Floating Point Error Divide By Zero Fluent
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Floating Point Exception Ansys Fluent
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is unclear or undesirable. Exceptions can be ignored, in which case some default action is taken, such as returning a special value. When trapping is enabled for an exception, a error is signalled whenever that exception occurs. These are the possible floating point exceptions: :underflow This exception occurs when the result of an https://www.scribd.com/doc/55179155/Floating-Point-Error-Wiki-Fluent operation is too small to be represented as a normalized float in its format. If trapping is enabled, the floating-point-underflow condition is signalled. Otherwise, the operation results in a denormalized float or zero. :overflow This exception occurs when the result of an operation is http://www.umiacs.umd.edu/~resnik/ling645_sp2002/cmu_manual/node19.html too large to be represented as a float in its format. If trapping is enabled, the floating-point-overflow exception is signalled. Otherwise, the operation results in the appropriate infinity. :inexact This exception occurs when the result of a floating point operation is not exact, i.e. the result was rounded. If trapping is enabled, the extensions:floating-point-inexact condition is signalled. Otherwise, the rounded result is returned. :invalid This exception occurs when the result of an operation is ill-defined, such as (/ 0.0 0.0). If trapping is enabled, the extensions:floating-point-invalid condition is signalled. Otherwise, a quiet NaN is returned. :divide-by-zero This exception occurs when a float is divided by zero. If trapping is enabled, the divide-by-zero condition is signalled. Otherwise, the appropriate infinity is returned. Raymond Toy Mon Jul 14 09:11:27 EDT 1997
When computations go awry in your program, you may notice incorrect numbers in some output fields, even though your program continues to execute. Sometimes you may notice strings like INF and NaN in fields where only numbers should be; these indicate https://www.cisl.ucar.edu/news/01/tips/0209.fpe.html certain kinds of floating-point exceptions (FPEs). INF means "infinity" and NaN means "not a number." Sometimes it's hard to find where these FPEs occur in your code, but you must find and fix them. They are useful only as http://zhidao.baidu.com/question/300095372.html diagnostics, and they harm performance since each FPE interrupts the processor on which it occurs. So how do you find where FPEs are occuring in your code? Salting your code with print statements is hit-or-miss and invasive, and floating point we do not recommend it. If you believe you have only a few FPEs, you are well advised to use a debugger like TotalView or dbx, which will often automatically point at the first FPE in your core file. But if you have many FPEs, weeding them out in this manner can be tedious. Error trapping An alternative and reliable method is called "trapping." By trapping, we mean setting a trap at your program's runtime that floating point error gets tripped when an FPE occurs, after which the program execution follows a prescribed course of your choice. This course is referred to as "handling" the error, where the handling you choose may cause the program to abort, print a diagnostic message, or provide a traceback. With certain methods of trapping, you can even provide a subroutine or function that changes the behavior of the floating-point arithmetic, though you should consult a numerical analyst about the consequences before handling errors in this manner. Since trapping and handling require extra processor time, you may wish to remove trapping/handling subroutine calls and compiler options after you have removed your program's FPEs. Both trapping and handling are implemented via "signals," and you often find their documentation under the broader topics of "signals" or "signal handling". All computers discussed in this article utilize IEEE binary floating-point arithmetic [1], with the exception of Cray, which uses Cray floating-point arithmetic. (Please consult a Cray Research CPU hardware reference manual if you need information about Cray's format.) Six floating-point error types There are six FPE types in the context of IEEE floating-point arithmetic. Underflow One form of underflow exception is signaled by the creation of a tiny nonzero result between the minimum expressible exponent, which, because it is tiny, may cause some other exception later. The other form of underflow exception is signaled by an
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