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• Java concurrency - Should block or yield?

  - by teto

  Hi, I have multiple threads each one with its own private concurrent queue and all they do is run an infinite loop retrieving messages from it. It could happen that one of the queues doesn't receive messages for a period of time (maybe a couple seconds), and also they could come in big bursts and fast processing is necessary. I would like to know what would be the most appropriate to do in the first case: use a blocking queue and block the thread until I have more input or do a Thread.yield()? I want to have as much CPU resources available as possible at a given time, as the number of concurrent threads may increase with time, but also I don't want the message processing to fall behind, as there is no guarantee of when the thread will be reescheduled for execution when doing a yield(). I know that hardware, operating system and other factors play an important role here, but setting that aside and looking at it from a Java (JVM?) point of view, what would be the most optimal?

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• Opengl problem with texture in model from obj

  - by subSeven

  Hello! I writing small program in OpenGL, and I have problem ( textures are skew, and I dont know why, this model work in another obj viewer) What I have: http://img696.imageshack.us/i/obrazo.png/ What I want http://img88.imageshack.us/i/obraz2d.jpg/ This code where I load texture: bool success; ILuint texId; GLuint image; ilGenImages(1, &texId); ilBindImage(texId); success = ilLoadImage((WCHAR*)fileName.c_str()); if(success) { success = ilConvertImage(IL_RGB, IL_UNSIGNED_BYTE); if(!success) { return false; } } else { return false; } glGenTextures(1, &image); glBindTexture(GL_TEXTURE_2D, image); glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR); glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR); glTexImage2D(GL_TEXTURE_2D, 0, ilGetInteger(IL_IMAGE_BPP), ilGetInteger(IL_IMAGE_WIDTH), ilGetInteger(IL_IMAGE_HEIGHT), 0, ilGetInteger(IL_IMAGE_FORMAT), GL_UNSIGNED_BYTE, ilGetData()); ilDeleteImages(1, &texId); Code to load obj: triangles.clear(); std::ifstream in; std::string cmd; in.open (fileName.c_str()); if (in.is_open()) { while(!in.eof()) { in>>cmd; if(cmd=="v") { Vector3d vector; in>>vector.x; in>>vector.y; in>>vector.z; points.push_back(vector); } if(cmd=="vt") { Vector2d texcord; in>>texcord.x; in>>texcord.y; texcords.push_back(texcord); } if(cmd=="vn") { Vector3d normal; in>>normal.x; in>>normal.y; in>>normal.z; normals.push_back(normal); } if(cmd=="f") { Triangle triangle; std::string str; std::string str1,str2,str3; std::string delimeter("/"); int pos; int n; std::stringstream ss (std::stringstream::in | std::stringstream::out); in>>str; pos = str.find(delimeter); str1 = str.substr(0,pos); str2 = str.substr(pos+delimeter.length()); pos = str2.find(delimeter); str3 = str2.substr(pos+delimeter.length()); str2 = str2.substr(0,pos); ss<<str1; ss>>n; triangle.a= n-1; ss.clear(); ss<<str3; ss>>n; triangle.an =n-1; ss.clear(); ss<<str2; ss>>n; ss.clear(); triangle.atc = n-1; in>>str; pos = str.find(delimeter); str1 = str.substr(0,pos); str2 = str.substr(pos+delimeter.length()); pos = str2.find(delimeter); str3 = str2.substr(pos+delimeter.length()); str2 = str2.substr(0,pos); ss<<str1; ss>>n; triangle.b= n-1; ss.clear(); ss<<str3; ss>>n; triangle.bn =n-1; ss.clear(); ss<<str2; ss>>n; ss.clear(); triangle.btc = n-1; in>>str; pos = str.find(delimeter); str1 = str.substr(0,pos); str2 = str.substr(pos+delimeter.length()); pos = str2.find(delimeter); str3 = str2.substr(pos+delimeter.length()); str2 = str2.substr(0,pos); ss<<str1; ss>>n; triangle.c= n-1; ss.clear(); ss<<str3; ss>>n; triangle.cn =n-1; ss.clear(); ss<<str2; ss>>n; ss.clear(); triangle.ctc = n-1; triangles.push_back(triangle); } cmd = ""; } in.close(); return true; } return false; Code to draw model: glEnable(GL_TEXTURE_2D); glTexEnvi(GL_TEXTURE_ENV, GL_TEXTURE_ENV_MODE, GL_DECAL); glBindTexture(GL_TEXTURE_2D,image); glBegin(GL_TRIANGLES); for(int i=0;i<triangles.size();i++) { glTexCoord2f(texcords[triangles[i].ctc].x, texcords[triangles[i].ctc].y); glNormal3f(normals[triangles[i].cn].x, normals[triangles[i].cn].y, normals[triangles[i].cn].z); glVertex3f( points[triangles[i].c].x, points[triangles[i].c].y, points[triangles[i].c].z); glTexCoord2f(texcords[triangles[i].btc].x, texcords[triangles[i].btc].y); glNormal3f(normals[triangles[i].bn].x, normals[triangles[i].bn].y, normals[triangles[i].bn].z); glVertex3f( points[triangles[i].b].x, points[triangles[i].b].y, points[triangles[i].b].z); glTexCoord2f(texcords[triangles[i].atc].x, texcords[triangles[i].atc].y); glNormal3f(normals[triangles[i].an].x, normals[triangles[i].an].y, normals[triangles[i].an].z); glVertex3f( points[triangles[i].a].x, points[triangles[i].a].y, points[triangles[i].a].z); } glEnd(); glDisable(GL_TEXTURE_2D); Mayby someone find mistake in this

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• Performance issues when using SSD for a developer notebook (WAMP/LAMP stack)?

  - by András Szepesházi

  I'm a web application developer using my notebook as a standalone development environment (WAMP stack). I just switched from a Core2-duo Vista 32 bit notebook with 2Gb RAM and SATA HDD, to an i5-2520M Win7 64 bit with 4Gb RAM and 128 GB SDD (Corsair P3 128). My initial experience was what I expected, fast boot, quick load of all the applications (Eclipse takes now 5 seconds as opposed to 30s on my old notebook), overall great experience. Then I started to build up my development stack, both as LAMP (using VirtualBox with a debian guest) and WAMP (windows native apache + mysql + php). I wanted to compare those two. This still all worked great out, then I started to pull in my projects to these stacks. And here came the nasty surprise, one of those projects produced a lot worse response times than on my old notebook (that was true for both the VirtualBox and WAMP stack). Apache, php and mysql configurations were practically identical in all environments. I started to do a lot of benchmarking and profiling, and here is what I've found: All general benchmarks (Performance Test 7.0, HDTune Pro, wPrime2 and some more) gave a big advantage to the new notebook. Nothing surprising here. Disc specific tests showed that read/write operations peaked around 380M/160M for the SSD, and all the different sized block operations also performed very well. Started apache performance benchmarking with Apache Benchmark for a small static html file (10 concurrent threads, 500 iterations). Old notebook: min 47ms, median 111ms, max 156ms New WAMP stack: min 71ms, median 135ms, max 296ms New LAMP stack (in VirtualBox): min 6ms, median 46ms, max 175ms Right here I don't get why the native WAMP stack performed so bad, but at least the LAMP environment brought the expected speed. Apache performance measurement for non-cached php content. The php runs a loop of 1000 and generates sha1(uniqid()) inisde. Again, 10 concurrent threads, 500 iterations were used for the benchmark. Old notebook: min 0ms, median 39ms, max 218ms New WAMP stack: min 20ms, median 61ms, max 186ms New LAMP stack (in VirtualBox): min 124ms, median 704ms, max 2463ms What the hell? The new LAMP performed miserably, and even the new native WAMP was outperformed by the old notebook. php + mysql test. The test consists of connecting to a database and reading a single record form a table using INNER JOIN on 3 more (indexed) tables, repeated 100 times within a loop. Databases were identical. 10 concurrent threads, 100 iterations were used for the benchmark. Old notebook: min 1201ms, median 1734ms, max 3728ms New WAMP stack: min 367ms, median 675ms, max 1893ms New LAMP stack (in VirtualBox): min 1410ms, median 3659ms, max 5045ms And the same test with concurrency set to 1 (instead of 10): Old notebook: min 1201ms, median 1261ms, max 1357ms New WAMP stack: min 399ms, median 483ms, max 539ms New LAMP stack (in VirtualBox): min 285ms, median 348ms, max 444ms Strictly for my purposes, as I'm using a self contained development environment (= low concurrency) I could be satisfied with the second test's result. Though I have no idea why the VirtualBox environment performed so bad with higher concurrency. Finally I performed a test of including many php files. The application that I mentioned at the beginning, the one that was performing so bad, has a heavy bootstrap, loads hundreds of small library and configuration files while initializing. So this test does nothing else just includes about 100 files. Concurrency set to 1, 100 iterations: Old notebook: min 140ms, median 168ms, max 406ms New WAMP stack: min 434ms, median 488ms, max 604ms New LAMP stack (in VirtualBox): min 413ms, median 1040ms, max 1921ms Even if I consider that VirtualBox reached those files via shared folders, and that slows things down a bit, I still don't see how could the old notebook outperform so heavily both new configurations. And I think this is the real root of the slow performance, as the application uses even more includes, and the whole bootstrap will occur several times within a page request (for each ajax call, for example). To sum it up, here I am with a brand new high-performance notebook that loads the same page in 20 seconds, that my old notebook can do in 5-7 seconds. Needless to say, I'm not a very happy person right now. Why do you think I experience these poor performance values? What are my options to remedy this situation?

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• Le projet MonoDroid apporte .NET sur Android, Novell veut construire une passerelle entre le framewo

  Le projet MonoDroid apporte .NET sur Android Novell veut construire une passerelle entre le framework de Microsoft et l'OS de Google Ce n'est pas un scoop, .NET tend à se généraliser. Aujourd'hui, le framework de Microsoft pourrait bien toucher Android, la plateforme Java de son grand concurrent Google, grâce à un projet de Novell, l'éditeur de Mono. Petit retour sur le projet Mono. Mono est l'implantation open-source et portable du framework .Net. Certains vont même jusqu'à dire qu...

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• NET Math Libraries

  - by JoshReuben

  NET Mathematical Libraries   .NET Builder for Matlab The MathWorks Inc. - http://www.mathworks.com/products/netbuilder/ MATLAB Builder NE generates MATLAB based .NET and COM components royalty-free deployment creates the components by encrypting MATLAB functions and generating either a .NET or COM wrapper around them. .NET/Link for Mathematica www.wolfram.com a product that 2-way integrates Mathematica and Microsoft's .NET platform call .NET from Mathematica - use arbitrary .NET types directly from the Mathematica language. use and control the Mathematica kernel from a .NET program. turns Mathematica into a scripting shell to leverage the computational services of Mathematica. write custom front ends for Mathematica or use Mathematica as a computational engine for another program comes with full source code. Leverages MathLink - a Wolfram Research's protocol for sending data and commands back and forth between Mathematica and other programs. .NET/Link abstracts the low-level details of the MathLink C API. Extreme Optimization http://www.extremeoptimization.com/ a collection of general-purpose mathematical and statistical classes built for the.NET framework. It combines a math library, a vector and matrix library, and a statistics library in one package. download the trial of version 4.0 to try it out. Multi-core ready - Full support for Task Parallel Library features including cancellation. Broad base of algorithms covering a wide range of numerical techniques, including: linear algebra (BLAS and LAPACK routines), numerical analysis (integration and differentiation), equation solvers. Mathematics leverages parallelism using .NET 4.0's Task Parallel Library. Basic math: Complex numbers, 'special functions' like Gamma and Bessel functions, numerical differentiation. Solving equations: Solve equations in one variable, or solve systems of linear or nonlinear equations. Curve fitting: Linear and nonlinear curve fitting, cubic splines, polynomials, orthogonal polynomials. Optimization: find the minimum or maximum of a function in one or more variables, linear programming and mixed integer programming. Numerical integration: Compute integrals over finite or infinite intervals, over 2D and higher dimensional regions. Integrate systems of ordinary differential equations (ODE's). Fast Fourier Transforms: 1D and 2D FFT's using managed or fast native code (32 and 64 bit) BigInteger, BigRational, and BigFloat: Perform operations with arbitrary precision. Vector and Matrix Library Real and complex vectors and matrices. Single and double precision for elements. Structured matrix types: including triangular, symmetrical and band matrices. Sparse matrices. Matrix factorizations: LU decomposition, QR decomposition, singular value decomposition, Cholesky decomposition, eigenvalue decomposition. Portability and performance: Calculations can be done in 100% managed code, or in hand-optimized processor-specific native code (32 and 64 bit). Statistics Data manipulation: Sort and filter data, process missing values, remove outliers, etc. Supports .NET data binding. Statistical Models: Simple, multiple, nonlinear, logistic, Poisson regression. Generalized Linear Models. One and two-way ANOVA. Hypothesis Tests: 12 14 hypothesis tests, including the z-test, t-test, F-test, runs test, and more advanced tests, such as the Anderson-Darling test for normality, one and two-sample Kolmogorov-Smirnov test, and Levene's test for homogeneity of variances. Multivariate Statistics: K-means cluster analysis, hierarchical cluster analysis, principal component analysis (PCA), multivariate probability distributions. Statistical Distributions: 25 29 continuous and discrete statistical distributions, including uniform, Poisson, normal, lognormal, Weibull and Gumbel (extreme value) distributions. Random numbers: Random variates from any distribution, 4 high-quality random number generators, low discrepancy sequences, shufflers. New in version 4.0 (November, 2010) Support for .NET Framework Version 4.0 and Visual Studio 2010 TPL Parallellized – multicore ready sparse linear program solver - can solve problems with more than 1 million variables. Mixed integer linear programming using a branch and bound algorithm. special functions: hypergeometric, Riemann zeta, elliptic integrals, Frensel functions, Dawson's integral. Full set of window functions for FFT's. Product  Price Update subscription Single Developer License $999  $399  Team License (3 developers) $1999  $799  Department License (8 developers) $3999  $1599  Site License (Unlimited developers in one physical location) $7999  $3199    NMath http://www.centerspace.net .NET math and statistics libraries matrix and vector classes random number generators Fast Fourier Transforms (FFTs) numerical integration linear programming linear regression curve and surface fitting optimization hypothesis tests analysis of variance (ANOVA) probability distributions principal component analysis cluster analysis built on the Intel Math Kernel Library (MKL), which contains highly-optimized, extensively-threaded versions of BLAS (Basic Linear Algebra Subroutines) and LAPACK (Linear Algebra PACKage). Product  Price Update subscription Single Developer License $1295 $388 Team License (5 developers) $5180 $1554   DotNumerics http://www.dotnumerics.com/NumericalLibraries/Default.aspx free DotNumerics is a website dedicated to numerical computing for .NET that includes a C# Numerical Library for .NET containing algorithms for Linear Algebra, Differential Equations and Optimization problems. The Linear Algebra library includes CSLapack, CSBlas and CSEispack, ports from Fortran to C# of LAPACK, BLAS and EISPACK, respectively. Linear Algebra (CSLapack, CSBlas and CSEispack). Systems of linear equations, eigenvalue problems, least-squares solutions of linear systems and singular value problems. Differential Equations. Initial-value problem for nonstiff and stiff ordinary differential equations ODEs (explicit Runge-Kutta, implicit Runge-Kutta, Gear's BDF and Adams-Moulton). Optimization. Unconstrained and bounded constrained optimization of multivariate functions (L-BFGS-B, Truncated Newton and Simplex methods).   Math.NET Numerics http://numerics.mathdotnet.com/ free an open source numerical library - includes special functions, linear algebra, probability models, random numbers, interpolation, integral transforms. A merger of dnAnalytics with Math.NET Iridium in addition to a purely managed implementation will also support native hardware optimization. constants & special functions complex type support real and complex, dense and sparse linear algebra (with LU, QR, eigenvalues, ... decompositions) non-uniform probability distributions, multivariate distributions, sample generation alternative uniform random number generators descriptive statistics, including order statistics various interpolation methods, including barycentric approaches and splines numerical function integration (quadrature) routines integral transforms, like fourier transform (FFT) with arbitrary lengths support, and hartley spectral-space aware sequence manipulation (signal processing) combinatorics, polynomials, quaternions, basic number theory. parallelized where appropriate, to leverage multi-core and multi-processor systems fully managed or (if available) using native libraries (Intel MKL, ACMS, CUDA, FFTW) provides a native facade for F# developers

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• Google I/O 2011: Memory management for Android Apps

  Google I/O 2011: Memory management for Android Apps Patrick Dubroy Android apps have more memory available to them than ever before, but are you sure you're using it wisely? This talk will cover the memory management changes in Gingerbread and Honeycomb (concurrent GC, heap-allocated bitmaps, "largeHeap" option) and explore tools and techniques for profiling the memory usage of Android apps. From: GoogleDevelopers Views: 5698 45 ratings Time: 58:42 More in Science & Technology

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• Thread placement policies on NUMA systems - update

  - by Dave

  In a prior blog entry I noted that Solaris used a "maximum dispersal" placement policy to assign nascent threads to their initial processors. The general idea is that threads should be placed as far away from each other as possible in the resource topology in order to reduce resource contention between concurrently running threads. This policy assumes that resource contention -- pipelines, memory channel contention, destructive interference in the shared caches, etc -- will likely outweigh (a) any potential communication benefits we might achieve by packing our threads more densely onto a subset of the NUMA nodes, and (b) benefits of NUMA affinity between memory allocated by one thread and accessed by other threads. We want our threads spread widely over the system and not packed together. Conceptually, when placing a new thread, the kernel picks the least loaded node NUMA node (the node with lowest aggregate load average), and then the least loaded core on that node, etc. Furthermore, the kernel places threads onto resources -- sockets, cores, pipelines, etc -- without regard to the thread's process membership. That is, initial placement is process-agnostic. Keep reading, though. This description is incorrect. On Solaris 10 on a SPARC T5440 with 4 x T2+ NUMA nodes, if the system is otherwise unloaded and we launch a process that creates 20 compute-bound concurrent threads, then typically we'll see a perfect balance with 5 threads on each node. We see similar behavior on an 8-node x86 x4800 system, where each node has 8 cores and each core is 2-way hyperthreaded. So far so good; this behavior seems in agreement with the policy I described in the 1st paragraph. I recently tried the same experiment on a 4-node T4-4 running Solaris 11. Both the T5440 and T4-4 are 4-node systems that expose 256 logical thread contexts. To my surprise, all 20 threads were placed onto just one NUMA node while the other 3 nodes remained completely idle. I checked the usual suspects such as processor sets inadvertently left around by colleagues, processors left offline, and power management policies, but the system was configured normally. I then launched multiple concurrent instances of the process, and, interestingly, all the threads from the 1st process landed on one node, all the threads from the 2nd process landed on another node, and so on. This happened even if I interleaved thread creating between the processes, so I was relatively sure the effect didn't related to thread creation time, but rather that placement was a function of process membership. I this point I consulted the Solaris sources and talked with folks in the Solaris group. The new Solaris 11 behavior is intentional. The kernel is no longer using a simple maximum dispersal policy, and thread placement is process membership-aware. Now, even if other nodes are completely unloaded, the kernel will still try to pack new threads onto the home lgroup (socket) of the primordial thread until the load average of that node reaches 50%, after which it will pick the next least loaded node as the process's new favorite node for placement. On the T4-4 we have 64 logical thread contexts (strands) per socket (lgroup), so if we launch 48 concurrent threads we will find 32 placed on one node and 16 on some other node. If we launch 64 threads we'll find 32 and 32. That means we can end up with our threads clustered on a small subset of the nodes in a way that's quite different that what we've seen on Solaris 10. So we have a policy that allows process-aware packing but reverts to spreading threads onto other nodes if a node becomes too saturated. It turns out this policy was enabled in Solaris 10, but certain bugs suppressed the mixed packing/spreading behavior. There are configuration variables in /etc/system that allow us to dial the affinity between nascent threads and their primordial thread up and down: see lgrp_expand_proc_thresh, specifically. In the OpenSolaris source code the key routine is mpo_update_tunables(). This method reads the /etc/system variables and sets up some global variables that will subsequently be used by the dispatcher, which calls lgrp_choose() in lgrp.c to place nascent threads. Lgrp_expand_proc_thresh controls how loaded an lgroup must be before we'll consider homing a process's threads to another lgroup. Tune this value lower to have it spread your process's threads out more. To recap, the 'new' policy is as follows. Threads from the same process are packed onto a subset of the strands of a socket (50% for T-series). Once that socket reaches the 50% threshold the kernel then picks another preferred socket for that process. Threads from unrelated processes are spread across sockets. More precisely, different processes may have different preferred sockets (lgroups). Beware that I've simplified and elided details for the purposes of explication. The truth is in the code. Remarks: It's worth noting that initial thread placement is just that. If there's a gross imbalance between the load on different nodes then the kernel will migrate threads to achieve a better and more even distribution over the set of available nodes. Once a thread runs and gains some affinity for a node, however, it becomes "stickier" under the assumption that the thread has residual cache residency on that node, and that memory allocated by that thread resides on that node given the default "first-touch" page-level NUMA allocation policy. Exactly how the various policies interact and which have precedence under what circumstances could the topic of a future blog entry. The scheduler is work-conserving. The x4800 mentioned above is an interesting system. Each of the 8 sockets houses an Intel 7500-series processor. Each processor has 3 coherent QPI links and the system is arranged as a glueless 8-socket twisted ladder "mobius" topology. Nodes are either 1 or 2 hops distant over the QPI links. As an aside the mapping of logical CPUIDs to physical resources is rather interesting on Solaris/x4800. On SPARC/Solaris the CPUID layout is strictly geographic, with the highest order bits identifying the socket, the next lower bits identifying the core within that socket, following by the pipeline (if present) and finally the logical thread context ("strand") on the core. But on Solaris on the x4800 the CPUID layout is as follows. [6:6] identifies the hyperthread on a core; bits [5:3] identify the socket, or package in Intel terminology; bits [2:0] identify the core within a socket. Such low-level details should be of interest only if you're binding threads -- a bad idea, the kernel typically handles placement best -- or if you're writing NUMA-aware code that's aware of the ambient placement and makes decisions accordingly. Solaris introduced the so-called critical-threads mechanism, which is expressed by putting a thread into the FX scheduling class at priority 60. The critical-threads mechanism applies to placement on cores, not on sockets, however. That is, it's an intra-socket policy, not an inter-socket policy. Solaris 11 introduces the Power Aware Dispatcher (PAD) which packs threads instead of spreading them out in an attempt to be able to keep sockets or cores at lower power levels. Maximum dispersal may be good for performance but is anathema to power management. PAD is off by default, but power management polices constitute yet another confounding factor with respect to scheduling and dispatching. If your threads communicate heavily -- one thread reads cache lines last written by some other thread -- then the new dense packing policy may improve performance by reducing traffic on the coherent interconnect. On the other hand if your threads in your process communicate rarely, then it's possible the new packing policy might result on contention on shared computing resources. Unfortunately there's no simple litmus test that says whether packing or spreading is optimal in a given situation. The answer varies by system load, application, number of threads, and platform hardware characteristics. Currently we don't have the necessary tools and sensoria to decide at runtime, so we're reduced to an empirical approach where we run trials and try to decide on a placement policy. The situation is quite frustrating. Relatedly, it's often hard to determine just the right level of concurrency to optimize throughput. (Understanding constructive vs destructive interference in the shared caches would be a good start. We could augment the lines with a small tag field indicating which strand last installed or accessed a line. Given that, we could augment the CPU with performance counters for misses where a thread evicts a line it installed vs misses where a thread displaces a line installed by some other thread.)

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• Victor Grazi, Java Champion!

  - by Tori Wieldt

  Congratulations to Victor Grazi, who has been made a Java Champion! He was nominated by his peers and selected as a Java Champion for his experience as a developer, and his work in the Java and Open Source communities. Grazi is a Java evangelist and serves on the Executive Committee of the Java Community Process, representing Credit Suisse - the first non-technology vendor on the JCP. He also arranges the NY Java SIG meetings at Credit Suisse's New York campus each month, and he says it has been a valuable networking opportunity. He also is the spec lead for JSR 354, the Java Money and Currency API. Grazi has been building real time financial systems in Java since JDK version 1.02! In 1996, the internet was just starting to happen, Grazi started a dot com called Supermarkets to Go, that provided an on-line shopping presence to supermarkets and grocers. Grazi wrote most of the code, which was a great opportunity for him to learn Java and UI development, as well as database management. Next, he went to work at Bank of NY building a trading system. He studied for Java certification, and he noted that getting his certification was a game changer because it helped him started to learn the nuances of the Java language. He has held other development positions, "You may have noticed that you don't get as much junk mail from Citibank as you used to - that is thanks to one of my projects!" he told us. Grazi joined Credit Suisse in 2005 and is currently Vice President on the central architecture team. Grazi is proud of his open source project, Java Concurrent Animated, a series of animations that visualize the functionality of the components in the java.util.concurrent library. "It has afforded me the opportunity to speak around the globe" and because of it, has discovered that he really enjoys doing public presentations. He is a fine addition to the Java Champions program. The Java Champions are an exclusive group of passionate Java technology and community leaders who are community-nominated and selected under a project sponsored by Oracle. Nominees are named and selected through a peer review process. Java Champions get the opportunity to provide feedback, ideas, and direction that will help Oracle grow the Java Platform. This interchange may be in the form of technical discussions and/or community-building activities with Oracle's Java Development and Developer Program teams.

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• ATG Live Webcast March 29: Diagnosing E-Business Suite JVM and Forms Performance Issues (Performance Series Part 4 of 4)

  - by BillSawyer

  The next webcast in our popular EBS series on performance management is going to be a showstopper.  Dave Suri, Project Lead, Applications Performance and Gustavo Jimenez, Senior Development Manager will discuss some of the steps involved in triaging and diagnosing E-Business Suite systems related to JVM and Forms components. Please join us for our next ATG Live Webcast on Mar. 29, 2012: Triage and Diagnostics for E-Business Suite JVM and Forms The topics covered in this webcast will be: Overall Menu/Sections Architecture Patches/Certified browsers/jdk versions JVM Tuning JVM Tools (jstat,eclipse mat, ibm tda) Forms Tools (strace/FRD) Java Concurrent Program options location Case studies Case Studies JVM Thread dump case for Oracle Advanced Product Catalog Forms FRD trace relating to Saving an SR Java Concurrent Program for BT Date:               Thursday, March 29, 2012Time:              8:00 AM - 9:00 AM Pacific Standard TimePresenters:  Dave Suri, Project Lead, Applications Performance                        Gustavo Jimenez, Senior Development ManagerWebcast Registration Link (Preregistration is optional but encouraged)To hear the audio feed:   Domestic Participant Dial-In Number:            877-697-8128    International Participant Dial-In Number:      706-634-9568    Additional International Dial-In Numbers Link:    Dial-In Passcode:                                              99342To see the presentation:    The Direct Access Web Conference details are:    Website URL: https://ouweb.webex.com    Meeting Number:  597073984 If you miss the webcast, or you have missed any webcast, don't worry -- we'll post links to the recording as soon as it's available from Oracle University.  You can monitor this blog for pointers to the replay. And, you can find our archive of our past webcasts and training here.If you have any questions or comments, feel free to email Bill Sawyer (Senior Manager, Applications Technology Curriculum) at BilldotSawyer-AT-Oracle-DOT-com. 

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• Tips On Using The Service Contracts Import Program

  - by LuciaC

  Prior to release 12.1 there was no supported way to import contracts into the EBS Service Contracts application - there were no public APIs nor contract load programs provided.  From release 12.1 onwards the 'Service Contracts Import Program' is provided to load service contracts into the application. The Service Contracts Import functionality is explained in How to Use the Service Contracts Import Program - Scope and Limitations (Doc ID 1057242.1).  This note includes an attached document which explains the program architecture, shows the Entity Relationship Diagram and details the interface table definitions. The Import program takes data from the interface tables listed below and populates the contracts schema tables:  OKS_USAGE_COUNTERS_INTERFACE OKS_SALES_CREDITS_INTERFACEOKS_NOTES_INTERFACEOKS_LINES_INTERFACEOKS_HEADERS_INTERFACEOKS_COVERED_LEVELS_INTERFACEThese interface tables must be loaded via a custom load program.The Service Contracts Import concurrent request is then submitted to create contracts from this legacy data. The parameters to run the Import program are:  Parameter Description  Mode Validate only, Import  Batch Number Batch_Id (unique id populated into the OKS_HEADERS_INTERFACE table)  Number of Workers Number of workers required (these are spawned as separate sub-requests)  Commit size Represents number of successfully processed contracts commited to database The program spawns sub-requests for the import worker(s) and the 'Service Contracts Import Report'.  The data is validated prior to import and into the Contracts tables and will report errors in the Service Contracts Import Report program output file (Import Execution Report).  Troubleshooting tips are provided in R12.1 - Common Service Contract Import Errors (Doc ID 762545.1); this document lists some, but not all, import errors.  The document will be updated over time.  Additional help is given in Debugging Tip for Service Contracts Import Errors (Doc ID 971426.1).After you successfully import contracts, you can purge the records from the interface tables by running the Service Contracts Import Purge concurrent program. Note that there is no supported way to mass delete data from the Contracts schema tables once they are populated, so data loaded by the Import program must be fully tested and verified before the program is run to load data into a Production system.A Service Contracts Import Test program has been provided which will take an existing contract in the application and load the interface tables using the data from that contract.  This can be used as an example for guidance on how to load the interface tables.  The Test program functionality is explained in How to Use the Service Contracts Test Import Program Provided in Release 12.1 (Doc ID 761209.1).  Note that the Test program has some limitations which do not apply to the full Import program and is not a supported program, it is simply a testing tool.  

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• L'alternative d'Apple au Flash s'appelle Gianduia, écrite en JavaScript elle s'appuierait sur Cocoa

  Mise à jour du 10/05/10 L'alternative d'Apple au Flash s'appelle Gianduia Elle est écrite en JavaScript Critiquer c'est bien. Proposer c'est mieux. C'est ce que Apple serait sur le point de faire avec sa propre solution pour remplacer Flash (et par la même occasion Silverlight, le concurrent de chez Microsoft). Baptisée Gianduia, cette technologie RIA aurait déjà été testée par Apple dans plusieurs de ses services de distribution comme le programme One-to-One, (formation individuelle dans les magasins de la marque), le système de réservation de l'iPhone ou les applications des Concierges (ses vendeurs spécialisés).

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• Is there any way to test how will the site perform under load

  - by Pankaj Upadhyay

  I have made an Asp.net MVC website and hosted it on a shared hosting provider. Since my website surrounds a very generic idea, it might have number of concurrent users sometime in future. So, I was thinking of a way to test my website for on-load performance. Like how will the site perform when 100 or 1000 users are online at the same time and surfing the website. This will also make me understand whether my LINQ queries are well written or not.

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• Internet Explorer 9 ne soutiendra que le H.264 : vers un nouveau coup dur pour Flash ?

  Mise à jour du 30/04/10 Internet Explorer 9 ne supportera que le H.264 Vers un nouveau coup dur pour Flash ? Microsoft vient de réitérer son implication dans la future norme du HTML 5. « Le futur du Web c'est le HTML5 », a même écrit hier sur son blog le General Manager d'Internet Explorer, qui explique que « la spécification HTML 5 permet de décrire le support d'une vidéo sans spécifier un format particulier ». Jusqu'ici, rien de très nouveau, même si cette implication pose la question de son articulation avec Silverlight, le concurrent maison de Flash (lire par ailleurs :

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• Basic Defensive Database Programming Techniques

  We can all recognise good-quality database code: It doesn't break with every change in the server's configuration, or on upgrade. It isn't affected by concurrent usage, or high workload. In an extract from his forthcoming book, Alex explains just how to go about producing resilient TSQL code that works, and carries on working.

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• If immutable objects are good, why do people keep creating mutable objects?

  - by Vinoth Kumar

  If immutable objects are good,simple and offers benefits in concurrent programming why do programmers keep creating mutable objects? I have four years of experience in Java programming and as I see it, the first thing people do after creating a class is generate getters and setters in the IDE (thus making it mutable). Is there a lack of awareness or can we get away with using mutable objects in most scenarios?

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• Basic Defensive Database Programming Techniques

  We can all recognize good-quality database code: It doesn't break with every change in the server's configuration, or on upgrade. It isn't affected by concurrent usage, or high workload. In an extract from his forthcoming book, Alex explains just how to go about producing resilient TSQL code that works, and carries on working.

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• L'ancien cadre de Microsoft interdit d'embauche chez Salesforce par la justice, après le vol de données confidentielles

  L'ancien cadre de Microsoft interdit d'embauche chez Salesforce Par la justice, suite au vol de données confidentielles du CRM de Microsoft Mise à jour du 24/02/11, par Hinault Romaric Matt Miszewski, ancien directeur de la division des activités grands comptes de Microsoft, ne pourra pas occuper un poste similaire chez Saleforces.com, concurrent de Microsoft dans le domaine des CRM en mode Cloud Computing. C'est ce qui ressort de la décision de justice après l'étude de la requête déposée par Microsoft auprès de la cour de Washington. Pour mémoire Microsoft accusait Miszewski d'avoir emporté avec lui 600 Mo de données confidenti...

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• Level of detail algorithm not functioning correctly

  - by Darestium

  I have been working on this problem for months; I have been creating Planet Generator of sorts, after more than 6 months of work I am no closer to finishing it then I was 4 months ago. My problem; The terrain does not subdivide in the correct locations properly, it almost seems as if there is a ghost camera next to me, and the quads subdivide based on the position of this "ghost camera". Here is a video of the broken program: http://www.youtube.com/watch?v=NF_pHeMOju8 The best example of the problem occurs around 0:36. For detail limiting, I am going for a chunked LOD approach, which subdivides the terrain based on how far you are away from it. I use a "depth table" to determine how many subdivisions should take place. void PQuad::construct_depth_table(float distance) { tree[0] = -1; for (int i = 1; i < MAX_DEPTH; i++) { tree[i] = distance; distance /= 2.0f; } } The chuncked LOD relies on the child/parent structure of quads, the depth is determined by a constant e.g: if the constant is 6, there are six levels of detail. The quads which should be drawn go through a distance test from the player to the centre of the quad. void PQuad::get_recursive(glm::vec3 player_pos, std::vector<PQuad*>& out_children) { for (size_t i = 0; i < children.size(); i++) { children[i].get_recursive(player_pos, out_children); } if (this->should_draw(player_pos) || this->depth == 0) { out_children.emplace_back(this); } } bool PQuad::should_draw(glm::vec3 player_position) { float distance = distance3(player_position, centre); if (distance < tree[depth]) { return true; } return false; } The root quad has four children which could be visualized like the following: [] [] [] [] Where each [] is a child. Each child has the same amount of children up until the detail limit, the quads which have are 6 iterations deep are leaf nodes, these nodes have no children. Each node has a corresponding Mesh, each Mesh structure has 16x16 Quad-shapes, each Mesh's Quad-shapes halves in size each detail level deeper - creating more detail. void PQuad::construct_children() { // Calculate the position of the Quad based on the parent's location calculate_position(); if (depth < (int)MAX_DEPTH) { children.reserve((int)NUM_OF_CHILDREN); for (int i = 0; i < (int)NUM_OF_CHILDREN; i++) { children.emplace_back(PQuad(this->face_direction, this->radius)); PQuad *child = &children.back(); child->set_depth(depth + 1); child->set_child_index(i); child->set_parent(this); child->construct_children(); } } else { leaf = true; } } The following function creates the vertices for each quad, I feel that it may play a role in the problem - I just can't determine what is causing the problem. void PQuad::construct_vertices(std::vector<glm::vec3> *vertices, std::vector<Color3> *colors) { vertices->reserve(quad_width * quad_height); for (int y = 0; y < quad_height; y++) { for (int x = 0; x < quad_width; x++) { switch (face_direction) { case YIncreasing: vertices->emplace_back(glm::vec3(position.x + x * element_width, quad_height - 1.0f, -(position.y + y * element_width))); break; case YDecreasing: vertices->emplace_back(glm::vec3(position.x + x * element_width, 0.0f, -(position.y + y * element_width))); break; case XIncreasing: vertices->emplace_back(glm::vec3(quad_width - 1.0f, position.y + y * element_width, -(position.x + x * element_width))); break; case XDecreasing: vertices->emplace_back(glm::vec3(0.0f, position.y + y * element_width, -(position.x + x * element_width))); break; case ZIncreasing: vertices->emplace_back(glm::vec3(position.x + x * element_width, position.y + y * element_width, 0.0f)); break; case ZDecreasing: vertices->emplace_back(glm::vec3(position.x + x * element_width, position.y + y * element_width, -(quad_width - 1.0f))); break; } // Position the bottom, right, front vertex of the cube from being (0,0,0) to (-16, -16, 16) (*vertices)[vertices->size() - 1] -= glm::vec3(quad_width / 2.0f, quad_width / 2.0f, -(quad_width / 2.0f)); colors->emplace_back(Color3(255.0f, 255.0f, 255.0f, false)); } } switch (face_direction) { case YIncreasing: this->centre = glm::vec3(position.x + quad_width / 2.0f, quad_height - 1.0f, -(position.y + quad_height / 2.0f)); break; case YDecreasing: this->centre = glm::vec3(position.x + quad_width / 2.0f, 0.0f, -(position.y + quad_height / 2.0f)); break; case XIncreasing: this->centre = glm::vec3(quad_width - 1.0f, position.y + quad_height / 2.0f, -(position.x + quad_width / 2.0f)); break; case XDecreasing: this->centre = glm::vec3(0.0f, position.y + quad_height / 2.0f, -(position.x + quad_width / 2.0f)); break; case ZIncreasing: this->centre = glm::vec3(position.x + quad_width / 2.0f, position.y + quad_height / 2.0f, 0.0f); break; case ZDecreasing: this->centre = glm::vec3(position.x + quad_width / 2.0f, position.y + quad_height / 2.0f, -(quad_height - 1.0f)); break; } this->centre -= glm::vec3(quad_width / 2.0f, quad_width / 2.0f, -(quad_width / 2.0f)); } Any help in discovering what is causing this "subdivding in the wrong place" would be greatly appreciated.

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• Next Post...

  - by James Michael Hare

  The next post on the concurrent collections will be next Monday.  I'm a little behind from my Topeka trip earlier this week, so sorry about the delay! Also, I was thinking about starting a C++ Little Wonders series as well.  Would anyone have an interest in that topic?  I primarily use C# in my development work, but there is still a lot of legacy C++ I work on as well and could share some tips & tricks.

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• WebM : la justice américaine enquête sur le groupe MPEG-LA et des actions potentiellement anticoncurrentielles contre le format de Google

  WebM : la justice américaine enquête sur le groupe MPEG-LA Et des actions potentiellement anticoncurrentielles contre le format de Google Mise à jour du 07/03/2011 par Idelways D'après un rapport rendu public par le Wall Street Journal, le département de la justice américaine aurait lancé une enquête antitrust sur le groupe de gestion de brevets MPEG-LA, le soupçonnant de vouloir attenter injustement à un rival technologique open-source supporté par Google (VP8). Le groupe MPEG-LA avait lancé mi-février un appel à tous les industriels qui estiment détenir des brevets potentiellement utilisés par le codec concurrent « VP...

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• Why is multithreading often preferred for improving performance?

  - by user1849534

  I have a question, it's about why programmers seems to love concurrency and multi-threaded programs in general. I'm considering 2 main approaches here: an async approach basically based on signals, or just an async approach as called by many papers and languages like the new C# 5.0 for example, and a "companion thread" that manages the policy of your pipeline a concurrent approach or multi-threading approach I will just say that I'm thinking about the hardware here and the worst case scenario, and I have tested this 2 paradigms myself, the async paradigm is a winner at the point that I don't get why people 90% of the time talk about multi-threading when they want to speed up things or make a good use of their resources. I have tested multi-threaded programs and async program on an old machine with an Intel quad-core that doesn't offer a memory controller inside the CPU, the memory is managed entirely by the motherboard, well in this case performances are horrible with a multi-threaded application, even a relatively low number of threads like 3-4-5 can be a problem, the application is unresponsive and is just slow and unpleasant. A good async approach is, on the other hand, probably not faster but it's not worst either, my application just waits for the result and doesn't hangs, it's responsive and there is a much better scaling going on. I have also discovered that a context change in the threading world it's not that cheap in real world scenario, it's in fact quite expensive especially when you have more than 2 threads that need to cycle and swap among each other to be computed. On modern CPUs the situation it's not really that different, the memory controller it's integrated but my point is that an x86 CPUs is basically a serial machine and the memory controller works the same way as with the old machine with an external memory controller on the motherboard. The context switch is still a relevant cost in my application and the fact that the memory controller it's integrated or that the newer CPU have more than 2 core it's not bargain for me. For what i have experienced the concurrent approach is good in theory but not that good in practice, with the memory model imposed by the hardware, it's hard to make a good use of this paradigm, also it introduces a lot of issues ranging from the use of my data structures to the join of multiple threads. Also both paradigms do not offer any security abut when the task or the job will be done in a certain point in time, making them really similar from a functional point of view. According to the X86 memory model, why the majority of people suggest to use concurrency with C++ and not just an async approach ? Also why not considering the worst case scenario of a computer where the context switch is probably more expensive than the computation itself ?

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• Oracle E-Business Suite Tip : SQL Tracing

  - by Giri Mandalika

  Issue: Attempts to enable SQL tracing from concurrent request form fails with error: Function not available to this responsibility. Change Responsibilities or contact your System Administrator Resolution: Switch responsibility to "System Administrator". Navigate to System - Profiles, and query for "%Diagnostics% ("Utilities : Diagnostics")". Once found the profile, change its value to "Yes". Restart web browser and try enabling SQL trace again.

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• How can I limit the number of open AF_INET sockets?

  - by Stefano Palazzo

  Is there a way to limit the number of concurretly open AF_INET sockets (only)? If so, how do I do it, and how will the networking behave if I'm above the limit? For background: My cheap commodity router is a bit eager to detect 'syn flooding'. When it does, it crashes (and doesn't automatically restart itself). I'm thinking limiting concurrent connections to around 1000 should keep it from bickering.

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• Google Maps, baisse des prix de l'API : pour contrer la montée en puissance d'OpenStreetMap ?

  Google Maps : baisse des prix de l'API Pour contrer la montée en puissance de OpenStreetMap ? Mise à jour du 25/06/12 Depuis que Google a décidé de rendre payant l'API de son service de cartographie, plusieurs migrations ont été constatées vers le service concurrent ? et open-source ? OpenStreetMap : Foursquare, Apple et Wikipedia en tête. Même si seuls 0.35 % des sites qui utilisent les Google Maps dépassent les seuils de l'usage gratuit et même si ces trois exemples ne sont pas exclusivement liés à ce passage au ...

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• WebP se dote d'un mode de compression d'images sans perte, le format open source de Google veut aussi concurrencer le PNG

  WebP se dote d'un mode de compression d'images sans perte Le format open source de Google veut aussi concurrencer le PNG Mise à jour du 21 novembre 2011 Google voit grand pour son format d'image WebP et veut manifestement en faire un format à tout faire. Positionné au départ (lire ci-devant) comme un concurrent plus optimisé que le JPEG, avec en prime une couche alpha progressive (de transparence), il se dote aujourd'hui de capacités d'optimisation non destructives des images, à l'instar du PNG. Le nouveau mode lossless (sans perte) allierait densité de compression et facilité de décodage d'après un billet...

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