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  • Floating point vs integer calculations on modern hardware

    - by maxpenguin
    I am doing some performance critical work in C++, and we are currently using integer calculations for problems that are inherently floating point because "its faster". This causes a whole lot of annoying problems and adds a lot of annoying code. Now, I remember reading about how floating point calculations were so slow approximately circa the 386 days, where I believe (IIRC) that there was an optional co-proccessor. But surely nowadays with exponentially more complex and powerful CPUs it makes no difference in "speed" if doing floating point or integer calculation? Especially since the actual calculation time is tiny compared to something like causing a pipeline stall or fetching something from main memory? I know the correct answer is to benchmark on the target hardware, what would be a good way to test this? I wrote two tiny C++ programs and compared their run time with "time" on Linux, but the actual run time is too variable (doesn't help I am running on a virtual server). Short of spending my entire day running hundreds of benchmarks, making graphs etc. is there something I can do to get a reasonable test of the relative speed? Any ideas or thoughts? Am I completely wrong? The programs I used as follows, they are not identical by any means: #include <iostream> #include <cmath> #include <cstdlib> #include <time.h> int main( int argc, char** argv ) { int accum = 0; srand( time( NULL ) ); for( unsigned int i = 0; i < 100000000; ++i ) { accum += rand( ) % 365; } std::cout << accum << std::endl; return 0; } Program 2: #include <iostream> #include <cmath> #include <cstdlib> #include <time.h> int main( int argc, char** argv ) { float accum = 0; srand( time( NULL ) ); for( unsigned int i = 0; i < 100000000; ++i ) { accum += (float)( rand( ) % 365 ); } std::cout << accum << std::endl; return 0; } Thanks in advance!

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  • liquid CSS issues - rtl, floating and scrollers

    - by Rani
    hi I want to build a site that will have these restrictions: RTL direction vertical scroll on the right side whole page is floating to the right page has 2 columns the right (main) column has min width the right (main) column has table inside it that can expend in its data and get wider making all other data in the column expend to the same width as well the sidebar should be on the left side but still floating to the right of the main div it should fit low resolution so the page will be able to add horizontal scroll if needed should work in all major browsers don't use table for constructing the page Can someone help or direct me? Thanks Rani

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  • expand floating object when floating object within expands

    - by Scarface
    Hey guys, quick question, I have a link when clicked drops down a list. This list is floated to the right to position it properly. This list is in another box that has been floated. My problem is that when the list expands, the box does not and the list comes out of the container box, unless the list is not floated. However floating it seems like the only way to get it to the position I want. If anyone has any ideas on how to solve this problem I would appreciate it. .container-box { margin-top:0px; float:left; padding-left:5px; position:relative; } #box-within { float:right; font-weight:bold; max-height:250px; display: none; background-color:#fff; overflow: auto; width:325px; padding:5px; position:relative; }

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  • Understanding floating point problems

    - by Maxim Gershkovich
    Could someone here please help me understand how to determine when floating point limitations will cause errors in your calculations. For example the following code. CalculateTotalTax = function (TaxRate, TaxFreePrice) { return ((parseFloat(TaxFreePrice) / 100) * parseFloat(TaxRate)).toFixed(4); }; I have been unable to input any two values that have caused for me an incorrect result for this method. If I remove the toFixed(4) I can infact see where the calculations start to lose accuracy (somewhere around the 6th decimal place). Having said that though, my understanding of floats is that even small numbers can sometimes fail to be represented or have I misunderstood and can 4 decimal places (for example) always be represented accurately. MSDN explains floats as such... This means they cannot hold an exact representation of any quantity that is not a binary fraction (of the form k / (2 ^ n) where k and n are integers) Now I assume this applies to all floats (inlcuding those used in javascript). Fundamentally my question boils down to this. How can one determine if any specific method will be vulnerable to errors in floating point operations, at what precision will those errors materialize and what inputs will be required to produce those errors? Hopefully what I am asking makes sense.

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  • Nicely representing a floating-point number in python

    - by dln385
    I want to represent a floating-point number as a string rounded to some number of significant digits, and never using the exponential format. Essentially, I want to display any floating-point number and make sure it “looks nice”. There are several parts to this problem: I need to be able to specify the number of significant digits. The number of significant digits needs to be variable, which can't be done with with the string formatting operator. I need it to be rounded the way a person would expect, not something like 1.999999999999 I've figured out one way of doing this, though it looks like a work-round and it's not quite perfect. (The maximum precision is 15 significant digits.) >>> def f(number, sigfig): return ("%.15f" % (round(number, int(-1 * floor(log10(number)) + (sigfig - 1))))).rstrip("0").rstrip(".") >>> print f(0.1, 1) 0.1 >>> print f(0.0000000000368568, 2) 0.000000000037 >>> print f(756867, 3) 757000 Is there a better way to do this? Why doesn't Python have a built-in function for this?

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  • Convert pre-IEEE-574 C++ floating-point numbers to/from C#

    - by Richard Kucia
    Before .Net, before math coprocessors, before IEEE-574, Microsoft defined a bit pattern for floating-point numbers. Old versions of the C++ compiler happily used that definition. I am writing a C# app that needs to read/write such floating-point numbers in a file. How can I do the conversions between the 2 bit formats? I need conversion methods in both directions. This app is going to run in a PocketPC/WinCE environment. Changing the structure of the file is out-of-scope for this project. Is there a C++ compiler option that instructs it to use the old FP format? That would be ideal. I could then exchange data between the C# code and C++ code by using a null-terminated text string, and the C++ methods would be simple wrappers around sprintf and atof functions. At the very least, I'm hoping someone can reply with the bit definitions for the old FP format, so I can put together a low-level bit manipulation algorithm if necessary. Thanks.

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  • How to efficiently compare the sign of two floating-point values while handling negative zeros

    - by François Beaune
    Given two floating-point numbers, I'm looking for an efficient way to check if they have the same sign, given that if any of the two values is zero (+0.0 or -0.0), they should be considered to have the same sign. For instance, SameSign(1.0, 2.0) should return true SameSign(-1.0, -2.0) should return true SameSign(-1.0, 2.0) should return false SameSign(0.0, 1.0) should return true SameSign(0.0, -1.0) should return true SameSign(-0.0, 1.0) should return true SameSign(-0.0, -1.0) should return true A naive but correct implementation of SameSign in C++ would be: bool SameSign(float a, float b) { if (fabs(a) == 0.0f || fabs(b) == 0.0f) return true; return (a >= 0.0f) == (b >= 0.0f); } Assuming the IEEE floating-point model, here's a variant of SameSign that compiles to branchless code (at least with with Visual C++ 2008): bool SameSign(float a, float b) { int ia = binary_cast<int>(a); int ib = binary_cast<int>(b); int az = (ia & 0x7FFFFFFF) == 0; int bz = (ib & 0x7FFFFFFF) == 0; int ab = (ia ^ ib) >= 0; return (az | bz | ab) != 0; } with binary_cast defined as follow: template <typename Target, typename Source> inline Target binary_cast(Source s) { union { Source m_source; Target m_target; } u; u.m_source = s; return u.m_target; } I'm looking for two things: A faster, more efficient implementation of SameSign, using bit tricks, FPU tricks or even SSE intrinsics. An efficient extension of SameSign to three values.

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  • Why differs floating-point precision in C# when separated by parantheses and when separated by state

    - by Andreas Larsen
    I am aware of how floating point precision works in the regular cases, but I stumbled on an odd situation in my C# code. Why aren't result1 and result2 the exact same floating point value here? const float A; // Arbitrary value const float B; // Arbitrary value float result1 = (A*B)*dt; float result2 = (A*B); result2 *= dt; From this page I figured float arithmetic was left-associative and that this means values are evaluated and calculated in a left-to-right manner. The full source code involves XNA's Quaternions. I don't think it's relevant what my constants are and what the VectorHelper.AddPitchRollYaw() does. The test passes just fine if I calculate the delta pitch/roll/yaw angles in the same manner, but as the code is below it does not pass: X Expected: 0.275153548f But was: 0.275153786f [TestFixture] internal class QuaternionPrecisionTest { [Test] public void Test() { JoystickInput input; input.Pitch = 0.312312432f; input.Roll = 0.512312432f; input.Yaw = 0.912312432f; const float dt = 0.017001f; float pitchRate = input.Pitch * PhysicsConstants.MaxPitchRate; float rollRate = input.Roll * PhysicsConstants.MaxRollRate; float yawRate = input.Yaw * PhysicsConstants.MaxYawRate; Quaternion orient1 = Quaternion.Identity; Quaternion orient2 = Quaternion.Identity; for (int i = 0; i < 10000; i++) { float deltaPitch = (input.Pitch * PhysicsConstants.MaxPitchRate) * dt; float deltaRoll = (input.Roll * PhysicsConstants.MaxRollRate) * dt; float deltaYaw = (input.Yaw * PhysicsConstants.MaxYawRate) * dt; // Add deltas of pitch, roll and yaw to the rotation matrix orient1 = VectorHelper.AddPitchRollYaw( orient1, deltaPitch, deltaRoll, deltaYaw); deltaPitch = pitchRate * dt; deltaRoll = rollRate * dt; deltaYaw = yawRate * dt; orient2 = VectorHelper.AddPitchRollYaw( orient2, deltaPitch, deltaRoll, deltaYaw); } Assert.AreEqual(orient1.X, orient2.X, "X"); Assert.AreEqual(orient1.Y, orient2.Y, "Y"); Assert.AreEqual(orient1.Z, orient2.Z, "Z"); Assert.AreEqual(orient1.W, orient2.W, "W"); } } Granted, the error is small and only presents itself after a large number of iterations, but it has caused me some great headackes.

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  • Floating point mantissa bias

    - by user69514
    Does anybody know how to go out solving this problem? * a = 1.0 × 2^9 * b = -1.0 × 2^9 * c = 1.0 × 2^1 Using the floating-point (the representation uses a 14-bit format, 5 bits for the exponent with a bias of 16, a normalized mantissa of 8 bits, and a single sign bit for the number), perform the following two calculations, paying close attention to the order of operations. * b + (a + c) = ? * (b + a) + c = ?

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  • cpu floating operations cost

    - by wiso
    I'm interesting in the time cost on a modern desktop cpu of some floating point operations in order to optimize a mathematical evaluation. In particular I'm interested on the comparison between complex operations like exp, log and simple operation like +, *, /. I tried to search for these information, but I can't find a source.

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  • Fixing Floating Point Error

    - by HannesNZ
    I have some code that gets the leading value (non-zero) of a Double using normal math instead of String Math... For Example: 0.020 would return 2 3.12 would return 3 1000 should return 1 The code I have at the moment is: LeadingValue := Trunc(ResultValue * Power(10, -(Floor(Log10(ResultValue))))) However when ResultValue is 1000 then LeadingValue ends up as 0. What can I do to fix this problem I'm assuming is being caused by floating point errors? Thanks.

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  • JQuery: Floating share box like Mashable?

    - by Prashant
    I am looking for a jQuery plugin which can serve me the floating share box functionality just like mashable implement in their new design: http://mashable.com/2010/03/12/flickr-co-founders-startup-hunch-raises-10-million/. On the page above in the left side mashable has sharing options which stays on the screen when you scroll the page. I want to implement same kind of functionality in my application, using a jQuery plugin.

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  • Real life example fo Floating Point error

    - by Rob
    Is there any examples of a company that was burned by floating point data that caused a rounding issue? We're implementing a new system and all the monetary values are stored in floats. I think if i can show actual examples of why this has failed it'll have more weight than the theory of why the values can't be stored properly.

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  • How to create "floating TextViews" in Android?

    - by Sotapanna
    Hi stackies, I'm programmatically putting various TextViews into a LinearLayout with a horizontal orientation. After 2h of research I couldn't find out how to tell Android not to squeeze all the TextViews in one line but instead to "float" non-fitting TextViews into the next line. I know there isn't something like actual "lines" in a LinearLayout, but how can I tell the TextViews to actually behave like floating DIVs from the HTML world? Thanks alot! Be well S.

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  • Floating point arithmetics restricted to integers

    - by user396672
    I use doubles for a uniform implementation of some arithmetic calculations. These calculations may be actually applied to integers too, but there are no C++-like templates in Java and I don't want to duplicate the implementation code, so I simply use "double" version for ints. Does JVM spec guarantees the correctness of integer operations such a <=,=, +, -, *, and / (in case of remainder==0) when the operations are emulated as corresponding floating point ops? (Any integer, of course, has reasonable size to be represented in double's mantissa)

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  • Floating point computer - Trouble with getting back correct results

    - by Francisco P.
    Having trouble with a challenge. Let's say I have a theoretical, base 10, floating point calculator with the following characteristics Only 3 digits for mantissa 1 digit for exponent Sign for mantissa and exponent How would this machine compute the following? 300 + \sum_{i=1}^{100} 0.2 The correct result is 320. The machine's result is 300. But why? Can't get where the 20 goes goes missing... Thanks for your time.

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  • Custom Floating Point Representation

    - by Abion47
    I'm trying to write a parser that will read a particular file type, and I need to map the different data types to C# equivalents. Most of them aren't that difficult, but I'm having trouble wrapping my head around what "int16 with a bias of 14" means. I've deduced that it's some kind of floating point type, so my best bet would be to write a converter that would map it to a float, double, or decimal type. I'm not sure where to take it from here, though.

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  • Floating DIV's alignment problem.

    - by Rodrigo
    I have a fluid layout with DIV's of different heights and widths, and I'd like them to be aligned by lines, kind of like when you do a search on istockphoto, except aligned to the top: image here--http://i207.photobucket.com/albums/bb121/jpbanks/Capturadepantalla2010-06-02alas1902.png I tried floating all the DIV's to the left, but they are not aligned correctly into lines: image here--http://i207.photobucket.com/albums/bb121/jpbanks/Capturadepantalla2010-06-02alas1900.png See how "Prueba" doesn't go all the way to the left? I thought of the jQuery plugin Masonry but what I want is obviously different. Any solution using either CSS or jQuery would be fine. Any ideas?

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  • Another floating point question

    - by jeffmax329
    I have read most of the posts on here regarding floating point, and I understand the basic underlying issue that using IEEE 754 (and just by the nature of storing numbers in binary) certain fractions cannot be represented. I am trying to figure out the following: If both Python and JavaScript use the IEEE 754 standard, why is it that executing the following in Python .1 + .1 Results in 0.20000000000000001 (which is to be expected) Where as in Javascript (in at least Chrome and Firefox) the answer is .2 However performing .1 + .2 In both languages results in 0.30000000000000004 In addition, executing var a = 0.3; in JavaScript and printing a results in 0.3 Where as doing a = 0.3 in Python results in 0.29999999999999999 I would like to understand the reason for this difference in behavior. In addition, many of the posts on OS link to a JavaScript port of Java's BigDecimal, but the link is dead. Does anyone have a copy?

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