Copyright | Contents | Index | Previous | Next

G Numerics

The Numerics Annex addresses the particular needs of the numerically intensive computing community. Like the other specialized needs annexes, support of this annex is optional. The annex covers the following topics

Note that since the elementary functions and random number generation are in the core language, they and their accuracy requirements are discussed elsewhere (see A.3). The majority of attributes (including the so-called "primitive function" attributes) are also defined in the core language.

Implementations conforming to the numerics annex should also support the package Interfaces.Fortran, which is discussed in B.4.

G.1 Complex Arithmetic

Several application areas depend on the use of complex arithmetic. Complex fast Fourier transforms are used, for example, in conjunction with radar and similar sensors; conformal mapping uses complex arithmetic in fluid-flow problems such as the analysis of velocity fields around airfoils; and electrical circuit analysis is classically modelled in terms of complex exponentials.

The Ada 95 facilities for complex arithmetic consist of the generic packages

which are children of Ada.

G.1.1 Complex Types and Arithmetic Operations

When first designed, Numerics.Generic_Complex_Types was patterned after the version of the generic package, Generic_Complex_Types, that was then being developed in the SIGAda Numerics Working Group for proposal as an ISO standard for Ada 83. At that time, roughly mid-1992, the latter defined a complex type as well as types for vectors and matrices of complex components, together with a large set of scalar, vector, and matrix operations on those types. A decision was made to abbreviate the Ada 95 package by omitting the vector and matrix types and operations. One reason was that such types and operations were largely self-evident, so that little real help would be provided by defining them in the language. Another reason was that a future version of Ada might add enhancements for array manipulation and so it would be inappropriate to lock in such operations prematurely.

The initial design for Numerics.Generic_Complex_Types also inherited a rather pedestrian approach to defining the complex type from the same proposed standard. The Ada 9X Distinguished Reviewers recommended a different approach that enabled the writing of expressions whose appearance closely matches that of the standard mathematical notation for complex arithmetic. The idea was to define not just a complex type, but also a pure imaginary type Imaginary and a constant i of that type; operations could then easily be defined to allow one to write expressions such as

   3.0 + 5.0*i   -- of type Complex
which has the feel of a complex literal.

Another advantage of this approach is that by providing mixed mode operations between complex and imaginary as well as between complex and real, it is possible to avoid unnecessary arithmetic operations on degenerate components. Moreover, avoiding such unnecessary operations is crucial in environments featuring IEEE arithmetic [IEC 89], where signed infinities can arise and can be used in meaningful ways.

(Ada 95 does not support infinite values, but in an implementation in which the Machine_Overflows attribute is False, an overflow or a division by zero yields an implementation-defined result, which could well be an infinite value. Thus a future Ada binding to IEEE arithmetic could capitalize on exactly that opportunity by providing semantics of arithmetic with infinities as in [IEC 89].)

To see how avoiding unnecessary operations provides more than just a gain in efficiency, consider the multiplication of a complex value x + iy by a pure imaginary value iv. The result of the multiplication should, of course, be -vy + ivx. Without a pure imaginary type, we have to represent the pure imaginary value as the complex value 0.0 + iv and perform a full complex multiplication, yielding (0.0x-vy) + i(vx+0.0y). This, of course, reduces to the same value as before, unless x or y is an infinity.

However, if x is infinity, y is finite, and v is nonzero (say, positive), the result should be -vy + i*infinity, but instead we get NaN + i*infinity, since multiplication of zero and infinity yields a NaN ("Not-a-Number") in IEEE arithmetic, and NaNs propagate through addition. See [Kahan 91].

A similar situation can be demonstrated for the multiplication of a complex value and a pure real value, but in that case we expect to have such a mixed-mode operation, and if we use it the generation of the NaN is avoided.

Another subtle problem occurs when the imaginary value iv is added to the complex value x + iy. The result, of course, should be x + i(y+v). Without an imaginary type, we have to represent the imaginary value as the complex value 0.0 + iv and perform a full complex addition, yielding (x+0.0) + i(y+v). The problem here [Kahan 91] is that if x is a negative zero, the real component of the result of the full complex addition will have the wrong sign; it will be a positive zero instead of the expected negative zero. This phenomenon, also a consequence of the rules of IEEE arithmetic, can and does occur in existing Ada 83 implementations, since it does not require an extension permitting infinities.

In both cases, the pure imaginary type gives the programmer the same opportunity to avoid problems in mixed complex/imaginary arithmetic as in mixed complex/real arithmetic.

With the inclusion of a pure imaginary type and mixed complex/imaginary operations, the generic complex types package in Ada 95 could have diverged from the proposed standard under development in the SIGAda NumWG. This was averted, however, when the working group changed its proposed standard to agree with the Ada 95 version. It also removed the vector and matrix types and operations from its generic complex types package and put them in a separate package. And so, Numerics.Generic_Complex_Types is just a slight variation of the generic package proposed for standardization for Ada 83. (The differences have to do with the use of Real'Base, rather than just Real, as the subtype mark for the components of the complex type and for the parameter and result subtypes of some of the operations, Real being the name of the generic formal parameter. This capability is lacking in Ada 83.)

The type Complex defined by Numerics.Generic_Complex_Types is a visible record type thus

   type Complex is
         Re, Im: Real'Base;
      end record;
corresponding to the cartesian representation of a complex value. We have made the type visible to allow one to write complex "literals" using aggregate notation as in some other languages (and to ensure efficiency). The cartesian representation was chosen over a polar representation to avoid canonicalization problems and because it is normal practice. An explicit choice of representation is required in any case to give meaning to the accuracy requirements. Operations are provided, however, to compute the modulus (length) and argument (angle) of a complex value and to construct a complex value from a given modulus and argument, so that it is easy to convert between the built-in cartesian representation and a polar representation, if needed.

It is perhaps unusual that the components of the complex type are of the unconstrained subtype of the generic formal parameter, Real'Base, rather than just Real, but this is intended to increase the chances of being able to deliver the result computed by a complex arithmetic operation even when their operands belong to some restricted domain. This provides behavior analogous to that of the elementary functions (see A.3.1), which also yield results in the unconstrained subtype of the relevant generic formal parameter. It is also similar to the behavior of the predefined arithmetic operations, which yield results of an unconstrained subtype, even when their operands are of a constrained subtype. A consequence is that we cannot create complex types with constrained components, but that does not seem so severe in view of the fact that applications of complex arithmetic typically have a naturally circular domain, rather than a rectangular domain.

The type Imaginary, on the other hand, is private, its full type being derived from Real'Base thus

   type Imaginary is new Real'Base;

Making it private prevents the implicit conversion of a real literal to the type Imaginary, which would be available if Imaginary were visibly derived from Real'Base. This avoids various ambiguous expressions and enables overload resolution to work properly. It has the additional advantage of suppressing the implicit declaration of multiplying operators for the Imaginary type, which would incorrectly yield a result of the type Imaginary, when it should be Real'Base. Operations with the correct result type such as

   function "*" (Left, Right: Imaginary) return Real'Base;
are, of course, explicitly declared. The same benefits could have been achieved by defining Imaginary as a visible record type with a single component, but the method chosen prevents the writing of expressions containing pure imaginary values as aggregates, whose meaning would not be intuitively obvious.

The imaginary constant i (and its equivalent, j, provided for the engineering community), has the value 1.0, and so unoptimized expressions like 5.0*i will have the proper numerical value. However, it is expected that compilers will optimize this and effectively convert the real literal 5.0 to the imaginary type. Similarly, an expression such as 3.0 + 5.0*i can be optimized to perform no arithmetic at all since it is functionally equivalent to the aggregate (3.0, 5.0) of the type Complex.

Note that there are also constructor and selector functions such as Compose_From_Cartesian. The following expressions are thus equivalent

   X + i*Y                         -- using operators

   (X, Y)                          -- using an aggregate

   Compose_From_Cartesian(X, Y)    -- using the constructor

The constructor function has the merit that it can be used as a generic actual parameter, if it should be necessary.

Nongeneric equivalents of Numerics.Generic_Complex_Types are provided corresponding to instantiations with the predefined types Float, Long_Float and so on with names such as

   Numerics.Complex_Types        -- for Float
   Numerics.Long_Complex_Types   -- for Long_Float

This means that applications can effectively treat single-precision complex, double-precision complex, etc., as predefined types with the same convenience that is provided for the predefined floating point types (or, more importantly, so that independent libraries assuming the existence and availability of such types without the use of generics can be constructed and freely used in applications). The nongeneric forms also have the advantage that Fortran programmers migrating to Ada do not have to learn generics in order to use complex arithemtic.

Accuracy requirements are generally specified for the complex arithmetic operations only in the strict mode. Nevertheless, certain special cases are prescribed to give the exact result even in the relaxed mode, ensuring high quality at negligible implementation cost. Examples are where one operand is pure real or pure imaginary. (These prescribed results are likely to be achieved even without special attention by the implementation.) Accuracy requirements are not given for exponentiation of a complex operand by an integer, owing to the variety of implementations that are allowed (ranging from repeated complex multiplication to well-known operations on the polar representation).

Note finally that spurious overflows (those occurring during the computation of an intermediate result, when the final result, or its components, would not overflow) are not allowed. Thus, implementations of complex multiplication and division need to be somewhat more sophisticated than the textbook formulae for those operations.

G.1.2 Complex Elementary Functions

The package Numerics.Generic_Complex_Elementary_Functions differs from the corresponding proposed standard for Ada 83 by taking advantage of the formal package parameter facility of Ada 95. Thus it imports the one parameter which is an instance of Numerics.Generic_Complex_Types instead of the complex type and a long list of operations exported by such an instance.

In the Ada 83 version, the complex type has to be imported as a private type, and implementations of the complex elementary functions there have no choice but to use the imported selector functions, Re and Im, to extract the real and imaginary components of a complex value, and the imported constructor function, Compose_From_Cartesian, to assemble such components into a complex value. Implementations of the Ada 95 version see that type as the record type that it is, allowing more efficient composition and decomposition of complex values; they also have available the complete set of operations, not just the partial (albeit long enough) list of operations imported in the Ada 83 version.

Nonngeneric equivalents of Numerics.Generic_Complex_Elementary_Functions are also provided for each of the predefined floating point types.

The overloading of the Exp function for a pure imaginary parameter is provided to give the user an alternative way to construct the complex value having a given modulus and argument. Thus, we can write either

   Compose_From_Polar(R, Theta)
   R * Exp(i * Theta).

The treatment of accuracy requirements and prescribed results for the complex elementary functions is analogous to that discussed above for the complex arithmetic operations. However, since the avoidance of spurious overflows is difficult and expensive to achieve in several of the functions, it is explicitly permitted, allowing those functions to be implemented in the obvious way. No accuracy requirement is imposed on complex exponentiation (the operator "**") by a pure real, pure imaginary, or complex exponent, because the obvious implementation in terms of complex exponentials and logarithms yields poor accuracy in some parts of the domain, and better algorithms are not available.

G.1.3 Complex I/O

Complex I/O is performed using the procedures in the generic child package, Text_IO.Complex_IO. As with Numerics.Generic_Complex_Elementary_Functions, the user instantiates this generic package with an instance of Numerics.Generic_Complex_Types. (Note that nongeneric equivalents do not exist.)

A fundamental design decision underlying Text_IO.Complex_IO is that complex values are represented on the external medium in parenthesized aggregate notation, as in Fortran list-directed I/O. This is the format produced on output, and it is the format expected on input, except that the comma, the parentheses, or both may be omitted when the real and imaginary components are separated by appropriate white space. (This allows the reading of existing Fortran files containing complex data written by a variety of techniques.)

An implementation of Text_IO.Complex_IO can easily be built around an instance of Text_IO.Float_IO for Real'Base; the optional parentheses on input requires the use of the procedure Text_IO.Look_Ahead; see A.4.2.

Text_IO.Complex_IO defines similar Get and Put procedures to Text_IO.Float_IO with analogous semantics. The only somewhat arbitrary decision that we made concerns the mechanism for filling the target string in the case of output to a string. The question is where to put any extra blanks that are needed to fill the string. A rule like the one for Text_IO.Float_IO.Put to a string might read:

     Outputs the value of the parameter Item to the given string,
     following the same rule as for output to a file, using a value
     for Fore such that the sequence of characters output exactly
     fills, or comes closest to filling, the string; in the latter
     case, the string is filled by inserting one extra blank
     immediately after the comma.

Such a rule essentially allocates the available space equally to the real and imaginary components. But that is not desirable when the value of the Exp parameter is zero and the two components of the Item parameter have disparate magnitudes, so that the integer part of one requires more characters than the integer part of the other. To accommodate this case, we have chosen a rule, simple to implement, that left justifies the left parenthesis, real component, and comma and right justifies the imaginary component and right parenthesis. All the extra spaces are placed between the comma and the imaginary component; they are available to accommodate either component, should it be unusually long. If strings produced by this rule are eventually displayed in the output, the real and imaginary components will not line up in columns as well as with the previously cited rule, but it can be argued that output to a string is intended for further computation, rather than for display.

Early implementation experience indicated that it might also have been profitable to consider yet another rule for output to a string, namely, that all the components be right justified, with all the padding placed at the beginning of the string. This rule would be extremely easy to implement if Text_IO.Float_IO.Put to a string (which right justifies its output in the target string), had an additional out parameter which gave the index of the first non-blank character that it put into the output string.

G.2 Floating Point Machine Numbers

Many of the attributes of floating point types are defined with reference to a particular mathematical representation of rational numbers called the canonical form, which is defined, together with those attributes, in the Predefined Language Environment. The definitions clarify certain aspects of the floating point machine numbers. Several new representation-oriented attributes of floating point machine numbers are also defined, together with a group of functional attributes called the "primitive function" attributes.

G.2.1 Clarification of Existing Attributes

The machine numbers of a floating point type are somewhat informally defined as the values of the type that are capable of being represented to full accuracy in all unconstrained variables of the type. The intent is to exclude from the set of machine numbers any extra-precise numbers that might be held in extended registers in which they are generated as a consequence of performing arithmetic operations. In other words, it is the stored values that matter and not values generated as intermediate results.

The representation-oriented attributes S'Machine_Mantissa, S'Machine_Emin, and S'Machine_Emax of a floating point subtype S are defined in terms of bounds on the components of the canonical form. The attribute S'Machine_Radix is the radix of the hardware representation of the type and is used as the radix of the mantissa in the canonical form.

These definitions clarify that S'Machine_Emin is the minimum canonical-form exponent such that all numbers expressible in the canonical form, with that exponent, are indeed machine numbers. In other words, S'Machine_Emin is determined by the normalized floating point numbers only; the presence of IEEE denormalized numbers in the implementation does not affect (reduce) the value of S'Machine_Emin. A consequence of this definition of S'Machine_Emin is that the primitive function attribute S'Exponent(X) can yield a result whose value is less than that of S'Machine_Emin if X is denormalized.

The definitions also clarify that S'Machine_Emax is the canonical-form exponent of the machine number of largest magnitude whose negation is also a machine number; it is not the canonical-form exponent of the most negative number on radix-complement machines.

Alternative definitions for S'Machine_Emin and S'Machine_Emax were considered, namely, that they yield the minimum and maximum canonical- form exponents for which some combination of sign, exponent, and mantissa yields a machine number. This would have allowed denormalized numbers to be accommodated without relaxing the normalization requirement (see the next section) in the definition of the canonical form, and the result of S'Exponent(X) would have necessarily remained within the range S'Machine_Emin .. S'Machine_Emax, which is appealing. Nevertheless, it was judged to be too much of a departure from current practice and therefore too likely to cause compatibility problems.

G.2.2 Attributes Concerned With Denormalized Numbers and Signed Zeros

Many implementations of Ada do provide IEEE denormalized numbers and "gradual underflow", as defined in [IEC 89], even though the full capabilities of IEEE arithmetic are not provided and must await an Ada binding to IEEE arithmetic. (Denormalized numbers come for free with IEEE hardware chips and have always been consistent with the Ada model of floating point arithmetic; it would require extra code generation to suppress them.) Since denormalized numbers are capable of being stored in variables of an unconstrained floating point type, they are machine numbers. What characterizes the denormalized numbers is that they can be represented in the canonical form with an exponent of S'Machine_Emin, provided that the normalization requirement on the mantissa is relaxed. If every nonzero number expressible in this weakened canonical form is a machine number of the subtype S, then the new representation-oriented attribute, S'Denorm, is defined to have the value True.

Many implementations also provide IEEE signed zeros, which similarly come for free. The new representation-oriented attribute, S'Signed_Zeros, is True if signed zeros are provided by the implementation and used by the predefined floating point operations as specified in [IEC 89]. The idea behind a signed zero is that a zero resulting from a computation that underflows can retain the sign of the underflowing quantity, as if the zero represented an infinitesimal quantity instead; the sign of a zero quantity, interpreted as if it were infinitesimal, can then affect the outcome of certain arithmetic operations.

Moreover, various higher-level operations, such as the elementary functions, are defined to yield zeros with specified signs for particular parameter values when the Signed_Zeros attribute of the target type is True. And indeed, some such operations, for example, Arctan and Arccot produce different (nonzero) results in certain cases, depending on the sign of a zero parameter, when Signed_Zeros is True.

In some cases, no conventions exist yet for the sign of a zero result. We do not specify the sign in such cases, but instead require the implementation to document the sign it produces. Also, we have not carried over into the facilities for complex arithmetic and the complex elementary functions the specifications for the signs of zero results (or their components) developed by the SIGAda NumWG, largely because of their excessive complexity. Instead we merely suggest that implementations should attempt to provide sensible and consistent behavior in this regard (for example, by preserving the sign of a zero parameter component in a result component that behaves like an odd function).

G.2.3 The Primitive Function Attributes

A group of attributes of floating point types called the "primitive function" attributes is provided, in support of Requirement R11.1-A(1), to facilitate certain operations needed by the numerical specialists who develop mathematical software libraries such as the elementary functions.

These attributes are modelled on the various functions in the standard package Generic_Primitive_Functions for Ada 83 [ISO 94b], but made available in the language as attributes of a floating point subtype rather than as functions in a generic package. These attributes support

All of these attributes yield the mathematically specified results, which are either machine numbers or have the accuracy of the parameters. For a general rationale for the design of the primitive functions, see [Dritz 91b]. (Some of the attributes have different names from the corresponding functions in [ISO 94b], since some of the names in the latter had already been used for other new, but unrelated, attributes.)

The casting of the primitive functions as attributes, rather than as functions in a generic package, befits their primitive nature and allows them to be used as components of static expressions, when their parameters are static.

The Exponent and Fraction attributes decompose a floating point number into its exponent and (signed) fraction parts. (These attributes, along with Scaling and Leading_Part, are useful in the argument reduction step of certain kinds of function approximation algorithms in high-quality mathematical software; Scaling and Compose are similarly useful in the final result assembly steps of such algorithms.)

T'Exponent(X) is defined in such a way that it gives an indication of the gross magnitude of X, even when X is denormalized. In particular, if X is repetitively scaled down by a power of the hardware radix, T'Exponent(X) will decrease by one and will continue to do so (on machines with denormalized numbers) even after X becomes denormalized. T'Fraction(X) will not change as X is scaled in this way, even when X becomes denormalized. To achieve this behavior, an implementation of Exponent must do more than just pick up the hardware exponent field and unbias it, and an implementation of Fraction must do more than just pick up the hardware fraction field; both attributes must be sensitive to denormalized numbers.

(Indeed, the presence or absence of the leading fraction digit is dependent on whether a number is denormalized or not, on IEEE hardware. Probably the most efficient solution to the problem is for the implementation of Exponent to scale the operand up by an appropriate fixed amount k sufficient to normalize it when the operand is in the denormalized range, as evidenced by its having the minimum exponent or by other means; extract and unbias the exponent field; and then subtract k to obtain the result. The implementation of Fraction can simply extract the fraction field after a similar scaling up when the operand is in the denormalized range, and then attach the appropriate fixed exponent; what the scaling accomplishes is the left shifting of the fraction field and the removal of its leading digit.)

The Copy_Sign attribute transfers the sign of one value to another. It is provided for those applications that require a sign to be propagated, even (on machines with signed zeros) when it originates on a zero; such a need cannot be met by the predefined comparison operators and various sign-changing operations (like abs and negation), because comparison ignores the sign of zero and therefore cannot be used to determine the sign of a zero. By the same token, the implementation of Copy_Sign must likewise use some other technique, such as direct transfer of the sign bit or some other examination of the Sign operand to determine its sign. An application can use the Copy_Sign attribute to determine the sign of a zero value, when required, by transferring that sign to a nonzero value and then comparing the latter to zero.

The Remainder attribute computes the remainder upon dividing its first floating point parameter by its second. It considers both parameters to be exact, and it delivers the exact remainder, even when the first parameter is many orders of magnitude larger than the second; for this reason, its implementation can be tricky. This attribute is useful in implementing the argument reduction step of algorithms for computing periodic functions (when the period is given exactly). The function T'Remainder(X, Y) is defined so that the magnitude of the result is less than or equal to half the magnitude of Y; it may be negative, even when both parameters are positive. Note that the Remainder attribute cannot be considered to be a natural extension of the predefined rem operator for floating point operands (and, indeed, that is one reason why the functionality was not made available by overloading rem). To see this, observe that

   Float'Remainder(14.0, 5.0)   -- yields -1.0
   14 rem 5                     -- yields 4
and so are quite different. The rationale for defining Remainder this way is twofold: it exhibits the behavior preferred by numerical analysts (i.e., it yields a reduced argument of generally smaller magnitude), and it agrees with the IEEE rem operator. Indeed, the latter is implemented in hardware on some machines; when available, it should certainly be used instead of the painful alternative documented in [Dritz 91b].

The functionality of the Successor and Predecessor functions of [ISO 94b] is provided by extending the existing attributes Succ and Pred to floating point types. Note that T'Succ(0.0) returns the smallest positive number, which is a denormalized number if T'Denorm is True and a normalized number if T'Denorm is False; this is equivalent to the "fmin" derived constant of LIA-1 (Language Independent Arithmetic) [ISO 93]. (Most of the other constants and operations of LIA-1 are provided either as primitive functions or other attributes in Ada 95; those that are absent can be reliably defined in terms of existing attributes.)

G.3 Assignments to Variables of Unconstrained Numeric Types

Ada 83 did not make a distinction between unconstrained and constrained numeric subtypes. Any subtype T was considered constrained by the values given by T'First and T'Last; if no range constraint was declared for T, then T'First = T'Base'First and T'Last = T'Base'Last.

It was technically not possible for a variable of an unconstrained subtype to be assigned a value outside the range T'Base'First .. T'Base'Last. This prevented the optimization of leaving a value in an extended register beyond an assignment, fulfilling subsequent references to the target variable from the register. To guarantee that the new value of the target variable after an assignment is not outside the range T'Base'First .. T'Base'Last, it is necessary in Ada 83 either to store the register into the variable's assigned storage location (if such a store operation could signal violation of the range check by generating an overflow condition) or to compare the value in the register to the values of T'Base'First and T'Base'Last; if the range check succeeds, the value in the register can be used subsequently. Ada 95 does not perform a range check on the value assigned to a variable of an unconstrained numeric subtype; consequently, such a target variable can acquire a value outside its base range (T'Base'First .. T'Base'Last). This allows the value to be retained in the register in which it was generated and never stored (if all subsequent references to the target variable can be fulfilled from the register) nor checked by comparison for inclusion in the base range.

A consequence of this change, which generally allows more efficient object code to be generated, is that an Ada 83 program that raised Constraint_Error for a range violation on assignment to a variable of an unconstrained numeric subtype may raise the exception later (at a different place) or not at all. The first possibility arises because it may be necessary to store the extended register in the storage format for any of several reasons at a place far removed from the original assignment. The opportunity to raise the exception at such remote places is provided by a new rule that allows Constraint_Error to be raised at the place where a variable is referenced (fetched), if its value is outside its base range.

G.4 Accuracy and Other Performance Issues

The Numerics Annex specifies the accuracy to be delivered by the elementary functions, the complex arithmetic operations, and the complex elementary functions, and the performance to be expected from the random number generator. It also specifies the accuracy expected of the predefined floating point and fixed point arithmetic operators. If the Numerics Annex is not implemented, the predefined arithmetic operations and the various numerical packages do not have to yield any particular language-defined accuracy or performance, except where specified outside the Numerics Annex.

Even though the accuracy and performance requirements of the Numerics Annex are realistic, strict conformance may, in certain implementations, come only at a price. One or more native floating point instructions may produce slightly anomalous behavior that cannot conform to the model without some kind of sacrifice; for example, the purported maximum precision may have to be reduced, or some operations may have to be simulated in software. Similarly, achieving the specified accuracy in the elementary functions (say) may mean abandoning less accurate but much faster versions available on a hardware chip. Thus, to allow the user to trade accuracy for other considerations, the Numerics Annex specifies a pair of modes, strict mode and relaxed mode, that may be selected by the user. The accuracy and other performance requirements apply only when the strict mode is selected; the relaxed mode exempts implementations from these requirements, exactly as if the Numerics Annex had not been implemented.

A program that is intended for wide distribution, and whose numerical performance is to be guaranteed and portable across implementations, must be compiled and linked in the strict mode. Its portability will clearly extend, in that case, only to implementations that support the Numerics Annex.

The language does not specify how the modes should be implemented. It is clear, however, that the choice of mode can affect code generation, the values of the model-oriented attributes, and the version of the numerical libraries that is used. In implementations that meet the requirements without any undue sacrifices, or that have nothing substantial to gain from their relaxation, the two modes may, in fact, be identical.

G.4.1 Floating Point Arithmetic and Attributes

The strict-mode accuracy requirements for predefined floating point arithmetic operations are based on the same kind of model that was used in Ada 83, but with several changes. The Ada 83 model of floating point arithmetic was a two-level adaptation of the "Brown Model" [Brown 81] and defined both model numbers and safe numbers. The Ada 95 model is closer to a one-level, classical Brown Model that defines only model numbers, although it innovates slightly in the treatment of the overflow threshold.

The existence of both model numbers and safe numbers in Ada 83 caused confusion which hopefully will not apply to Ada 95. Note however, that the model numbers in Ada 95 are conceptually closer to the safe numbers rather than the model numbers of Ada 83 in terms of their role in the accuracy requirements. Other problems with the Ada 83 model centered around inherent compromises in the way the Brown Model was adapted to Ada 83. These compromises are eliminated in Ada 95, and other improvements are made, by

We will now consider these in detail.

Mantissa Length

The Ada 83 safe numbers have mantissa lengths that are a function of the Digits attribute of the underlying predefined type, giving them a quantized length chosen from the list (5, 8, 11, 15, 18, 21, 25, ...). Thus, on binary hardware having T'Machine_Mantissa equal to 24, which is a common mantissa length for the single-precision floating point hardware type, the last three bits of the machine representation exceed the precision of the safe numbers. As a consequence, even when the machine arithmetic is fully accurate (at the machine-number level), one cannot deduce that Ada arithmetic operations deliver full machine-number accuracy. By freeing the mantissa length from quantization, tighter accuracy claims will be provable on many machines. As an additional consequence of this change, in Ada 95 the two types declared as follows

   type T1 is digits D;
   type T2 is digits T1'Digits range T1'First .. T1'Last;
can be represented identically. This matches one's intuition, since the declaration of T2 requests neither more precision nor more range than that of T1. In Ada 83, the chosen representations almost always differ, with T2'Base'Digits being greater than T1'Base'Digits, for reasons having nothing to do with hardware considerations. (Note that this artificial example is not intended to illustrate how one should declare two different types with the same representation.)


Using the hardware radix rather than a binary radix has two effects:

As an example of the latter effect, note that T'Last will become a model number on most hexadecimal machines. Also, on hexadecimal hardware, a 64-bit double-precision type having 14 hexadecimal (or 56 binary) digits in the hardware mantissa, as on many IBM machines, has safe numbers with a mantissa length of 51 binary bits in Ada 83, and thus no machine number of this type with more than 51 bits of significance is a safe number. In Ada 95, such a type would have a mantissa length of 14 hexadecimal digits, with the consequence that every machine number with 53 bits of significance is now a model number, as are some with even more.

Note that the type under discussion does not have Ada 83 safe numbers with 55 bits in the mantissa, even though that is the next possible quantized length and one which is less than that of the machine mantissa. This is because some machine numbers with 54 or 55 bits of significance do not yield exact results when divided by two and cannot therefore be safe numbers. This is a consequence of their hexadecimal normalization, and it gives rise to the phenomenon known as "wobbling precision": the hexadecimal exponent remains unchanged by the division, while the mantissa is shifted right, losing a bit at the low-order end.

Safe Range

Extending the model numbers to an infinite set is intended to fill a gap in Ada 83 wherein the results of arithmetic operations are not formally defined when they are outside the range of the safe numbers but an exception is not raised. Some of the reasons why this can happen are as follows:

The change means, of course, that one can no longer say that the model numbers of a type are a subset of the machine numbers of the type. As a consequence we have introduced the concept of the "safe range" of a type in Ada 95. This is the subrange of the type's base range that is guaranteed to be free of overflow; in Ada 83 terms, this subrange was the range of the type's safe numbers. Thus, in Ada 95 the the model numbers of a type within the type's safe range are indeed a subset of the machine numbers of the type.

By continuing the model numbers beyond the safe range of a type, we can say that an operation whose result interval (defined, as usual, in terms of model numbers) transcends the safe range of the result type either raises Constraint_Error, signalling overflow, or delivers a value from that result interval (provided the Machine_Overflows attribute is True for the type). In Ada 83, the result when an exception was not raised was completely implementation defined in this case. Of course, it continues to be implementation defined when the Machine_Overflows attribute is False for the type.

Incidentally, the safe range of a type has bounds characterized by two independent attributes, Safe_First and Safe_Last. In Ada 83, the range of safe numbers of a type was necessarily symmetric, with its upper bound being given by the value of the Safe_Large attribute. This was necessary, because Safe_Large was itself defined in terms of Safe_Emax, which gave the maximum exponent of the safe numbers. Because the model numbers no longer have a finite range, we no longer talk about the maximum exponent of the model numbers, and in fact there is no longer such an atttribute. Allowing the safe range to be asymmetric accommodates radix-complement machines better than Ada 83; in fact, it removes another impediment to the identical representation of the types T1 and T2 in the example given earlier.

The 4B Rule

Separating range and precision considerations is equivalent to dropping the "4B Rule" as it applies to the predefined types. There is however a "4D Rule" which affects the implementation's implicit selection of an underlying representation for a user-declared floating point type lacking a range specification, providing in that case a guaranteed range tied to the requested precision.

The change in the application of the 4B Rule allows all hardware representations to be accommodated as predefined types with attributes that accurately characterize their properties. Such types are available for implicit selection by the implementation when their properties are compatible with the precision and range requested by the user; but they remain unavailable for implicit selection, in the absence of an explicit range specification, exactly as in Ada 83.

The 4D Rule says that the representation chosen for a floating point type declared with a decimal precision of d, but lacking a range specification, must provide a safe range of at least -10.04d .. 10.04d. If the type declaration includes a range specification, the safe range need only cover the specified range.

The 4B Rule was introduced in Ada 83 in order to define the model numbers of a type entirely as a function of a single parameter (the requested decimal precision). By its nature, the rule potentially precludes the implementation of Ada in some (hypothetical) environments; in other (actual) environments, it artificially penalizes some hardware types so strongly that they have only marginal utility as predefined types available for implicit selection and may end up being ignored by the vendor. Such matters are best left to the judgment of the marketplace and not dictated by the language. The particular minimum range required in Ada 83 (as a function of precision) is furthermore about twice that deemed minimally necessary for numeric applications [Brown 81].

Among implementations of Ada 83, the only predefined types whose characteristics are affected by the relaxation of the 4B Rule are DEC VAX D-format and IBM Extended Precision, both of which have a narrow exponent range in relation to their precision.

In the case of VAX D-format, even though the hardware type provides the equivalent of 16 decimal digits of precision, its narrow exponent range requires that the Digits attribute for this type be severely penalized and reported as 9 in Ada 83; the Mantissa attribute is similarly penalized and reported as 31, and the other model attributes follow suit. In Ada 95, in contrast, the Digits attribute of this predefined type would have a truthful value of 16, the Model_Mantissa attribute (corresponding to Ada 83's Mantissa attribute, but interpreted relative to the hardware radix rather than a fixed radix of two) would have a value of 56, and the other model-oriented attributes would accurately reflect the type's actual properties. A user-declared floating point type requesting more than 9 digits of precision does not select D-format as the underlying representation in Ada 83, but instead selects H-format; in Ada 95, it still cannot select D-format if it lacks a range specification (because of the effect of the new 4D Rule), but it can select D-format if it includes an explicit range specification with sufficiently small bounds.

The IBM Extended Precision hardware type has an actual decimal precision of 32, but the 4B Rule requires the value of its Digits attribute to be severely penalized and reported as 18 in Ada 83, only three more than that of the double-precision type. Supporting this type allows an Ada 83 implementation to increase System.Max_Digits from 15 to 18, a marginal gain and perhaps the reason why it is rarely supported. In Ada 95, on the other hand, such an implementation can support Extended Precision with a Digits attribute having a truthful value of 32, though System.Max_Digits must still be 18. Although a floating point type declaration lacking a range specification cannot request more than 18 digits on this machine, those including an explicit range specification with sufficiently small bounds can do so and can thereby select Extended Precision.

Note that the named number System.Max_Base_Digits has been added to Ada 95; it gives the maximum decimal precision that can be requested in a type declaration that includes a range specification. In IBM systems having the Extended Precision type, the value of this named number can be 32.

Minimum Exponent

Freeing the minimum exponent of the model numbers to reflect only underflow considerations removes another compromise made necessary in Ada 83 by defining the model numbers of a type in terms of a single parameter. The minimum exponent of the model or safe numbers of a type in Ada 83 is required to be the negation of the maximum exponent (thereby tying it implicitly both to the overflow threshold and, through the 4B Rule, to the precision of the model numbers).

One consequence of this is that Ada 83's range of safe numbers may need to be reduced simply to avoid having the smallest positive safe number lie inside the implementation's actual underflow threshold. Such a reduction gives yet another way of obtaining values outside the range of safe numbers without raising an exception. Another consequnce is that the smallest positive safe number may, on the other hand, have a value unnecessarily greater than the actual underflow threshold.

This change therefore allows more of the machine numbers to be model numbers, allowing sharper accuracy claims to be proved.

Machine and Model Numbers

Consideration was given to eliminating the model numbers and retaining only the machine numbers. While this would further simplify the semantics of floating point arithmetic, it would not eliminate the interval orientation of the accuracy requirements if variations in rounding mode from one implementation to another and the use of extended registers are both to be tolerated. It would simply substitute the machine numbers and intervals for the model numbers and intervals in those requirements, but their qualitative form would remain the same. However, rephrasing the accuracy requirements in terms of machine numbers and intervals cannot be realistically considered, since many platforms on which Ada has been implemented and might be implemented in the future could not conform to such stringent requirements.

If an implementation has clean arithmetic, its model numbers in the safe range will in fact coincide with its machine numbers, and an analysis of a program's behavior in terms of the model numbers will not only have the same qualitative form as it would have if the accuracy requirements were expressed in terms of machine numbers, but it will have the same quantitative implications as well. On the other hand, if an implementation lacks guard digits or has genuine anomalies, its model numbers in the safe range will be a subset of its machine numbers having less precision, a narrower exponent range, or both, and accuracy requirements expressed in the same qualitative form, albeit in terms of the machine numbers, would be unsatisfiable.

The values of the model-oriented attributes of a subtype S of a floating point type T are defined in terms of the model numbers and safe range of the type T, when the Numerics Annex is implemented; this is true even in the relaxed mode. (Some of these attributes have partially implementation-defined values if the Numerics Annex is not implemented.)

Although these attributes generally have counterparts in Ada 83, their names are new in Ada 95. The reason is that their values may be different in Ada 95. Clearly, S'Model_Mantissa and S'Model_Emin will have very different values on a nonbinary machine, since they are interpreted relative to the hardware radix, rather than a radix of two. (On a hexadecimal machine, each will have roughly a quarter of the value of the corresponding attribute in Ada 83.) S'Model_Small and S'Model_Epsilon will only change slightly, if at all, because various effects will tend to cancel each other out. In any case, the new names convert what would be upward inconsistencies into upward incompatibilities. We have recommended that implementations continue to provide the old attributes, as implementation-defined attributes, during a transition period, with compiler warning messages when they are used. An Ada 83 program using the Safe_Small or Base'Epsilon attributes should be able to substitute the Model_Small and Model_Epsilon attributes for an equivalent (and logically consistent) effect, but substitutions for the other attributes may require more careful case by case analysis.

It is instructive to consider how the fundamental model-oriented attributes and the Digits attribute of a predefined floating point type P are determined in Ada 95, when the Numerics Annex is implemented. The algorithm is as follows.

P'Model_Epsilon and P'Model_Small are defined in terms of other attributes by familiar formulae. The algorithm for Ada 83, which is not given here, is much more complex and subtle, with more couplings among the attributes.

G.4.2 Fixed Point Arithmetic and Attributes

The revision of the model of fixed point arithmetic focuses on two of the problems concerning fixed point types that have been identified in Ada 83:

The solution adopted in Ada 95 is to remove some of the freedoms of the interval-based accuracy requirements that have never been exploited and to relax the accuracy requirements so as to encourage wider support for fixed point. Applications that use binary scaling and/or carefully matched ("compatible") scale factors in multiplications and divisions, which is typical of sensor-based and other embedded applications, will see no loss of accuracy or efficiency.

A host of specialized requirements for information systems applications is addressed by the division of fixed point types into ordinary and decimal fixed point types. The facilities for the latter are to be found in the Information Systems Annex, see Chapter F.

The default small in Ada 95 is an implementation-defined power of two less than or equal to the delta, whereas in Ada 83 it was defined to be the largest power of two less than or equal to the delta. The purpose of this change is merely to allow implementations that previously used extra bits in the representation of a fixed point type for increased precision rather than for increased range, giving the safe numbers more precision than the model numbers, to continue to do so. An implementation that does so must, however, accept the minor incompatibility represented by the fact that the type's default small will differ from its value in Ada 83. Implementations that used extra bits for extra range have no reason to change their default choice of small, even though Ada 95 allows them to do so.

Note that the simplification of the accuracy requirements that apply in the strict mode, by expressing them directly in terms of integer multiples of the result type's small rather than in terms of model or safe intervals, removes the need for many of the attributes of fixed point types. However, it is recommended that implementations continue to provide these attributes as implementation-defined attributes during a transition period, with their Ada 83 values, and that implementations produce warning messages upon detecting their use.

The accuracy requirements for the adding operators and comparisons now simply say that the result is exact. This was always the case in Ada 83, assuming operands were always safe numbers, and yet it was not clear from the model-interval form of the accuracy requirements that comparison of fixed point quantities was, in practice, deterministic. Other accuracy requirements are now expressed in terms of small sets of allowable results, called "perfect result sets" or "close result sets" depending on the amount of accuracy that it is practical to require. These sets comprise consecutive integer multiples of the result type's small (or of a "virtual" small of 1.0 in the case of multiplication or division giving an integer result type). In some cases, the sets contain a single such multiple or a pair of consecutive multiples; this translates into a requirement that the result be exact, if possible, but never off by more than one rounding error or truncation error. This occurs with fixed point multiplications and divisions in which the operand and result smalls are "compatible" meaning that the product or quotient of the operand smalls (depending on whether the operation is a multiplication or a division) is either an integer multiple of the result small, or vice versa.

These compatible cases cover much of the careful matching of types typically exhibited by sensor-based and other embedded applications, which are intended to produce exact results for multiplications and at- most-one-rounding-error results for divisions, with no extra code for scaling; they can produce the same results in Ada 95, and with the same efficient implementation. Our definition of "compatible" is more general than required just to cover those cases of careful matching of operand and result types, permitting some multiplications that require scaling of the result by at worst a single integer division, with an error no worse than one rounding error.

In cases where the smalls are incompatible, the accuracy requirements are relaxed, in support of Requirement R2.2-A(1); in fact, they are left implementation defined. Implementations need not go so far as to use the Hilfinger algorithms [Hilfinger 90], though they may of course do so. An Ada 95 implementation could, for instance, perform all necessary scaling on the result of a multiplication or division by a single integer multiplication or division (or shifting). That is, the efficiency for the cases of incompatible smalls need not be less than that for the cases of compatible smalls. This relaxation of the requirements is intended to encourage support for a wider range of smalls. Indeed, we considered making support for all smalls mandatory in the strict mode on the grounds that the weaker requirements removed all barriers to practical support for arbitrary smalls, but we rejected it because it would make many existing implementations (which could in all other respects satisfy the requirements of strict mode) instantly nonconforming.

Ada 95 allows an operand of fixed point multiplication or division to be a real literal, named number, or attribute. Since the value v of that operand can always be factored as an integer multiple of a compatible small, the operation must be performed with no more than one rounding error and will cost no more than one integer multiplication or division for scaling. That v can always be factored in this way follows from the fact that it, and the smalls of the other operand and the result, are necessarily all rational quantities.

The accuracy requirements for fixed point multiplication, division, and conversion to a floating point target are left implementation defined (except when the operands' smalls are powers of the target's machine radix) because the implementation techniques described in [Hilfinger 90] rely on the availability of several extra bits in typical floating point representations beyond those belonging to the Ada 83 safe numbers; with the revision of the floating point model, in particular the elimination of the quantization of the mantissa lengths of model numbers, those bits are now likely gone. Except when the operands' smalls are powers of the target's machine radix, requiring model-number accuracy for these operations would demand implementation techniques that are more exacting, expensive, and complicated than those in [Hilfinger 90], or it would result in penalizing the mantissa length of the model numbers of a floating point type just to recover those bits for this one relatively unimportant operation. An implementation may use the techniques in [Hilfinger 90] for fixed point multiplication, division, and conversion to a floating point target; the accuracy achieved will be exactly as in Ada 83, but will simply not be categorizable as model-number accuracy, unless the operands' smalls are powers of the target's hardware radix. Furthermore, in the latter case, even simpler algorithms are available.

G.4.3 Accuracy of the Numerics Packages

The Numerics Annex specifies the accuracy or other performance requirements that the mandatory elementary function and random number packages must satisfy in the strict mode. These are discussed in A.3 with the packages themselves.

G.5 Requirements Summary

The facilities of the Numerics Annex and the floating point attributes of the Predefined Language Environment Annex relate to the requirements in 11.1 (Floating Point).

The requirement

     R11.1-A(1) - Standard Mathematics Packages
is met in part by the Complex types and related packages of the Numerics Annex and in part by the elementary functions and random numbers packages of the Predefined Language Environment Annex.

The study topic

     S11.1-B(1) - Floating Point Facilities
is met by the numeric model presented in the Numerics Annex and by the floating point attributes provided in the Predefined Language Environment Annex.

Copyright | Contents | Index | Previous | Next
Laurent Guerby