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  • JavaScript distributed computing project

    - by Ben L.
    I made a website that does absolutely nothing, and I've proven to myself that people like to stay there - I've already logged 11+ hours worth of cumulative time on the page. My question is whether it would be possible (or practical) to use the website as a distributed computing site. My first impulse was to find out if there were any JavaScript distributed computing projects already active, so that I could put a piece of code on the page and be done. Unfortunately, all I could find was a big list of websites that thought it might be a cool idea. I'm thinking that I might want to start with something like integer factorization - in this case, RSA numbers. It would be easy for the server to check if an answer was correct (simply test for modulus equals zero), and also easy to implement. Is my idea feasible? Is there already a project out there that I can use?

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  • Why Ultra-Low Power Computing Will Change Everything

    - by Tori Wieldt
    The ARM TechCon keynote "Why Ultra-Low Power Computing Will Change Everything" was anything but low-powered. The speaker, Dr. Johnathan Koomey, knows his subject: he is a Consulting Professor at Stanford University, worked for more than two decades at Lawrence Berkeley National Laboratory, and has been a visiting professor at Stanford University, Yale University, and UC Berkeley's Energy and Resources Group. His current focus is creating a standard (computations per kilowatt hour) and measuring computer energy consumption over time. The trends are impressive: energy consumption has halved every 1.5 years for the last 60 years. Battery life has made roughly a 10x improvement each decade since 1960. It's these improvements that have made laptops and cell phones possible. What does the future hold? Dr. Koomey said that in the past, the race by chip manufacturers was to create the fastest computer, but the priorities have now changed. New computers are tiny, smart, connected and cheap. "You can't underestimate the importance of a shift in industry focus from raw performance to power efficiency for mobile devices," he said. There is also a confluence of trends in computing, communications, sensors, and controls. The challenge is how to reduce the power requirements for these tiny devices. Alternate sources of power that are being explored are light, heat, motion, and even blood sugar. The University of Michigan has produced a miniature sensor that harnesses solar energy and could last for years without needing to be replaced. Also, the University of Washington has created a sensor that scavenges power from existing radio and TV signals.Specific devices designed for a purpose are much more efficient than general purpose computers. With all these sensors, instead of big data, developers should focus on nano-data, personalized information that will adjust the lights in a room, a machine, a variable sign, etc.Dr. Koomey showed some examples:The Proteus Digital Health Feedback System, an ingestible sensor that transmits when a patient has taken their medicine and is powered by their stomach juices. (Gives "powered by you" a whole new meaning!) Streetline Parking Systems, that provide real-time data about available parking spaces. The information can be sent to your phone or update parking signs around the city to point to areas with available spaces. Less driving around looking for parking spaces!The BigBelly trash system that uses solar power, compacts trash, and sends a text message when it is full. This dramatically reduces the number of times a truck has to come to pick up trash, freeing up resources and slashing fuel costs. This is a classic example of the efficiency of moving "bits not atoms." But researchers are approaching the physical limits of sensors, Dr. Kommey explained. With the current rate of technology improvement, they'll reach the three-atom transistor by 2041. Once they hit that wall, it will force a revolution they way we do computing. But wait, researchers at Purdue University and the University of New South Wales are both working on a reliable one-atom transistors! Other researchers are working on "approximate computing" that will reduce computing requirements drastically. So it's unclear where the wall actually is. In the meantime, as Dr. Koomey promised, ultra-low power computing will change everything.

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  • Commercial uses for grid computing?

    - by paxdiablo
    I keep hearing from associates about grid computing which, from what I can gather, is highly distributed stuff along the lines of SETI@Home. Is anyone working on these sort of systems for business use? My interest is in figuring out if there's a commercial reason for starting software development in this field.

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  • Suggestions for open source testing tool for cloud computing

    - by vikraman
    Hi, I want to know if there is any open source testing tool for cloud computing. We have built a cloud framework with Xen, Eucalyptus, Hadoop, HBase as different layers. I am not looking at testing each of these tools separately, but i want to test them from the perspective of fitting into a cloud environment (for example scalability of xen hypervisor to handle multiple VMs). Would be great if you can suggest me some tool (open source) for the above.

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  • Extreme Optimization – Numerical Algorithm Support

    - by JoshReuben
    Function Delegates Many calculations involve the repeated evaluation of one or more user-supplied functions eg Numerical integration. The EO MathLib provides delegate types for common function signatures and the FunctionFactory class can generate new delegates from existing ones. RealFunction delegate - takes one Double parameter – can encapsulate most of the static methods of the System.Math class, as well as the classes in the Extreme.Mathematics.SpecialFunctions namespace: var sin = new RealFunction(Math.Sin); var result = sin(1); BivariateRealFunction delegate - takes two Double parameters: var atan2 = new BivariateRealFunction (Math.Atan2); var result = atan2(1, 2); TrivariateRealFunction delegate – represents a function takes three Double arguments ParameterizedRealFunction delegate - represents a function taking one Integer and one Double argument that returns a real number. The Pow method implements such a function, but the arguments need order re-arrangement: static double Power(int exponent, double x) { return ElementaryFunctions.Pow(x, exponent); } ... var power = new ParameterizedRealFunction(Power); var result = power(6, 3.2); A ComplexFunction delegate - represents a function that takes an Extreme.Mathematics.DoubleComplex argument and also returns a complex number. MultivariateRealFunction delegate - represents a function that takes an Extreme.Mathematics.LinearAlgebra.Vector argument and returns a real number. MultivariateVectorFunction delegate - represents a function that takes a Vector argument and returns a Vector. FastMultivariateVectorFunction delegate - represents a function that takes an input Vector argument and an output Matrix argument – avoiding object construction  The FunctionFactory class RealFromBivariateRealFunction and RealFromParameterizedRealFunction helper methods - transform BivariateRealFunction or a ParameterizedRealFunction into a RealFunction delegate by fixing one of the arguments, and treating this as a new function of a single argument. var tenthPower = FunctionFactory.RealFromParameterizedRealFunction(power, 10); var result = tenthPower(x); Note: There is no direct way to do this programmatically in C# - in F# you have partial value functions where you supply a subset of the arguments (as a travelling closure) that the function expects. When you omit arguments, F# generates a new function that holds onto/remembers the arguments you passed in and "waits" for the other parameters to be supplied. let sumVals x y = x + y     let sumX = sumVals 10     // Note: no 2nd param supplied.     // sumX is a new function generated from partially applied sumVals.     // ie "sumX is a partial application of sumVals." let sum = sumX 20     // Invokes sumX, passing in expected int (parameter y from original)  val sumVals : int -> int -> int val sumX : (int -> int) val sum : int = 30 RealFunctionsToVectorFunction and RealFunctionsToFastVectorFunction helper methods - combines an array of delegates returning a real number or a vector into vector or matrix functions. The resulting vector function returns a vector whose components are the function values of the delegates in the array. var funcVector = FunctionFactory.RealFunctionsToVectorFunction(     new MultivariateRealFunction(myFunc1),     new MultivariateRealFunction(myFunc2));  The IterativeAlgorithm<T> abstract base class Iterative algorithms are common in numerical computing - a method is executed repeatedly until a certain condition is reached, approximating the result of a calculation with increasing accuracy until a certain threshold is reached. If the desired accuracy is achieved, the algorithm is said to converge. This base class is derived by many classes in the Extreme.Mathematics.EquationSolvers and Extreme.Mathematics.Optimization namespaces, as well as the ManagedIterativeAlgorithm class which contains a driver method that manages the iteration process.  The ConvergenceTest abstract base class This class is used to specify algorithm Termination , convergence and results - calculates an estimate for the error, and signals termination of the algorithm when the error is below a specified tolerance. Termination Criteria - specify the success condition as the difference between some quantity and its actual value is within a certain tolerance – 2 ways: absolute error - difference between the result and the actual value. relative error is the difference between the result and the actual value relative to the size of the result. Tolerance property - specify trade-off between accuracy and execution time. The lower the tolerance, the longer it will take for the algorithm to obtain a result within that tolerance. Most algorithms in the EO NumLib have a default value of MachineConstants.SqrtEpsilon - gives slightly less than 8 digits of accuracy. ConvergenceCriterion property - specify under what condition the algorithm is assumed to converge. Using the ConvergenceCriterion enum: WithinAbsoluteTolerance / WithinRelativeTolerance / WithinAnyTolerance / NumberOfIterations Active property - selectively ignore certain convergence tests Error property - returns the estimated error after a run MaxIterations / MaxEvaluations properties - Other Termination Criteria - If the algorithm cannot achieve the desired accuracy, the algorithm still has to end – according to an absolute boundary. Status property - indicates how the algorithm terminated - the AlgorithmStatus enum values:NoResult / Busy / Converged (ended normally - The desired accuracy has been achieved) / IterationLimitExceeded / EvaluationLimitExceeded / RoundOffError / BadFunction / Divergent / ConvergedToFalseSolution. After the iteration terminates, the Status should be inspected to verify that the algorithm terminated normally. Alternatively, you can set the ThrowExceptionOnFailure to true. Result property - returns the result of the algorithm. This property contains the best available estimate, even if the desired accuracy was not obtained. IterationsNeeded / EvaluationsNeeded properties - returns the number of iterations required to obtain the result, number of function evaluations.  Concrete Types of Convergence Test classes SimpleConvergenceTest class - test if a value is close to zero or very small compared to another value. VectorConvergenceTest class - test convergence of vectors. This class has two additional properties. The Norm property specifies which norm is to be used when calculating the size of the vector - the VectorConvergenceNorm enum values: EuclidianNorm / Maximum / SumOfAbsoluteValues. The ErrorMeasure property specifies how the error is to be measured – VectorConvergenceErrorMeasure enum values: Norm / Componentwise ConvergenceTestCollection class - represent a combination of tests. The Quantifier property is a ConvergenceTestQuantifier enum that specifies how the tests in the collection are to be combined: Any / All  The AlgorithmHelper Class inherits from IterativeAlgorithm<T> and exposes two methods for convergence testing. IsValueWithinTolerance<T> method - determines whether a value is close to another value to within an algorithm's requested tolerance. IsIntervalWithinTolerance<T> method - determines whether an interval is within an algorithm's requested tolerance.

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  • Perl numerical sorting: how to ignore leading alpha character [migrated]

    - by Luke Sheppard
    I have a 1,660 row array like this: ... H00504 H00085 H00181 H00500 H00103 H00007 H00890 H08793 H94316 H00217 ... And the leading character never changes. It is always "H" then five digits. But when I do what I believe is a numerical sort in Perl, I'm getting strange results. Some segments are sorted in order, but then a different segment starts up. Here is a segment after sorting: ... H01578 H01579 H01580 H01581 H01582 H01583 H01584 H00536 H00537 H00538 H01585 H01586 H01587 H01588 H01589 H01590 ... What I'm trying is this: my @sorted_array = sort {$a <=> $b} @raw_array; But obviously it is not working. Anyone know why?

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  • Opportunities in Cloud Computing

    - by Paul Sorensen
    A recent article from CIO Journal indicates that there is an extreme labor shortage (in certain technology areas) that is is leading to upward pressure on wages for IT Workers. This represents a great opportunity for those with certain skill-sets, among which include Java (Oracle certification is mentioned specifically). The article points out that a key driver of the labor shortage is the expansion of cloud computing. Cloud computing is set up to make life extremely simple for end-users, but the model pushes the complexity to back-end systems which are sophisticated, enterprise-level computing stacks (Oracle has an extensive set of cloud computing solutions). These complex systems require very highly-skilled IT professionals (the best-of-the-best) to successfully develop, implement, administer and maintain them. What this mean for you is that there is opportunity for those who have the appropriate skills at the appropriate levels. If you want to be a part of this opportunity you should do a self-assessment of your own skill-sets and experience. Based upon your results you can decide where it would be most appropriate to spend your time and resources for the highest return on your investment. By expanding and sharpening your skills and by gaining greater experience you will be better prepared to take advantage of career opportunities (like this) that come along periodically. As you evaluate your needs remember that Oracle University has a tremendous selection of high-quality eduction offerings (including training and certification) that can you help move your career forward. Thanks and best of luck!

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  • F# performance in scientific computing

    - by aaa
    hello. I am curious as to how F# performance compares to C++ performance? I asked a similar question with regards to Java, and the impression I got was that Java is not suitable for heavy numbercrunching. I have read that F# is supposed to be more scalable and more performant, but how is this real-world performance compares to C++? specific questions about current implementation are: How well does it do floating-point? Does it allow vector instructions how friendly is it towards optimizing compilers? How big a memory foot print does it have? Does it allow fine-grained control over memory locality? does it have capacity for distributed memory processors, for example Cray? what features does it have that may be of interest to computational science where heavy number processing is involved? Are there actual scientific computing implementations that use it? Thanks

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  • Managed language for scientific computing software

    - by heisen
    Scientific computing is algorithm intensive and can also be data intensive. It often needs to use a lot of memory to run analysis and release it before continuing with the next. Sometime it also uses memory pool to recycle memory for each analysis. Managed language is interesting here because it can allow the developer to concentrate on the application logic. Since it might need to deal with huge dataset, performance is important too. But how can we control memory and performance with managed language?

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  • Parallel Computing in .Net 4.0

    - by kaleidoscope
    Technorati Tags: Ram,Parallel Computing in .Net 4.0 Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem: To be run using multiple CPUs A problem is broken into discrete parts that can be solved concurrently Each part is further broken down to a series of instructions Instructions from each part execute simultaneously on different CPUs Parallel Extensions in .NET 4.0 provides a set of libraries and tools to achieve the above mentioned objectives. This supports two paradigms of parallel computing Data Parallelism – This refers to dividing the data across multiple processors for parallel execution.e.g we are processing an array of 1000 elements we can distribute the data between two processors say 500 each. This is supported by the Parallel LINQ (PLINQ) in .NET 4.0 Task Parallelism – This breaks down the program into multiple tasks which can be parallelized and are executed on different processors. This is supported by Task Parallel Library (TPL) in .NET 4.0 A high level view is shown below:

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  • Technical Computing

      Today, Microsoft announced our Technical Computing initiative.    Through the Technical Computing initiative, we will enable scientists, engineers and analysts to more easily model the world at much greater fidelity.  The Technical Computing initiative will address a wide range of users.  One of the most critical elements is to help developers create applications that can take advantage of parallelism on their desktop, in a cluster, and in public and private clouds. ...Did you know that DotNetSlackers also publishes .net articles written by top known .net Authors? We already have over 80 articles in several categories including Silverlight. Take a look: here.

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  • Windows Azure Recipe: High Performance Computing

    - by Clint Edmonson
    One of the most attractive ways to use a cloud platform is for parallel processing. Commonly known as high-performance computing (HPC), this approach relies on executing code on many machines at the same time. On Windows Azure, this means running many role instances simultaneously, all working in parallel to solve some problem. Doing this requires some way to schedule applications, which means distributing their work across these instances. To allow this, Windows Azure provides the HPC Scheduler. This service can work with HPC applications built to use the industry-standard Message Passing Interface (MPI). Software that does finite element analysis, such as car crash simulations, is one example of this type of application, and there are many others. The HPC Scheduler can also be used with so-called embarrassingly parallel applications, such as Monte Carlo simulations. Whatever problem is addressed, the value this component provides is the same: It handles the complex problem of scheduling parallel computing work across many Windows Azure worker role instances. Drivers Elastic compute and storage resources Cost avoidance Solution Here’s a sketch of a solution using our Windows Azure HPC SDK: Ingredients Web Role – this hosts a HPC scheduler web portal to allow web based job submission and management. It also exposes an HTTP web service API to allow other tools (including Visual Studio) to post jobs as well. Worker Role – typically multiple worker roles are enlisted, including at least one head node that schedules jobs to be run among the remaining compute nodes. Database – stores state information about the job queue and resource configuration for the solution. Blobs, Tables, Queues, Caching (optional) – many parallel algorithms persist intermediate and/or permanent data as a result of their processing. These fast, highly reliable, parallelizable storage options are all available to all the jobs being processed. Training Here is a link to online Windows Azure training labs where you can learn more about the individual ingredients described above. (Note: The entire Windows Azure Training Kit can also be downloaded for offline use.) Windows Azure HPC Scheduler (3 labs)  The Windows Azure HPC Scheduler includes modules and features that enable you to launch and manage high-performance computing (HPC) applications and other parallel workloads within a Windows Azure service. The scheduler supports parallel computational tasks such as parametric sweeps, Message Passing Interface (MPI) processes, and service-oriented architecture (SOA) requests across your computing resources in Windows Azure. With the Windows Azure HPC Scheduler SDK, developers can create Windows Azure deployments that support scalable, compute-intensive, parallel applications. See my Windows Azure Resource Guide for more guidance on how to get started, including links web portals, training kits, samples, and blogs related to Windows Azure.

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  • With MSDN and BizSpark, Cloud Computing is Closer than You Think

    Cloud computing offers significant advantages for businesses of all sizes, and it's easier to get started than you think. Microsoft makes Windows Azure compute time available for MSDN subscribers, as well as for software start-ups through the Microsoft BizSpark program. Learn why cloud computing is a good fit for you and how you can get started.

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  • With MSDN and BizSpark, Cloud Computing is Closer than You Think

    Cloud computing offers significant advantages for businesses of all sizes, and it's easier to get started than you think. Microsoft makes Windows Azure compute time available for MSDN subscribers, as well as for software start-ups through the Microsoft BizSpark program. Learn why cloud computing is a good fit for you and how you can get started.

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  • Understanding the levels of computing

    - by RParadox
    Sorry, for my confused question. I'm looking for some pointers. Up to now I have been working mostly with Java and Python on the application layer and I have only a vague understanding of operating systems and hardware. I want to understand much more about the lower levels of computing, but it gets really overwhelming somehow. At university I took a class about microprogramming, i.e. how processors get hard-wired to implement the ASM codes. Up to now I always thought I wouldn't get more done if learned more about the "low level". One question I have is: how is it even possible that hardware gets hidden almost completely from the developer? Is it accurate to say that the operating system is a software layer for the hardware? One small example: in programming I have never come across the need to understand what L2 or L3 Cache is. For the typical business application environment one almost never needs to understand assembler and the lower levels of computing, because nowadays there is a technology stack for almost anything. I guess the whole point of these lower levels is to provide an interface to higher levels. On the other hand I wonder how much influence the lower levels can have, for example this whole graphics computing thing. So, on the other hand, there is this theoretical computer science branch, which works on abstract computing models. However, I also rarely encountered situations, where I found it helpful thinking in the categories of complexity models, proof verification, etc. I sort of know, that there is a complexity class called NP, and that they are kind of impossible to solve for a big number of N. What I'm missing is a reference for a framework to think about these things. It seems to me, that there all kinds of different camps, who rarely interact. The last few weeks I have been reading about security issues. Here somehow, much of the different layers come together. Attacks and exploits almost always occur on the lower level, so in this case it is necessary to learn about the details of the OSI layers, the inner workings of an OS, etc.

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  • Error at lapack cgesv when matrix is not singular

    - by Jan Malec
    This is my first post. I usually ask classmates for help, but they have a lot of work now and I'm too desperate to figure this out on my own :). I am working on a project for school and I have come to a point where I need to solve a system of linear equations with complex numbers. I have decided to call lapack routine "cgesv" from c++. I use the c++ complex library to work with complex numbers. Problem is, when I call the routine, I get error code "2". From lapack documentation: INFO is INTEGER = 0: successful exit < 0: if INFO = -i, the i-th argument had an illegal value > 0: if INFO = i, U(i,i) is exactly zero. The factorization has been completed, but the factor U is exactly singular, so the solution could not be computed. Therefore, the element U(2, 2) should be zero, but it is not. This is how I declare the function: void cgesv_( int* N, int* NRHS, std::complex* A, int* lda, int* ipiv, std::complex* B, int* ldb, int* INFO ); This is how I use it: int *IPIV = new int[NA]; int INFO, NRHS = 1; std::complex<double> *aMatrix = new std::complex<double>[NA*NA]; for(int i=0; i<NA; i++){ for(int j=0; j<NA; j++){ aMatrix[j*NA+i] = A[i][j]; } } cgesv_( &NA, &NRHS, aMatrix, &NA, IPIV, B, &NB, &INFO ); And this is how the matrix looks like: (1,-160.85) (0,0.000306796) (0,-0) (0,-0) (0,-0) (0,0.000306796) (1,-40.213) (0,0.000306796) (0,-0) (0,-0) (0,-0) (0,0.000306796) (1,-0.000613592) (0,0.000306796) (0,-0) (0,-0) (0,-0) (0,0.000306796) (1,-40.213) (0,0.000306796) (0,-0) (0,-0) (0,-0) (0,0.000306796) (1,-160.85) I had to split the matrix colums, otherwise it did not format correctly. My first suspicion was that complex is not parsed correctly, but I have used lapack functions with complex numbers before this way. Any ideas?

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  • matlab fit exp2

    - by HelloWorld
    I'm unsuccessfully looking for documentation of fit function using exp2 (sum of 2 exponents). How to operate the function is clear: [curve, gof] = fit(x, y,'exp2'); But since there are multiple ways to fit a sum of exponents I'm trying to find out what algorithm is used. Particularly what happens when I'm fitting one exponent (the raw data) with a bit of noise, how the exponents are spread. I've simulated several cases, and it seems that it "drops" all the weight on the second set of coefficients, but row data analysis often shows different behavior. Does anyone have suggestions of documentation?

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  • Quantum Computing and Encryption Breaking

    - by Earlz
    Ok, I read a while back that Quantum Computers can break most types of hashing and encryption in use today in a very short amount of time(I believe it was mere minutes). How is it possible? I've tried reading articles about it but I get lost at the a quantum bit can be 1, 0, or something else. Can someone explain how this relates to cracking such algorithms in plain English without all the fancy maths?

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