty functions and data definitions, compiles and links the stub object from the generated glue code, and then deletes the generated glue code. Another perl script used after both objects have been built, to compare the real and stub objects, using data from elfdump, and validate that they present the same linking interface. By June 2008, I had written the above, and generated a stub object for libc. It was a useful prototype process to go through, and it allowed me to explore the ideas at a deep level. Ultimately though, the result was unsatisfactory as a basis for real product. There were so many issues: The use of stylized comments were fine for a prototype, but not close to professional enough for shipping product. The idea of having to document and support it was a large concern. The ideal solution for stub objects really does involve having the link-editor accept the same arguments used to build the real object, augmented with a single extra command line option. Any other solution, such as our prototype script, will require makefiles to be modified in deeper ways to support building stubs, and so, will raise barriers to converting existing code. A validation script that rederives what the linker knew when it built an object will always be at a disadvantage relative to the actual linker that did the work. A stub object should be identifyable as such. In the prototype, there was no tag or other metadata that would let you know that they weren't real objects. Being able to identify a stub object in this way means that the file command can tell you what it is, and that the runtime linker can refuse to try and run a program that loads one. At that point, we needed to apply this prototype to building Solaris. As you might imagine, the task of modifying all the makefiles in the core Solaris code base in order to do this is a massive task, and not something you'd enter into lightly. The quality of the prototype just wasn't good enough to justify that sort of time commitment, so I tabled the project, putting it on my list of long term things to think about, and moved on to other work. It would sit there for a couple of years. Semi-coincidentally, one of the projects I tacked after that was to create a new mapfile syntax for the Solaris link-editor. We had wanted to do something about the old mapfile syntax for many years. Others before me had done some paper designs, and a great deal of thought had already gone into the features it should, and should not have, but for various reasons things had never moved beyond the idea stage. When I joined Sun in late 2005, I got involved in reviewing those things and thinking about the problem. Now in 2008, fresh from relearning for the Nth time why the old mapfile syntax was a huge impediment to linker progress, it seemed like the right time to tackle the mapfile issue. Paving the way for proper stub object support was not the driving force behind that effort, but I certainly had them in mind as I moved forward. The new mapfile syntax, which we call version 2, integrated into Nevada build snv_135 in in February 2010: 6916788 ld version 2 mapfile syntax PSARC/2009/688 Human readable and extensible ld mapfile syntax In order to prove that the new mapfile syntax was adequate for general purpose use, I had also done an overhaul of the ON consolidation to convert all mapfiles to use the new syntax, and put checks in place that would ensure that no use of the old syntax would creep back in. That work went back into snv_144 in June 2010: 6916796 OSnet mapfiles should use version 2 link-editor syntax That was a big putback, modifying 517 files, adding 18 new files, and removing 110 old ones. I would have done this putback anyway, as the work was already done, and the benefits of human readable syntax are obvious. However, among the justifications listed in CR 6916796 was this We anticipate adding additional features to the new mapfile language that will be applicable to ON, and which will require all sharable object mapfiles to use the new syntax. I never explained what those additional features were, and no one asked. It was premature to say so, but this was a reference to stub objects. By that point, I had already put together a working prototype link-editor with the necessary support for stub objects. I was pleased to find that building stubs was indeed very fast. On my desktop system (Ultra 24), an amd64 stub for libc can can be built in a fraction of a second: % ptime ld -64 -z stub -o stubs/libc.so.1 -G -hlibc.so.1 \ -ztext -zdefs -Bdirect ... real 0.019708910 user 0.010101680 sys 0.008528431 In order to go from prototype to integrated link-editor feature, I knew that I would need to prove that stub objects were valuable. And to do that, I knew that I'd have to switch the Solaris ON consolidation to use stub objects and evaluate the outcome. And in order to do that experiment, ON would first need to be converted to version 2 mapfiles. Sub-mission accomplished. Normally when you design a new feature, you can devise reasonably small tests to show it works, and then deploy it incrementally, letting it prove its value as it goes. The entire point of stub objects however was to demonstrate that they could be successfully applied to an extremely large and complex code base, and specifically to solve the Solaris build issues detailed above. There was no way to finesse the matter — in order to move ahead, I would have to successfully use stub objects to build the entire ON consolidation and demonstrate their value. In software, the need to boil the ocean can often be a warning sign that things are trending in the wrong direction. Conversely, sometimes progress demands that you build something large and new all at once. A big win, or a big loss — sometimes all you can do is try it and see what happens. And so, I spent some time staring at ON makefiles trying to get a handle on how things work, and how they'd have to change. It's a big and messy world, full of complex interactions, unspecified dependencies, special cases, and knowledge of arcane makefile features... ...and so, I backed away, put it down for a few months and did other work... ...until the fall, when I felt like it was time to stop thinking and pondering (some would say stalling) and get on with it. Without stubs, the following gives a simplified high level view of how Solaris is built: An initially empty directory known as the proto, and referenced via the ROOT makefile macro is established to receive the files that make up the Solaris distribution. A top level setup rule creates the proto area, and performs operations needed to initialize the workspace so that the main build operations can be launched, such as copying needed header files into the proto area. Parallel builds are launched to build the kernel (usr/src/uts), libraries (usr/src/lib), and commands. The install makefile target builds each item and delivers a copy to the proto area. All libraries and executables link against the objects previously installed in the proto, implying the need to synchronize the order in which things are built. Subsequent passes run lint, and do packaging. Given this structure, the additions to use stub objects are: A new second proto area is established, known as the stub proto and referenced via the STUBROOT makefile macro. The stub proto has the same structure as the real proto, but is used to hold stub objects. All files in the real proto are delivered as part of the Solaris product. In contrast, the stub proto is used to build the product, and then thrown away. A new target is added to library Makefiles called stub. This rule builds the stub objects. The ld command is designed so that you can build a stub object using the same ld command line you'd use to build the real object, with the addition of a single -z stub option. This means that the makefile rules for building the stub objects are very similar to those used to build the real objects, and many existing makefile definitions can be shared between them. A new target is added to the Makefiles called stubinstall which delivers the stub objects built by the stub rule into the stub proto. These rules reuse much of existing plumbing used by the existing install rule. The setup rule runs stubinstall over the entire lib subtree as part of its initialization. All libraries and executables link against the objects in the stub proto rather than the main proto, and can therefore be built in parallel without any synchronization. There was no small way to try this that would yield meaningful results. I would have to take a leap of faith and edit approximately 1850 makefiles and 300 mapfiles first, trusting that it would all work out. Once the editing was done, I'd type make and see what happened. This took about 6 weeks to do, and there were many dark days when I'd question the entire project, or struggle to understand some of the many twisted and complex situations I'd uncover in the makefiles. I even found a couple of new issues that required changes to the new stub object related code I'd added to ld. With a substantial amount of encouragement and help from some key people in the Solaris group, I eventually got the editing done and stub objects for the entire workspace built. I found that my desktop system could build all the stub objects in the workspace in roughly a minute. This was great news, as it meant that use of the feature is effectively free — no one was likely to notice or care about the cost of building them. After another week of typing make, fixing whatever failed, and doing it again, I succeeded in getting a complete build! The next step was to remove all of the make rules and .WAIT statements dedicated to controlling the order in which libraries under usr/src/lib are built. This came together pretty quickly, and after a few more speed bumps, I had a workspace that built cleanly and looked like something you might actually be able to integrate someday. This was a significant milestone, but there was still much left to do. I turned to doing full nightly builds. Every type of build (open, closed, OpenSolaris, export, domestic) had to be tried. Each type failed in a new and unique way, requiring some thinking and rework. As things came together, I became aware of things that could have been done better, simpler, or cleaner, and those things also required some rethinking, the seeking of wisdom from others, and some rework. After another couple of weeks, it was in close to final form. My focus turned towards the end game and integration. This was a huge workspace, and needed to go back soon, before changes in the gate would made merging increasingly difficult. At this point, I knew that the stub objects had greatly simplified the makefile logic and uncovered a number of race conditions, some of which had been there for years. I assumed that the builds were faster too, so I did some builds intended to quantify the speedup in build time that resulted from this approach. It had never occurred to me that there might not be one. And so, I was very surprised to find that the wall clock build times for a stock ON workspace were essentially identical to the times for my stub library enabled version! This is why it is important to always measure, and not just to assume. One can tell from first principles, based on all those removed dependency rules in the library makefile, that the stub object version of ON gives dmake considerably more opportunities to overlap library construction. Some hypothesis were proposed, and shot down: Could we have disabled dmakes parallel feature? No, a quick check showed things being build in parallel. It was suggested that we might be I/O bound, and so, the threads would be mostly idle. That's a plausible explanation, but system stats didn't really support it. Plus, the timing between the stub and non-stub cases were just too suspiciously identical. Are our machines already handling as much parallelism as they are capable of, and unable to exploit these additional opportunities? Once again, we didn't see the evidence to back this up. Eventually, a more plausible and obvious reason emerged: We build the libraries and commands (usr/src/lib, usr/src/cmd) in parallel with the kernel (usr/src/uts). The kernel is the long leg in that race, and so, wall clock measurements of build time are essentially showing how long it takes to build uts. Although it would have been nice to post a huge speedup immediately, we can take solace in knowing that stub objects simplify the makefiles and reduce the possibility of race conditions. The next step in reducing build time should be to find ways to reduce or overlap the uts part of the builds. When that leg of the build becomes shorter, then the increased parallelism in the libs and commands will pay additional dividends. Until then, we'll just have to settle for simpler and more robust. And so, I integrated the link-editor support for creating stub objects into snv_153 (November 2010) with 6993877 ld should produce stub objects PSARC/2010/397 ELF Stub Objects followed by the work to convert the ON consolidation in snv_161 (February 2011) with 7009826 OSnet should use stub objects 4631488 lib/Makefile is too patient: .WAITs should be reduced This was a huge putback, with 2108 modified files, 8 new files, and 2 removed files. Due to the size, I was allowed a window after snv_160 closed in which to do the putback. It went pretty smoothly for something this big, a few more preexisting race conditions would be discovered and addressed over the next few weeks, and things have been quiet since then. Conclusions and Looking Forward Solaris has been built with stub objects since February. The fact that developers no longer specify the order in which libraries are built has been a big success, and we've eliminated an entire class of build error. That's not to say that there are no build races left in the ON makefiles, but we've taken a substantial bite out of the problem while generally simplifying and improving things. The introduction of a stub proto area has also opened some interesting new possibilities for other build improvements. As this article has become quite long, and as those uses do not involve stub objects, I will defer that discussion to a future article.

    Read the article

  • Tomcat 7 taking ages to start up after upgrade

    - by Lawrence
    I recently updated my server installation from Tomcat 6 to Tomcat 7, in order to take advantage of better connection pooling. My project uses Hibernate, for object persistance, a Mysql 5.5.20 database, and memcached for caching. When I was using Tomcat 6, Tomcat would start in about 8 seconds. After moving to Tomcat 7, it now takes between 75 - 80 seconds to start (this is on a Macbook pro 15", core i7 2Ghz, 8Gb of RAM). The only thing that has really changed between during the move from Tomcat 6 to 7 has been my context.xml file, which controls the connection pooling information: <Context antiJARLocking="true" reloadable="true" path=""> <Resource name="jdbc/test-db" auth="Container" type="javax.sql.DataSource" factory="org.apache.tomcat.jdbc.pool.DataSourceFactory" testOnBorrow="true" testOnReturn="false" testWhileIdle="true" validationQuery="SELECT 1" validationQueryTimeout="20000" validationInterval="30000" timeBetweenEvictionRunsMillis="60000" logValidationErrors="true" autoReconnect="true" username="webuser" password="xxxxxxx" driverClassName="com.mysql.jdbc.Driver" url="jdbc:mysql://databasename.us-east-1.rds.amazonaws.com:3306/test-db" maxActive="15" minIdle="2" maxIdle="10" maxWait="10000" maxAge="7200000"/> </Context> Now, as you can see, the database is running on Amazon RDS (where our live servers are), and thus is about 200ms round trip time away from my machine. I have already checked that I have security permissions to that database from my machine, (and anyway, it connects after 75 secs, so it cant be that). My initial thought was that Tomcat 7 and hibernate are doing something weird (like pre-instantiating a bunch of connections or something), and the latency to the database is amplifying the effects. While trying to diagnose the problem, I used jstack to get a stack trace of the Tomcat 7 server while its doing its startup thing. Here is the stack trace... Full thread dump Java HotSpot(TM) 64-Bit Server VM (20.12-b01-434 mixed mode): "Attach Listener" daemon prio=9 tid=7fa4c0038800 nid=0x10c39a000 waiting on condition [00000000] java.lang.Thread.State: RUNNABLE "Abandoned connection cleanup thread" daemon prio=5 tid=7fa4bb810000 nid=0x10f3ba000 in Object.wait() [10f3b9000] java.lang.Thread.State: WAITING (on object monitor) at java.lang.Object.wait(Native Method) - waiting on <7f40a0070> (a java.lang.ref.ReferenceQueue$Lock) at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:118) - locked <7f40a0070> (a java.lang.ref.ReferenceQueue$Lock) at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:134) at com.mysql.jdbc.NonRegisteringDriver$1.run(NonRegisteringDriver.java:93) "PoolCleaner[545768040:1352724902327]" daemon prio=5 tid=7fa4be852800 nid=0x10e772000 in Object.wait() [10e771000] java.lang.Thread.State: TIMED_WAITING (on object monitor) at java.lang.Object.wait(Native Method) - waiting on <7f40c7c90> (a java.util.TaskQueue) at java.util.TimerThread.mainLoop(Timer.java:509) - locked <7f40c7c90> (a java.util.TaskQueue) at java.util.TimerThread.run(Timer.java:462) "localhost-startStop-1" daemon prio=5 tid=7fa4bd034800 nid=0x10d66b000 runnable [10d668000] java.lang.Thread.State: RUNNABLE at java.net.SocketInputStream.socketRead0(Native Method) at java.net.SocketInputStream.read(SocketInputStream.java:129) at com.mysql.jdbc.util.ReadAheadInputStream.fill(ReadAheadInputStream.java:114) at com.mysql.jdbc.util.ReadAheadInputStream.readFromUnderlyingStreamIfNecessary(ReadAheadInputStream.java:161) at com.mysql.jdbc.util.ReadAheadInputStream.read(ReadAheadInputStream.java:189) - locked <7f3673be0> (a com.mysql.jdbc.util.ReadAheadInputStream) at com.mysql.jdbc.MysqlIO.readFully(MysqlIO.java:3014) at com.mysql.jdbc.MysqlIO.reuseAndReadPacket(MysqlIO.java:3467) at com.mysql.jdbc.MysqlIO.reuseAndReadPacket(MysqlIO.java:3456) at com.mysql.jdbc.MysqlIO.checkErrorPacket(MysqlIO.java:3997) at com.mysql.jdbc.MysqlIO.sendCommand(MysqlIO.java:2468) at com.mysql.jdbc.MysqlIO.sqlQueryDirect(MysqlIO.java:2629) at com.mysql.jdbc.ConnectionImpl.execSQL(ConnectionImpl.java:2713) - locked <7f366a1c0> (a com.mysql.jdbc.JDBC4Connection) at com.mysql.jdbc.ConnectionImpl.configureClientCharacterSet(ConnectionImpl.java:1930) at com.mysql.jdbc.ConnectionImpl.initializePropsFromServer(ConnectionImpl.java:3571) at com.mysql.jdbc.ConnectionImpl.connectOneTryOnly(ConnectionImpl.java:2445) at com.mysql.jdbc.ConnectionImpl.createNewIO(ConnectionImpl.java:2215) - locked <7f366a1c0> (a com.mysql.jdbc.JDBC4Connection) at com.mysql.jdbc.ConnectionImpl.<init>(ConnectionImpl.java:813) at com.mysql.jdbc.JDBC4Connection.<init>(JDBC4Connection.java:47) at sun.reflect.GeneratedConstructorAccessor10.newInstance(Unknown Source) at sun.reflect.DelegatingConstructorAccessorImpl.newInstance(DelegatingConstructorAccessorImpl.java:27) at java.lang.reflect.Constructor.newInstance(Constructor.java:513) at com.mysql.jdbc.Util.handleNewInstance(Util.java:411) at com.mysql.jdbc.ConnectionImpl.getInstance(ConnectionImpl.java:399) at com.mysql.jdbc.NonRegisteringDriver.connect(NonRegisteringDriver.java:334) at org.apache.tomcat.jdbc.pool.PooledConnection.connectUsingDriver(PooledConnection.java:278) at org.apache.tomcat.jdbc.pool.PooledConnection.connect(PooledConnection.java:182) at org.apache.tomcat.jdbc.pool.ConnectionPool.createConnection(ConnectionPool.java:699) at org.apache.tomcat.jdbc.pool.ConnectionPool.borrowConnection(ConnectionPool.java:631) at org.apache.tomcat.jdbc.pool.ConnectionPool.init(ConnectionPool.java:485) at org.apache.tomcat.jdbc.pool.ConnectionPool.<init>(ConnectionPool.java:143) at org.apache.tomcat.jdbc.pool.DataSourceProxy.pCreatePool(DataSourceProxy.java:116) - locked <7f34f0dc8> (a org.apache.tomcat.jdbc.pool.DataSource) at org.apache.tomcat.jdbc.pool.DataSourceProxy.createPool(DataSourceProxy.java:103) at org.apache.tomcat.jdbc.pool.DataSourceFactory.createDataSource(DataSourceFactory.java:539) at org.apache.tomcat.jdbc.pool.DataSourceFactory.getObjectInstance(DataSourceFactory.java:237) at org.apache.naming.factory.ResourceFactory.getObjectInstance(ResourceFactory.java:143) at javax.naming.spi.NamingManager.getObjectInstance(NamingManager.java:304) at org.apache.naming.NamingContext.lookup(NamingContext.java:843) at org.apache.naming.NamingContext.lookup(NamingContext.java:154) at org.apache.naming.NamingContext.lookup(NamingContext.java:831) at org.apache.naming.NamingContext.lookup(NamingContext.java:168) at org.apache.catalina.core.NamingContextListener.addResource(NamingContextListener.java:1061) at org.apache.catalina.core.NamingContextListener.createNamingContext(NamingContextListener.java:671) at org.apache.catalina.core.NamingContextListener.lifecycleEvent(NamingContextListener.java:270) at org.apache.catalina.util.LifecycleSupport.fireLifecycleEvent(LifecycleSupport.java:119) at org.apache.catalina.util.LifecycleBase.fireLifecycleEvent(LifecycleBase.java:90) at org.apache.catalina.core.StandardContext.startInternal(StandardContext.java:5173) - locked <7f46b07f0> (a org.apache.catalina.core.StandardContext) at org.apache.catalina.util.LifecycleBase.start(LifecycleBase.java:150) - locked <7f46b07f0> (a org.apache.catalina.core.StandardContext) at org.apache.catalina.core.ContainerBase$StartChild.call(ContainerBase.java:1559) at org.apache.catalina.core.ContainerBase$StartChild.call(ContainerBase.java:1549) at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:303) at java.util.concurrent.FutureTask.run(FutureTask.java:138) at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) at java.lang.Thread.run(Thread.java:680) "Catalina-startStop-1" daemon prio=5 tid=7fa4b7a5e800 nid=0x10d568000 waiting on condition [10d567000] java.lang.Thread.State: WAITING (parking) at sun.misc.Unsafe.park(Native Method) - parking to wait for <7f480e970> (a java.util.concurrent.FutureTask$Sync) at java.util.concurrent.locks.LockSupport.park(LockSupport.java:156) at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:811) at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedInterruptibly(AbstractQueuedSynchronizer.java:969) at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireSharedInterruptibly(AbstractQueuedSynchronizer.java:1281) at java.util.concurrent.FutureTask$Sync.innerGet(FutureTask.java:218) at java.util.concurrent.FutureTask.get(FutureTask.java:83) at org.apache.catalina.core.ContainerBase.startInternal(ContainerBase.java:1123) - locked <7f453c630> (a org.apache.catalina.core.StandardHost) at org.apache.catalina.core.StandardHost.startInternal(StandardHost.java:800) - locked <7f453c630> (a org.apache.catalina.core.StandardHost) at org.apache.catalina.util.LifecycleBase.start(LifecycleBase.java:150) - locked <7f453c630> (a org.apache.catalina.core.StandardHost) at org.apache.catalina.core.ContainerBase$StartChild.call(ContainerBase.java:1559) at org.apache.catalina.core.ContainerBase$StartChild.call(ContainerBase.java:1549) at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:303) at java.util.concurrent.FutureTask.run(FutureTask.java:138) at java.util.concurrent.ThreadPoolExecutor$Worker.runTask(ThreadPoolExecutor.java:886) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:908) at java.lang.Thread.run(Thread.java:680) "GC Daemon" daemon prio=2 tid=7fa4b9912800 nid=0x10d465000 in Object.wait() [10d464000] java.lang.Thread.State: TIMED_WAITING (on object monitor) at java.lang.Object.wait(Native Method) - waiting on <7f4506d28> (a sun.misc.GC$LatencyLock) at sun.misc.GC$Daemon.run(GC.java:100) - locked <7f4506d28> (a sun.misc.GC$LatencyLock) "Low Memory Detector" daemon prio=5 tid=7fa4b480b800 nid=0x10c8ae000 runnable [00000000] java.lang.Thread.State: RUNNABLE "C2 CompilerThread1" daemon prio=9 tid=7fa4b480b000 nid=0x10c7ab000 waiting on condition [00000000] java.lang.Thread.State: RUNNABLE "C2 CompilerThread0" daemon prio=9 tid=7fa4b480a000 nid=0x10c6a8000 waiting on condition [00000000] java.lang.Thread.State: RUNNABLE "Signal Dispatcher" daemon prio=9 tid=7fa4b4809800 nid=0x10c5a5000 runnable [00000000] java.lang.Thread.State: RUNNABLE "Surrogate Locker Thread (Concurrent GC)" daemon prio=5 tid=7fa4b4808800 nid=0x10c4a2000 waiting on condition [00000000] java.lang.Thread.State: RUNNABLE "Finalizer" daemon prio=8 tid=7fa4b793f000 nid=0x10c297000 in Object.wait() [10c296000] java.lang.Thread.State: WAITING (on object monitor) at java.lang.Object.wait(Native Method) - waiting on <7f451c8f0> (a java.lang.ref.ReferenceQueue$Lock) at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:118) - locked <7f451c8f0> (a java.lang.ref.ReferenceQueue$Lock) at java.lang.ref.ReferenceQueue.remove(ReferenceQueue.java:134) at java.lang.ref.Finalizer$FinalizerThread.run(Finalizer.java:159) "Reference Handler" daemon prio=10 tid=7fa4b793e000 nid=0x10c194000 in Object.wait() [10c193000] java.lang.Thread.State: WAITING (on object monitor) at java.lang.Object.wait(Native Method) - waiting on <7f452e168> (a java.lang.ref.Reference$Lock) at java.lang.Object.wait(Object.java:485) at java.lang.ref.Reference$ReferenceHandler.run(Reference.java:116) - locked <7f452e168> (a java.lang.ref.Reference$Lock) "main" prio=5 tid=7fa4b7800800 nid=0x104329000 waiting on condition [104327000] java.lang.Thread.State: WAITING (parking) at sun.misc.Unsafe.park(Native Method) - parking to wait for <7f480e9a0> (a java.util.concurrent.FutureTask$Sync) at java.util.concurrent.locks.LockSupport.park(LockSupport.java:156) at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(AbstractQueuedSynchronizer.java:811) at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedInterruptibly(AbstractQueuedSynchronizer.java:969) at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireSharedInterruptibly(AbstractQueuedSynchronizer.java:1281) at java.util.concurrent.FutureTask$Sync.innerGet(FutureTask.java:218) at java.util.concurrent.FutureTask.get(FutureTask.java:83) at org.apache.catalina.core.ContainerBase.startInternal(ContainerBase.java:1123) - locked <7f451fd90> (a org.apache.catalina.core.StandardEngine) at org.apache.catalina.core.StandardEngine.startInternal(StandardEngine.java:302) - locked <7f451fd90> (a org.apache.catalina.core.StandardEngine) at org.apache.catalina.util.LifecycleBase.start(LifecycleBase.java:150) - locked <7f451fd90> (a org.apache.catalina.core.StandardEngine) at org.apache.catalina.core.StandardService.startInternal(StandardService.java:443) - locked <7f451fd90> (a org.apache.catalina.core.StandardEngine) at org.apache.catalina.util.LifecycleBase.start(LifecycleBase.java:150) - locked <7f453e810> (a org.apache.catalina.core.StandardService) at org.apache.catalina.core.StandardServer.startInternal(StandardServer.java:732) - locked <7f4506d58> (a [Lorg.apache.catalina.Service;) at org.apache.catalina.util.LifecycleBase.start(LifecycleBase.java:150) - locked <7f44f7ba0> (a org.apache.catalina.core.StandardServer) at org.apache.catalina.startup.Catalina.start(Catalina.java:684) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25) at java.lang.reflect.Method.invoke(Method.java:597) at org.apache.catalina.startup.Bootstrap.start(Bootstrap.java:322) at org.apache.catalina.startup.Bootstrap.main(Bootstrap.java:451) "VM Thread" prio=9 tid=7fa4b7939800 nid=0x10c091000 runnable "Gang worker#0 (Parallel GC Threads)" prio=9 tid=7fa4b7802000 nid=0x10772b000 runnable "Gang worker#1 (Parallel GC Threads)" prio=9 tid=7fa4b7802800 nid=0x10782e000 runnable "Gang worker#2 (Parallel GC Threads)" prio=9 tid=7fa4b7803000 nid=0x107931000 runnable "Gang worker#3 (Parallel GC Threads)" prio=9 tid=7fa4b7804000 nid=0x107a34000 runnable "Gang worker#4 (Parallel GC Threads)" prio=9 tid=7fa4b7804800 nid=0x107b37000 runnable "Gang worker#5 (Parallel GC Threads)" prio=9 tid=7fa4b7805000 nid=0x107c3a000 runnable "Gang worker#6 (Parallel GC Threads)" prio=9 tid=7fa4b7805800 nid=0x107d3d000 runnable "Gang worker#7 (Parallel GC Threads)" prio=9 tid=7fa4b7806800 nid=0x107e40000 runnable "Concurrent Mark-Sweep GC Thread" prio=9 tid=7fa4b78e3800 nid=0x10bd0b000 runnable "Gang worker#0 (Parallel CMS Threads)" prio=9 tid=7fa4b78e2800 nid=0x10b305000 runnable "Gang worker#1 (Parallel CMS Threads)" prio=9 tid=7fa4b78e3000 nid=0x10b408000 runnable "VM Periodic Task Thread" prio=10 tid=7fa4b4815800 nid=0x10c9b1000 waiting on condition "Exception Catcher Thread" prio=10 tid=7fa4b7801800 nid=0x104554000 runnable JNI global references: 919 The only thing I can figure out from this is that it looks like the mysql jdbc drivers might have something to do with the long start up (the various stack traces I took during the start up process all pretty much look the same as this). Could anyone shed some light on what might be causing this? Have I done something dense in my context.xml? Is hibernate perhaps to blame?

    Read the article

  • Convert ddply {plyr} to Oracle R Enterprise, or use with Embedded R Execution

    - by Mark Hornick
    The plyr package contains a set of tools for partitioning a problem into smaller sub-problems that can be more easily processed. One function within {plyr} is ddply, which allows you to specify subsets of a data.frame and then apply a function to each subset. The result is gathered into a single data.frame. Such a capability is very convenient. The function ddply also has a parallel option that if TRUE, will apply the function in parallel, using the backend provided by foreach. This type of functionality is available through Oracle R Enterprise using the ore.groupApply function. In this blog post, we show a few examples from Sean Anderson's "A quick introduction to plyr" to illustrate the correpsonding functionality using ore.groupApply. To get started, we'll create a demo data set and load the plyr package. set.seed(1) d <- data.frame(year = rep(2000:2014, each = 3),         count = round(runif(45, 0, 20))) dim(d) library(plyr) This first example takes the data frame, partitions it by year, and calculates the coefficient of variation of the count, returning a data frame. # Example 1 res <- ddply(d, "year", function(x) {   mean.count <- mean(x$count)   sd.count <- sd(x$count)   cv <- sd.count/mean.count   data.frame(cv.count = cv)   }) To illustrate the equivalent functionality in Oracle R Enterprise, using embedded R execution, we use the ore.groupApply function on the same data, but pushed to the database, creating an ore.frame. The function ore.push creates a temporary table in the database, returning a proxy object, the ore.frame. D <- ore.push(d) res <- ore.groupApply (D, D$year, function(x) {   mean.count <- mean(x$count)   sd.count <- sd(x$count)   cv <- sd.count/mean.count   data.frame(year=x$year[1], cv.count = cv)   }, FUN.VALUE=data.frame(year=1, cv.count=1)) You'll notice the similarities in the first three arguments. With ore.groupApply, we augment the function to return the specific data.frame we want. We also specify the argument FUN.VALUE, which describes the resulting data.frame. From our previous blog posts, you may recall that by default, ore.groupApply returns an ore.list containing the results of each function invocation. To get a data.frame, we specify the structure of the result. The results in both cases are the same, however the ore.groupApply result is an ore.frame. In this case the data stays in the database until it's actually required. This can result in significant memory and time savings whe data is large. R> class(res) [1] "ore.frame" attr(,"package") [1] "OREbase" R> head(res)    year cv.count 1 2000 0.3984848 2 2001 0.6062178 3 2002 0.2309401 4 2003 0.5773503 5 2004 0.3069680 6 2005 0.3431743 To make the ore.groupApply execute in parallel, you can specify the argument parallel with either TRUE, to use default database parallelism, or to a specific number, which serves as a hint to the database as to how many parallel R engines should be used. The next ddply example uses the summarise function, which creates a new data.frame. In ore.groupApply, the year column is passed in with the data. Since no automatic creation of columns takes place, we explicitly set the year column in the data.frame result to the value of the first row, since all rows received by the function have the same year. # Example 2 ddply(d, "year", summarise, mean.count = mean(count)) res <- ore.groupApply (D, D$year, function(x) {   mean.count <- mean(x$count)   data.frame(year=x$year[1], mean.count = mean.count)   }, FUN.VALUE=data.frame(year=1, mean.count=1)) R> head(res)    year mean.count 1 2000 7.666667 2 2001 13.333333 3 2002 15.000000 4 2003 3.000000 5 2004 12.333333 6 2005 14.666667 Example 3 uses the transform function with ddply, which modifies the existing data.frame. With ore.groupApply, we again construct the data.frame explicilty, which is returned as an ore.frame. # Example 3 ddply(d, "year", transform, total.count = sum(count)) res <- ore.groupApply (D, D$year, function(x) {   total.count <- sum(x$count)   data.frame(year=x$year[1], count=x$count, total.count = total.count)   }, FUN.VALUE=data.frame(year=1, count=1, total.count=1)) > head(res)    year count total.count 1 2000 5 23 2 2000 7 23 3 2000 11 23 4 2001 18 40 5 2001 4 40 6 2001 18 40 In Example 4, the mutate function with ddply enables you to define new columns that build on columns just defined. Since the construction of the data.frame using ore.groupApply is explicit, you always have complete control over when and how to use columns. # Example 4 ddply(d, "year", mutate, mu = mean(count), sigma = sd(count),       cv = sigma/mu) res <- ore.groupApply (D, D$year, function(x) {   mu <- mean(x$count)   sigma <- sd(x$count)   cv <- sigma/mu   data.frame(year=x$year[1], count=x$count, mu=mu, sigma=sigma, cv=cv)   }, FUN.VALUE=data.frame(year=1, count=1, mu=1,sigma=1,cv=1)) R> head(res)    year count mu sigma cv 1 2000 5 7.666667 3.055050 0.3984848 2 2000 7 7.666667 3.055050 0.3984848 3 2000 11 7.666667 3.055050 0.3984848 4 2001 18 13.333333 8.082904 0.6062178 5 2001 4 13.333333 8.082904 0.6062178 6 2001 18 13.333333 8.082904 0.6062178 In Example 5, ddply is used to partition data on multiple columns before constructing the result. Realizing this with ore.groupApply involves creating an index column out of the concatenation of the columns used for partitioning. This example also allows us to illustrate using the ORE transparency layer to subset the data. # Example 5 baseball.dat <- subset(baseball, year > 2000) # data from the plyr package x <- ddply(baseball.dat, c("year", "team"), summarize,            homeruns = sum(hr)) We first push the data set to the database to get an ore.frame. We then add the composite column and perform the subset, using the transparency layer. Since the results from database execution are unordered, we will explicitly sort these results and view the first 6 rows. BB.DAT <- ore.push(baseball) BB.DAT$index <- with(BB.DAT, paste(year, team, sep="+")) BB.DAT2 <- subset(BB.DAT, year > 2000) X <- ore.groupApply (BB.DAT2, BB.DAT2$index, function(x) {   data.frame(year=x$year[1], team=x$team[1], homeruns=sum(x$hr))   }, FUN.VALUE=data.frame(year=1, team="A", homeruns=1), parallel=FALSE) res <- ore.sort(X, by=c("year","team")) R> head(res)    year team homeruns 1 2001 ANA 4 2 2001 ARI 155 3 2001 ATL 63 4 2001 BAL 58 5 2001 BOS 77 6 2001 CHA 63 Our next example is derived from the ggplot function documentation. This illustrates the use of ddply within using the ggplot2 package. We first create a data.frame with demo data and use ddply to create some statistics for each group (gp). We then use ggplot to produce the graph. We can take this same code, push the data.frame df to the database and invoke this on the database server. The graph will be returned to the client window, as depicted below. # Example 6 with ggplot2 library(ggplot2) df <- data.frame(gp = factor(rep(letters[1:3], each = 10)),                  y = rnorm(30)) # Compute sample mean and standard deviation in each group library(plyr) ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y)) # Set up a skeleton ggplot object and add layers: ggplot() +   geom_point(data = df, aes(x = gp, y = y)) +   geom_point(data = ds, aes(x = gp, y = mean),              colour = 'red', size = 3) +   geom_errorbar(data = ds, aes(x = gp, y = mean,                                ymin = mean - sd, ymax = mean + sd),              colour = 'red', width = 0.4) DF <- ore.push(df) ore.tableApply(DF, function(df) {   library(ggplot2)   library(plyr)   ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))   ggplot() +     geom_point(data = df, aes(x = gp, y = y)) +     geom_point(data = ds, aes(x = gp, y = mean),                colour = 'red', size = 3) +     geom_errorbar(data = ds, aes(x = gp, y = mean,                                  ymin = mean - sd, ymax = mean + sd),                   colour = 'red', width = 0.4) }) But let's take this one step further. Suppose we wanted to produce multiple graphs, partitioned on some index column. We replicate the data three times and add some noise to the y values, just to make the graphs a little different. We also create an index column to form our three partitions. Note that we've also specified that this should be executed in parallel, allowing Oracle Database to control and manage the server-side R engines. The result of ore.groupApply is an ore.list that contains the three graphs. Each graph can be viewed by printing the list element. df2 <- rbind(df,df,df) df2$y <- df2$y + rnorm(nrow(df2)) df2$index <- c(rep(1,300), rep(2,300), rep(3,300)) DF2 <- ore.push(df2) res <- ore.groupApply(DF2, DF2$index, function(df) {   df <- df[,1:2]   library(ggplot2)   library(plyr)   ds <- ddply(df, .(gp), summarise, mean = mean(y), sd = sd(y))   ggplot() +     geom_point(data = df, aes(x = gp, y = y)) +     geom_point(data = ds, aes(x = gp, y = mean),                colour = 'red', size = 3) +     geom_errorbar(data = ds, aes(x = gp, y = mean,                                  ymin = mean - sd, ymax = mean + sd),                   colour = 'red', width = 0.4)   }, parallel=TRUE) res[[1]] res[[2]] res[[3]] To recap, we've illustrated how various uses of ddply from the plyr package can be realized in ore.groupApply, which affords the user explicit control over the contents of the data.frame result in a straightforward manner. We've also highlighted how ddply can be used within an ore.groupApply call.

    Read the article

  • 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.

    Read the article

  • External File Upload Optimizations for Windows Azure

    - by rgillen
    [Cross posted from here: http://rob.gillenfamily.net/post/External-File-Upload-Optimizations-for-Windows-Azure.aspx] I’m wrapping up a bit of the work we’ve been doing on data movement optimizations for cloud computing and the latest set of data yielded some interesting points I thought I’d share. The work done here is not really rocket science but may, in some ways, be slightly counter-intuitive and therefore seemed worthy of posting. Summary: for those who don’t like to read detailed posts or don’t have time, the synopsis is that if you are uploading data to Azure, block your data (even down to 1MB) and upload in parallel. Set your block size based on your source file size, but if you must choose a fixed value, use 1MB. Following the above will result in significant performance gains… upwards of 10x-24x and a reduction in overall file transfer time of upwards of 90% (eg, uploading a 1GB file averaged 46.37 minutes prior to optimizations and averaged 1.86 minutes afterwards). Detail: For those of you who want more detail, or think that the claims at the end of the preceding paragraph are over-reaching, what follows is information and code supporting these claims. As the title would indicate, these tests were run from our research facility pointing to the Azure cloud (specifically US North Central as it is physically closest to us) and do not represent intra-cloud results… we have performed intra-cloud tests and the overall results are similar in notion but the data rates are significantly different as well as the tipping points for the various block sizes… this will be detailed separately). We started by building a very simple console application that would loop through a directory and upload each file to Azure storage. This application used the shipping storage client library from the 1.1 version of the azure tools. The only real variation from the client library is that we added code to collect and record the duration (in ms) and size (in bytes) for each file transferred. The code is available here. We then created a directory that had a collection of files for the following sizes: 2KB, 32KB, 64KB, 128KB, 512KB, 1MB, 5MB, 10MB, 25MB, 50MB, 100MB, 250MB, 500MB, 750MB, and 1GB (50 files for each size listed). These files contained randomly-generated binary data and do not benefit from compression (a separate discussion topic). Our file generation tool is available here. The baseline was established by running the application described above against the directory containing all of the data files. This application uploads the files in a random order so as to avoid transferring all of the files of a given size sequentially and thereby spreading the affects of periodic Internet delays across the collection of results.  We then ran some scripts to split the resulting data and generate some reports. The raw data collected for our non-optimized tests is available via the links in the Related Resources section at the bottom of this post. For each file size, we calculated the average upload time (and standard deviation) and the average transfer rate (and standard deviation). As you likely are aware, transferring data across the Internet is susceptible to many transient delays which can cause anomalies in the resulting data. It is for this reason that we randomized the order of source file processing as well as executed the tests 50x for each file size. We expect that these steps will yield a sufficiently balanced set of results. Once the baseline was collected and analyzed, we updated the test harness application with some methods to split the source file into user-defined block sizes and then to upload those blocks in parallel (using the PutBlock() method of Azure storage). The parallelization was handled by simply relying on the Parallel Extensions to .NET to provide a Parallel.For loop (see linked source for specific implementation details in Program.cs, line 173 and following… less than 100 lines total). Once all of the blocks were uploaded, we called PutBlockList() to assemble/commit the file in Azure storage. For each block transferred, the MD5 was calculated and sent ensuring that the bits that arrived matched was was intended. The timer for the blocked/parallelized transfer method wraps the entire process (source file splitting, block transfer, MD5 validation, file committal). A diagram of the process is as follows: We then tested the affects of blocking & parallelizing the transfers by running the updated application against the same source set and did a parameter sweep on the block size including 256KB, 512KB, 1MB, 2MB, and 4MB (our assumption was that anything lower than 256KB wasn’t worth the trouble and 4MB is the maximum size of a block supported by Azure). The raw data for the parallel tests is available via the links in the Related Resources section at the bottom of this post. This data was processed and then compared against the single-threaded / non-optimized transfer numbers and the results were encouraging. The Excel version of the results is available here. Two semi-obvious points need to be made prior to reviewing the data. The first is that if the block size is larger than the source file size you will end up with a “negative optimization” due to the overhead of attempting to block and parallelize. The second is that as the files get smaller, the clock-time cost of blocking and parallelizing (overhead) is more apparent and can tend towards negative optimizations. For this reason (and is supported in the raw data provided in the linked worksheet) the charts and dialog below ignore source file sizes less than 1MB. (click chart for full size image) The chart above illustrates some interesting points about the results: When the block size is smaller than the source file, performance increases but as the block size approaches and then passes the source file size, you see decreasing benefit to the point of negative gains (see the values for the 1MB file size) For some of the moderately-sized source files, small blocks (256KB) are best As the size of the source file gets larger (see values for 50MB and up), the smallest block size is not the most efficient (presumably due, at least in part, to the increased number of blocks, increased number of individual transfer requests, and reassembly/committal costs). Once you pass the 250MB source file size, the difference in rate for 1MB to 4MB blocks is more-or-less constant The 1MB block size gives the best average improvement (~16x) but the optimal approach would be to vary the block size based on the size of the source file.    (click chart for full size image) The above is another view of the same data as the prior chart just with the axis changed (x-axis represents file size and plotted data shows improvement by block size). It again highlights the fact that the 1MB block size is probably the best overall size but highlights the benefits of some of the other block sizes at different source file sizes. This last chart shows the change in total duration of the file uploads based on different block sizes for the source file sizes. Nothing really new here other than this view of the data highlights the negative affects of poorly choosing a block size for smaller files.   Summary What we have found so far is that blocking your file uploads and uploading them in parallel results in significant performance improvements. Further, utilizing extension methods and the Task Parallel Library (.NET 4.0) make short work of altering the shipping client library to provide this functionality while minimizing the amount of change to existing applications that might be using the client library for other interactions.   Related Resources Source code for upload test application Source code for random file generator ODatas feed of raw data from non-optimized transfer tests Experiment Metadata Experiment Datasets 2KB Uploads 32KB Uploads 64KB Uploads 128KB Uploads 256KB Uploads 512KB Uploads 1MB Uploads 5MB Uploads 10MB Uploads 25MB Uploads 50MB Uploads 100MB Uploads 250MB Uploads 500MB Uploads 750MB Uploads 1GB Uploads Raw Data OData feeds of raw data from blocked/parallelized transfer tests Experiment Metadata Experiment Datasets Raw Data 256KB Blocks 512KB Blocks 1MB Blocks 2MB Blocks 4MB Blocks Excel worksheet showing summarizations and comparisons

    Read the article

  • await, WhenAll, WaitAll, oh my!!

    - by cibrax
    If you are dealing with asynchronous work in .NET, you might know that the Task class has become the main driver for wrapping asynchronous calls. Although this class was officially introduced in .NET 4.0, the programming model for consuming tasks was much more simplified in C# 5.0 in .NET 4.5 with the addition of the new async/await keywords. In a nutshell, you can use these keywords to make asynchronous calls as if they were sequential, and avoiding in that way any fork or callback in the code. The compiler takes care of the rest. I was yesterday writing some code for making multiple asynchronous calls to backend services in parallel. The code looked as follow, var allResults = new List<Result>(); foreach(var provider in providers) { var results = await provider.GetResults(); allResults.AddRange(results); } return allResults; You see, I was using the await keyword to make multiple calls in parallel. Something I did not consider was the overhead this code implied after being compiled. I started an interesting discussion with some smart folks in twitter. One of them, Tugberk Ugurlu, had the brilliant idea of actually write some code to make a performance comparison with another approach using Task.WhenAll. There are two additional methods you can use to wait for the results of multiple calls in parallel, WhenAll and WaitAll. WhenAll creates a new task and waits for results in that new task, so it does not block the calling thread. WaitAll, on the other hand, blocks the calling thread. This is the code Tugberk initially wrote, and I modified afterwards to also show the results of WaitAll. class Program { private static Func<Stopwatch, Task>[] funcs = new Func<Stopwatch, Task>[] { async (watch) => { watch.Start(); await Task.Delay(1000); Console.WriteLine("1000 one has been completed."); }, async (watch) => { await Task.Delay(1500); Console.WriteLine("1500 one has been completed."); }, async (watch) => { await Task.Delay(2000); Console.WriteLine("2000 one has been completed."); watch.Stop(); Console.WriteLine(watch.ElapsedMilliseconds + "ms has been elapsed."); } }; static void Main(string[] args) { Console.WriteLine("Await in loop work starts..."); DoWorkAsync().ContinueWith(task => { Console.WriteLine("Parallel work starts..."); DoWorkInParallelAsync().ContinueWith(t => { Console.WriteLine("WaitAll work starts..."); WaitForAll(); }); }); Console.ReadLine(); } static async Task DoWorkAsync() { Stopwatch watch = new Stopwatch(); foreach (var func in funcs) { await func(watch); } } static async Task DoWorkInParallelAsync() { Stopwatch watch = new Stopwatch(); await Task.WhenAll(funcs[0](watch), funcs[1](watch), funcs[2](watch)); } static void WaitForAll() { Stopwatch watch = new Stopwatch(); Task.WaitAll(funcs[0](watch), funcs[1](watch), funcs[2](watch)); } } After running this code, the results were very concluding. Await in loop work starts... 1000 one has been completed. 1500 one has been completed. 2000 one has been completed. 4532ms has been elapsed. Parallel work starts... 1000 one has been completed. 1500 one has been completed. 2000 one has been completed. 2007ms has been elapsed. WaitAll work starts... 1000 one has been completed. 1500 one has been completed. 2000 one has been completed. 2009ms has been elapsed. The await keyword in a loop does not really make the calls in parallel.

    Read the article

  • The WaitForAll Roadshow

    - by adweigert
    OK, so I took for granted some imaginative uses of WaitForAll but lacking that, here is how I am using. First, I have a nice little class called Parallel that allows me to spin together a list of tasks (actions) and then use WaitForAll, so here it is, WaitForAll's 15 minutes of fame ... First Parallel that allows me to spin together several Action delegates to execute, well in parallel.   public static class Parallel { public static ParallelQuery Task(Action action) { return new Action[] { action }.AsParallel(); } public static ParallelQuery> Task(Action action) { return new Action[] { action }.AsParallel(); } public static ParallelQuery Task(this ParallelQuery actions, Action action) { var list = new List(actions); list.Add(action); return list.AsParallel(); } public static ParallelQuery> Task(this ParallelQuery> actions, Action action) { var list = new List>(actions); list.Add(action); return list.AsParallel(); } }   Next, this is an example usage from an app I'm working on that just is rendering some basic computer information via WMI and performance counters. The WMI calls can be expensive given the distance and link speed of some of the computers it will be trying to communicate with. This is the actual MVC action from my controller to return the data for an individual computer.  public PartialViewResult Detail(string computerName) { var computer = this.Computers.Get(computerName); var perf = Factory.GetInstance(); var detail = new ComputerDetailViewModel() { Computer = computer }; try { var work = Parallel .Task(delegate { // Win32_ComputerSystem var key = computer.Name + "_Win32_ComputerSystem"; var system = this.Cache.Get(key); if (system == null) { using (var impersonation = computer.ImpersonateElevatedIdentity()) { system = computer.GetWmiContext().GetInstances().Single(); } this.Cache.Set(key, system); } detail.TotalMemory = system.TotalPhysicalMemory; detail.Manufacturer = system.Manufacturer; detail.Model = system.Model; detail.NumberOfProcessors = system.NumberOfProcessors; }) .Task(delegate { // Win32_OperatingSystem var key = computer.Name + "_Win32_OperatingSystem"; var os = this.Cache.Get(key); if (os == null) { using (var impersonation = computer.ImpersonateElevatedIdentity()) { os = computer.GetWmiContext().GetInstances().Single(); } this.Cache.Set(key, os); } detail.OperatingSystem = os.Caption; detail.OSVersion = os.Version; }) // Performance Counters .Task(delegate { using (var impersonation = computer.ImpersonateElevatedIdentity()) { detail.AvailableBytes = perf.GetSample(computer, "Memory", "Available Bytes"); } }) .Task(delegate { using (var impersonation = computer.ImpersonateElevatedIdentity()) { detail.TotalProcessorUtilization = perf.GetValue(computer, "Processor", "% Processor Time", "_Total"); } }).WithExecutionMode(ParallelExecutionMode.ForceParallelism); if (!work.WaitForAll(TimeSpan.FromSeconds(15), task => task())) { return PartialView("Timeout"); } } catch (Exception ex) { this.LogException(ex); return PartialView("Error.ascx"); } return PartialView(detail); }

    Read the article

  • Queueing Effect.Parallels in Scriptaculous doesn't work

    - by Matthew Robertson
    Each block of animations, grouped in an Effect.Parallel, runs simultaneously. That works fine. Then, I want each of the Effect.Parallels to trigger sequentially, with a delay. The second block doesn't wait its turn. It fires when the function is run. Why?! ///// FIRST BLOCK ///// new Effect.Parallel([ new Effect.Morph... ], { queue: 'front' }); ///// SECOND BLOCK ///// new Effect.Parallel([ Element.toggleClassName($$('#add_comment_button .glyph').first(), 'yay') ], { sync: true, queue: 'end', delay: 1 }); ///// THIRD BLOCK ///// new Effect.Parallel([ new Effect.SlideUp... ], { queue: 'end', delay: 4 });

    Read the article

  • High Throughput and Windows Workflow Foundation

    - by SometimesUseful
    Can WWF handle high throughput scenarios where several dozen records are 'actively' being processed in parallel at any one time? We want to build a workflow process which handles a few thousand records per hour. Each record takes up to a minute to process, because it makes external web service calls. We are testing Windows Workflow Foundation to do this. But our demo programs show processing of each record appear to be running in sequence not in parallel, when we use parallel activities to process several records at once within one workflow instance. Should we use multiple workflow instances or parallel activities? Are there any known patterns for high performance WWF processing?

    Read the article

  • Parallelism in .NET – Part 7, Some Differences between PLINQ and LINQ to Objects

    - by Reed
    In my previous post on Declarative Data Parallelism, I mentioned that PLINQ extends LINQ to Objects to support parallel operations.  Although nearly all of the same operations are supported, there are some differences between PLINQ and LINQ to Objects.  By introducing Parallelism to our declarative model, we add some extra complexity.  This, in turn, adds some extra requirements that must be addressed. In order to illustrate the main differences, and why they exist, let’s begin by discussing some differences in how the two technologies operate, and look at the underlying types involved in LINQ to Objects and PLINQ . LINQ to Objects is mainly built upon a single class: Enumerable.  The Enumerable class is a static class that defines a large set of extension methods, nearly all of which work upon an IEnumerable<T>.  Many of these methods return a new IEnumerable<T>, allowing the methods to be chained together into a fluent style interface.  This is what allows us to write statements that chain together, and lead to the nice declarative programming model of LINQ: double min = collection .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } Other LINQ variants work in a similar fashion.  For example, most data-oriented LINQ providers are built upon an implementation of IQueryable<T>, which allows the database provider to turn a LINQ statement into an underlying SQL query, to be performed directly on the remote database. PLINQ is similar, but instead of being built upon the Enumerable class, most of PLINQ is built upon a new static class: ParallelEnumerable.  When using PLINQ, you typically begin with any collection which implements IEnumerable<T>, and convert it to a new type using an extension method defined on ParallelEnumerable: AsParallel().  This method takes any IEnumerable<T>, and converts it into a ParallelQuery<T>, the core class for PLINQ.  There is a similar ParallelQuery class for working with non-generic IEnumerable implementations. This brings us to our first subtle, but important difference between PLINQ and LINQ – PLINQ always works upon specific types, which must be explicitly created. Typically, the type you’ll use with PLINQ is ParallelQuery<T>, but it can sometimes be a ParallelQuery or an OrderedParallelQuery<T>.  Instead of dealing with an interface, implemented by an unknown class, we’re dealing with a specific class type.  This works seamlessly from a usage standpoint – ParallelQuery<T> implements IEnumerable<T>, so you can always “switch back” to an IEnumerable<T>.  The difference only arises at the beginning of our parallelization.  When we’re using LINQ, and we want to process a normal collection via PLINQ, we need to explicitly convert the collection into a ParallelQuery<T> by calling AsParallel().  There is an important consideration here – AsParallel() does not need to be called on your specific collection, but rather any IEnumerable<T>.  This allows you to place it anywhere in the chain of methods involved in a LINQ statement, not just at the beginning.  This can be useful if you have an operation which will not parallelize well or is not thread safe.  For example, the following is perfectly valid, and similar to our previous examples: double min = collection .AsParallel() .Select(item => item.SomeOperation()) .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); However, if SomeOperation() is not thread safe, we could just as easily do: double min = collection .Select(item => item.SomeOperation()) .AsParallel() .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .Min(item => item.PerformComputation()); In this case, we’re using standard LINQ to Objects for the Select(…) method, then converting the results of that map routine to a ParallelQuery<T>, and processing our filter (the Where method) and our aggregation (the Min method) in parallel. PLINQ also provides us with a way to convert a ParallelQuery<T> back into a standard IEnumerable<T>, forcing sequential processing via standard LINQ to Objects.  If SomeOperation() was thread-safe, but PerformComputation() was not thread-safe, we would need to handle this by using the AsEnumerable() method: double min = collection .AsParallel() .Select(item => item.SomeOperation()) .Where(item => item.SomeProperty > 6 && item.SomeProperty < 24) .AsEnumerable() .Min(item => item.PerformComputation()); Here, we’re converting our collection into a ParallelQuery<T>, doing our map operation (the Select(…) method) and our filtering in parallel, then converting the collection back into a standard IEnumerable<T>, which causes our aggregation via Min() to be performed sequentially. This could also be written as two statements, as well, which would allow us to use the language integrated syntax for the first portion: var tempCollection = from item in collection.AsParallel() let e = item.SomeOperation() where (e.SomeProperty > 6 && e.SomeProperty < 24) select e; double min = tempCollection.AsEnumerable().Min(item => item.PerformComputation()); This allows us to use the standard LINQ style language integrated query syntax, but control whether it’s performed in parallel or serial by adding AsParallel() and AsEnumerable() appropriately. The second important difference between PLINQ and LINQ deals with order preservation.  PLINQ, by default, does not preserve the order of of source collection. This is by design.  In order to process a collection in parallel, the system needs to naturally deal with multiple elements at the same time.  Maintaining the original ordering of the sequence adds overhead, which is, in many cases, unnecessary.  Therefore, by default, the system is allowed to completely change the order of your sequence during processing.  If you are doing a standard query operation, this is usually not an issue.  However, there are times when keeping a specific ordering in place is important.  If this is required, you can explicitly request the ordering be preserved throughout all operations done on a ParallelQuery<T> by using the AsOrdered() extension method.  This will cause our sequence ordering to be preserved. For example, suppose we wanted to take a collection, perform an expensive operation which converts it to a new type, and display the first 100 elements.  In LINQ to Objects, our code might look something like: // Using IEnumerable<SourceClass> collection IEnumerable<ResultClass> results = collection .Select(e => e.CreateResult()) .Take(100); If we just converted this to a parallel query naively, like so: IEnumerable<ResultClass> results = collection .AsParallel() .Select(e => e.CreateResult()) .Take(100); We could very easily get a very different, and non-reproducable, set of results, since the ordering of elements in the input collection is not preserved.  To get the same results as our original query, we need to use: IEnumerable<ResultClass> results = collection .AsParallel() .AsOrdered() .Select(e => e.CreateResult()) .Take(100); This requests that PLINQ process our sequence in a way that verifies that our resulting collection is ordered as if it were processed serially.  This will cause our query to run slower, since there is overhead involved in maintaining the ordering.  However, in this case, it is required, since the ordering is required for correctness. PLINQ is incredibly useful.  It allows us to easily take nearly any LINQ to Objects query and run it in parallel, using the same methods and syntax we’ve used previously.  There are some important differences in operation that must be considered, however – it is not a free pass to parallelize everything.  When using PLINQ in order to parallelize your routines declaratively, the same guideline I mentioned before still applies: Parallelization is something that should be handled with care and forethought, added by design, and not just introduced casually.

    Read the article

  • Parallelism in .NET – Part 15, Making Tasks Run: The TaskScheduler

    - by Reed
    In my introduction to the Task class, I specifically made mention that the Task class does not directly provide it’s own execution.  In addition, I made a strong point that the Task class itself is not directly related to threads or multithreading.  Rather, the Task class is used to implement our decomposition of tasks.  Once we’ve implemented our tasks, we need to execute them.  In the Task Parallel Library, the execution of Tasks is handled via an instance of the TaskScheduler class. The TaskScheduler class is an abstract class which provides a single function: it schedules the tasks and executes them within an appropriate context.  This class is the class which actually runs individual Task instances.  The .NET Framework provides two (internal) implementations of the TaskScheduler class. Since a Task, based on our decomposition, should be a self-contained piece of code, parallel execution makes sense when executing tasks.  The default implementation of the TaskScheduler class, and the one most often used, is based on the ThreadPool.  This can be retrieved via the TaskScheduler.Default property, and is, by default, what is used when we just start a Task instance with Task.Start(). Normally, when a Task is started by the default TaskScheduler, the task will be treated as a single work item, and run on a ThreadPool thread.  This pools tasks, and provides Task instances all of the advantages of the ThreadPool, including thread pooling for reduced resource usage, and an upper cap on the number of work items.  In addition, .NET 4 brings us a much improved thread pool, providing work stealing and reduced locking within the thread pool queues.  By using the default TaskScheduler, our Tasks are run asynchronously on the ThreadPool. There is one notable exception to my above statements when using the default TaskScheduler.  If a Task is created with the TaskCreationOptions set to TaskCreationOptions.LongRunning, the default TaskScheduler will generate a new thread for that Task, at least in the current implementation.  This is useful for Tasks which will persist for most of the lifetime of your application, since it prevents your Task from starving the ThreadPool of one of it’s work threads. The Task Parallel Library provides one other implementation of the TaskScheduler class.  In addition to providing a way to schedule tasks on the ThreadPool, the framework allows you to create a TaskScheduler which works within a specified SynchronizationContext.  This scheduler can be retrieved within a thread that provides a valid SynchronizationContext by calling the TaskScheduler.FromCurrentSynchronizationContext() method. This implementation of TaskScheduler is intended for use with user interface development.  Windows Forms and Windows Presentation Foundation both require any access to user interface controls to occur on the same thread that created the control.  For example, if you want to set the text within a Windows Forms TextBox, and you’re working on a background thread, that UI call must be marshaled back onto the UI thread.  The most common way this is handled depends on the framework being used.  In Windows Forms, Control.Invoke or Control.BeginInvoke is most often used.  In WPF, the equivelent calls are Dispatcher.Invoke or Dispatcher.BeginInvoke. As an example, say we’re working on a background thread, and we want to update a TextBlock in our user interface with a status label.  The code would typically look something like: // Within background thread work... string status = GetUpdatedStatus(); Dispatcher.BeginInvoke(DispatcherPriority.Normal, new Action( () => { statusLabel.Text = status; })); // Continue on in background method .csharpcode, .csharpcode pre { font-size: small; color: black; font-family: consolas, "Courier New", courier, monospace; background-color: #ffffff; /*white-space: pre;*/ } .csharpcode pre { margin: 0em; } .csharpcode .rem { color: #008000; } .csharpcode .kwrd { color: #0000ff; } .csharpcode .str { color: #006080; } .csharpcode .op { color: #0000c0; } .csharpcode .preproc { color: #cc6633; } .csharpcode .asp { background-color: #ffff00; } .csharpcode .html { color: #800000; } .csharpcode .attr { color: #ff0000; } .csharpcode .alt { background-color: #f4f4f4; width: 100%; margin: 0em; } .csharpcode .lnum { color: #606060; } This works fine, but forces your method to take a dependency on WPF or Windows Forms.  There is an alternative option, however.  Both Windows Forms and WPF, when initialized, setup a SynchronizationContext in their thread, which is available on the UI thread via the SynchronizationContext.Current property.  This context is used by classes such as BackgroundWorker to marshal calls back onto the UI thread in a framework-agnostic manner. The Task Parallel Library provides the same functionality via the TaskScheduler.FromCurrentSynchronizationContext() method.  When setting up our Tasks, as long as we’re working on the UI thread, we can construct a TaskScheduler via: TaskScheduler uiScheduler = TaskScheduler.FromCurrentSynchronizationContext(); We then can use this scheduler on any thread to marshal data back onto the UI thread.  For example, our code above can then be rewritten as: string status = GetUpdatedStatus(); (new Task(() => { statusLabel.Text = status; })) .Start(uiScheduler); // Continue on in background method This is nice since it allows us to write code that isn’t tied to Windows Forms or WPF, but is still fully functional with those technologies.  I’ll discuss even more uses for the SynchronizationContext based TaskScheduler when I demonstrate task continuations, but even without continuations, this is a very useful construct. In addition to the two implementations provided by the Task Parallel Library, it is possible to implement your own TaskScheduler.  The ParallelExtensionsExtras project within the Samples for Parallel Programming provides nine sample TaskScheduler implementations.  These include schedulers which restrict the maximum number of concurrent tasks, run tasks on a single threaded apartment thread, use a new thread per task, and more.

    Read the article

  • Microsoft F#

    - by Aamir Hasan
    F# brings you type safe, succinct, efficient and expressive functional programming language on the .NET platform. It is a simple and pragmatic language, and has particular strengths in data-oriented programming, parallel I/O programming, parallel CPU programming, scripting and algorithmic development. F# cannot solve any problem C# could. F# is a functional language, statically typed. F# is a functional language that supports O-O-Programming References:http://msdn.microsoft.com/en-us/fsharp/cc835246.aspx http://research.microsoft.com/en-us/um/cambridge/projects/fsharp/

    Read the article

  • MathWorks offre une nouvelle fonctionnalité de calculs parallèles pour une simulation plus rapide et une génération de code améliorée

    MathWorks propose une nouvelle fonctionnalité de calculs parallèles Pour une simulation plus rapide et une génération de code améliorée grâce à Parallel Computing Toolbox MathWorks a annoncé aujourd'hui une nouvelle fonctionnalité qui permet d'accélérer la génération de code de système utilisant le référencement de modèles. Cette amélioration est rendue possible par Real-Time Workshop, un outil de génération de code qui tire désormais parti des outils d'amélioration de performance de la Parallel Computing Toolbox et du MATLAB Distributed Computing Server (MDCS). Cette fonction élargit également la prise en charge des calculs parallèles dans d'autres outils MathWorks pour améliorer...

    Read the article

  • IO Profiling of Applications: MPI Apps

    <b>Linux Magazine:</b> "In the last article we looked at using strace to examine the IO patterns of simple serial applications. In the High Performance Computing (HPC) world, applications use MPI (Message Passing Interface) to create parallel applications. This time around we discuss how to attack parallel applications using strace."

    Read the article

  • Detecting Hyper-Threading state

    - by jchang
    To interpret performance counters and execution statistics correctly, it is necessary to know state of Hyper-Threading. In principle, at low overall CPU utilization, for non-parallel execution plans, it should not matter whether HT is enabled or not. Of course, DBA life is never that simple. The state of HT does matter at high over utilization and in parallel execution plans depending on the DOP. SQL Server does seem to try to allocate threads on distinct physical cores at intermediate DOP (DOP less...(read more)

    Read the article

  • Detecting Hyper-Threading state

    - by jchang
    To interpret performance counters and execution statistics correctly, it is necessary to know state of Hyper-Threading. In principle, at low overall CPU utilization, for non-parallel execution plans, it should not matter whether HT is enabled or not. Of course, DBA life is never that simple. The state of HT does matter at high over utilization and in parallel execution plans depending on the DOP. SQL Server does seem to try to allocate threads on distinct physical cores at intermediate DOP (DOP less...(read more)

    Read the article

  • Parallelism in .NET – Part 1, Decomposition

    - by Reed
    The first step in designing any parallelized system is Decomposition.  Decomposition is nothing more than taking a problem space and breaking it into discrete parts.  When we want to work in parallel, we need to have at least two separate things that we are trying to run.  We do this by taking our problem and decomposing it into parts. There are two common abstractions that are useful when discussing parallel decomposition: Data Decomposition and Task Decomposition.  These two abstractions allow us to think about our problem in a way that helps leads us to correct decision making in terms of the algorithms we’ll use to parallelize our routine. To start, I will make a couple of minor points. I’d like to stress that Decomposition has nothing to do with specific algorithms or techniques.  It’s about how you approach and think about the problem, not how you solve the problem using a specific tool, technique, or library.  Decomposing the problem is about constructing the appropriate mental model: once this is done, you can choose the appropriate design and tools, which is a subject for future posts. Decomposition, being unrelated to tools or specific techniques, is not specific to .NET in any way.  This should be the first step to parallelizing a problem, and is valid using any framework, language, or toolset.  However, this gives us a starting point – without a proper understanding of decomposition, it is difficult to understand the proper usage of specific classes and tools within the .NET framework. Data Decomposition is often the simpler abstraction to use when trying to parallelize a routine.  In order to decompose our problem domain by data, we take our entire set of data and break it into smaller, discrete portions, or chunks.  We then work on each chunk in the data set in parallel. This is particularly useful if we can process each element of data independently of the rest of the data.  In a situation like this, there are some wonderfully simple techniques we can use to take advantage of our data.  By decomposing our domain by data, we can very simply parallelize our routines.  In general, we, as developers, should be always searching for data that can be decomposed. Finding data to decompose if fairly simple, in many instances.  Data decomposition is typically used with collections of data.  Any time you have a collection of items, and you’re going to perform work on or with each of the items, you potentially have a situation where parallelism can be exploited.  This is fairly easy to do in practice: look for iteration statements in your code, such as for and foreach. Granted, every for loop is not a candidate to be parallelized.  If the collection is being modified as it’s iterated, or the processing of elements depends on other elements, the iteration block may need to be processed in serial.  However, if this is not the case, data decomposition may be possible. Let’s look at one example of how we might use data decomposition.  Suppose we were working with an image, and we were applying a simple contrast stretching filter.  When we go to apply the filter, once we know the minimum and maximum values, we can apply this to each pixel independently of the other pixels.  This means that we can easily decompose this problem based off data – we will do the same operation, in parallel, on individual chunks of data (each pixel). Task Decomposition, on the other hand, is focused on the individual tasks that need to be performed instead of focusing on the data.  In order to decompose our problem domain by tasks, we need to think about our algorithm in terms of discrete operations, or tasks, which can then later be parallelized. Task decomposition, in practice, can be a bit more tricky than data decomposition.  Here, we need to look at what our algorithm actually does, and how it performs its actions.  Once we have all of the basic steps taken into account, we can try to analyze them and determine whether there are any constraints in terms of shared data or ordering.  There are no simple things to look for in terms of finding tasks we can decompose for parallelism; every algorithm is unique in terms of its tasks, so every algorithm will have unique opportunities for task decomposition. For example, say we want our software to perform some customized actions on startup, prior to showing our main screen.  Perhaps we want to check for proper licensing, notify the user if the license is not valid, and also check for updates to the program.  Once we verify the license, and that there are no updates, we’ll start normally.  In this case, we can decompose this problem into tasks – we have a few tasks, but there are at least two discrete, independent tasks (check licensing, check for updates) which we can perform in parallel.  Once those are completed, we will continue on with our other tasks. One final note – Data Decomposition and Task Decomposition are not mutually exclusive.  Often, you’ll mix the two approaches while trying to parallelize a single routine.  It’s possible to decompose your problem based off data, then further decompose the processing of each element of data based on tasks.  This just provides a framework for thinking about our algorithms, and for discussing the problem.

    Read the article

  • Recommendations for a JetDirect print server for USB 2.0 printers?

    - by eleven81
    I have been using some older HP JetDirect 300x print servers for a variety of parallel printers over the years. These things work great for every printer I have tried them with, including HP's, Dell's, and even a Mountbatten braille embosser! These have been a boon for printers whose internal network cards fail, but whose parallel ports continue working. I don't have to throw away the $500 printer that is one year and a week old, and can keep using it for many, many years. Now that very few printers are coming with parallel ports, but are coming solely with USB connections and network cards. When the network card fails but the printer is still usable, I want to continue using it on the network with a JetDirect card. In summary: Does anyone have any recommendations for JetDirect cards that will work as well with USB 2.0 printers of unspecified manufacturer that my old JetDirect 300x cards do?

    Read the article

  • Excel 2010: How to color the area between charts?

    - by Quasdunk
    Hello, I asked this question already on stackoverflow but it hasn't been answered yet. Instead I was advised to try it here, so here I go :) So there's that simple XY-Line-Chart in Excel (2010). It is surrounded by two other graphs which are parallel but offset by the same factor in both the positive and negative direction - something like this: ---------------- (positively offset parallel graph) ---------------- (main graph) ---------------- (negatively offset parallel graph) Now I want to color the space between the main graph and the offset ones, but I just can't seem to find a way! Is it maybe possible with VBA? Or is there maybe a solution for Excel 2007?

    Read the article

  • Dryad and DryadLINQ from MSR

    - by Daniel Moth
    Microsoft Research (MSR) researches technologies, incubates projects which many times result in technology that looks like a ready-to-use product (but it is important to understand that these are not the same as products built by the various… actual product teams here at Microsoft). A very popular MSR project has been DryadLINQ, which itself builds on Dryad. To learn more follow the project pages I just linked to and I also recommend this 1-hour channel 9 video. If you only have 3 minutes, watch this great elevator pitch instead. You can also stay tuned on the official blog, which includes a post that refers to internal adoption e.g by Bing, a quick DryadLINQ code example, and some history on how DryadLINQ generalizes the MapReduce pattern and makes it accessible to regular programmers (see this post and that post). Essentially, the DryadLINQ framework (building on the Dryad runtime) allows developers to re-use their LINQ skills for creating/generating programs that process large multi-gigabyte/terabyte datasets across 100s-1000s of machines. One way to think about it is that just as Parallel LINQ allows LINQ developers to seamlessly use multiple cores from a single process on a single machine, DryadLINQ allows LINQ developers to seamlessly use multiple machines for their data parallel algorithms. In the former scenario the motivation was speed of execution, in the latter it is speed of execution AND processing large datasets that simply don't fit on a single machine. Whenever I hear about execution of parallel code on multiple machines on the Microsoft platform, I immediately think of Windows HPC Server. Indeed Dryad and DryadLINQ were made available for Windows HPC Server and I encourage you to watch the PDC session on this topic: Data-Intensive Computing on Windows HPC Server with the DryadLINQ Framework. Watch this space… Comments about this post welcome at the original blog.

    Read the article

  • Are you a GPGPU developer? Participate in our UX study

    - by Daniel Moth
    You know that I work on the parallel debugger in Visual Studio and I've talked about GPGPU before and I have also mentioned UX. Below is a request from my UX colleagues that pulls all of it together. If you write and debug parallel code that uses GPUs for non-graphical, computationally intensive operations keep reading. The Microsoft Visual Studio Parallel Computing team is seeking developers for a 90-minute research study. The study will take place via LiveMeeting or at a usability lab in Redmond, depending on your preference. We will walk you through an example of debugging GPGPU code in Visual Studio with you giving us step-by-step feedback. ("Is this what you would you expect?", "Are we showing you the things that would help you?", "How would you improve this") The walkthrough utilizes a “paper” version of our current design. After the walkthrough, we would then show you some additional design ideas and seek your input on various design tradeoffs. Are you interested or know someone who might be a good fit? Let us know at this address: [email protected]. Those who participate (and those who referred them), will receive a gratuity item from a list of current Microsoft products. Comments about this post welcome at the original blog.

    Read the article

  • Are you a GPGPU developer? Participate in our UX study

    - by Daniel Moth
    You know that I work on the parallel debugger in Visual Studio and I've talked about GPGPU before and I have also mentioned UX. Below is a request from my UX colleagues that pulls all of it together. If you write and debug parallel code that uses GPUs for non-graphical, computationally intensive operations keep reading. The Microsoft Visual Studio Parallel Computing team is seeking developers for a 90-minute research study. The study will take place via LiveMeeting or at a usability lab in Redmond, depending on your preference. We will walk you through an example of debugging GPGPU code in Visual Studio with you giving us step-by-step feedback. ("Is this what you would you expect?", "Are we showing you the things that would help you?", "How would you improve this") The walkthrough utilizes a “paper” version of our current design. After the walkthrough, we would then show you some additional design ideas and seek your input on various design tradeoffs. Are you interested or know someone who might be a good fit? Let us know at this address: [email protected]. Those who participate (and those who referred them), will receive a gratuity item from a list of current Microsoft products. Comments about this post welcome at the original blog.

    Read the article

  • Isolating test data in acceptance tests

    - by Matt Phillips
    I'm looking for guidance on how to keep my acceptance tests isolated. Right now the issue I'm having with being able to run the tests in parallel is the database records that are manipulated in the tests. I've written helpers that take care of doing inserts and deletes before tests are executed, to make sure the state is correct. But now I can't run them in parallel against the same database without uniquely generating the test data fields for each test. For example. Testing creating a row i'll delete everything where column A = foo and column B = bar Then I'll navigate through the UI in the test and create a record with column A = foo and column B = bar. Testing that a duplicate row is not allowed to be created. I'll insert a row with column A = foo and column B = bar and then use the UI to try and do the exact same thing. This will display an error message in the UI as expected. These tests work perfectly when ran separately and serially. But I can't run them at the same time for fear that one will create or delete a record the other is expecting. Any tips on how to structure them better so they can be run in parallel?

    Read the article

  • GLES2.0 3D Android game performance and multi threading the update?

    - by Ofer
    I have profiled my mixed Java\C++ Android game and I got the following result: https://dl.dropbox.com/u/8025882/PompiDev/AndroidProfile.png As you can see, the pink think is a C++ functions that updates the game. It does things like updating the logic but it mostly it generates a "request list" for rendering. The thing is, I generate DrawLists on C++ and then send them to Java to process and draw using GLES2.0. Since then I was able to improve update from 9ms down to about 7ms, but I would like to ask if I would benefit from multi threading the update? As I understand from that diagram is that the function that takes the most time is the one you see it's color on the timeline. So the pink area is taken mostly by update. The other area has MainOpenGL.Handle as it's main contributor(whch is my java function), but since it's not drawn to the top of the diagram I can conclude other things are happening at the same time that use the CPU? Or even GPU stuff that isn't shown in this diagram. I am not sure how the GPU works on this. Does it calculate stuff in parallel to the CPU? Or is it part of the CPU usage as in SoC? I am not sure. Anyway, in case GPU things DO happen in parallel to CPU, then I would guess that if I do this C++ Update in parallel to the thread that makes the OpenGL calls, I might make use of "dead CPU time" due to GPU stalling or maybe have the GPU calls getting processed earlier because it won't have to wait for Update to finish? How do you suggest to improve performance based on that? Thanks.

    Read the article

< Previous Page | 19 20 21 22 23 24 25 26 27 28 29 30  | Next Page >