Abstract:
A system is presented that selectively enables expression folding during compilation of a program, wherein the compilation converts the program from source code into executable code. The system operates by forming an expression tree for an expression within the source code which includes an assignment operator. If the assignment operator is a first assignment operator that is a value assignment, only the computed value can be used in subsequent expressions, thereby disabling expression folding during the compilation process. On the other hand, if the assignment operator is a second assignment operator that specifies an expression assignment, the entire expression can be used in place of the variable on the left of the expression assignment, thereby enabling expression folding during the compilation process. The expression can include a mathematical interval. The expression folding can involve substituting a first expression for a variable within a second expression, and then simplifying the result.

Description:
RELATED APPLICATION 
     The application hereby claims priority under 35 U.S.C. §119 to U.S. Provisional Patent Application No. 60/163,372 filed on Nov. 3, 1999, and to U.S. Provisional Patent Application No. 60/211,455 filed on Jun. 13, 2000. 
    
    
     BACKGROUND 
     1. Field of the Invention 
     The present invention relates to compilers for programming languages. More specifically, the present invention relates to a method and an apparatus for selectively enabling expression folding during the compilation process by using a first assignment operator to specify an expression assignment and a second assignment operator to specify a value assignment. 
     2. Related Art 
     Rapid advances in computing technology make it possible to perform trillions of computational operations each second. This tremendous computational speed makes it practical to perform computationally intensive tasks as diverse as predicting the weather and optimizing the design of an aircraft engine. Such computational tasks are typically performed using machine-representable floating-point numbers to approximate values of real numbers. (For example, see the Institute of Electrical and Electronics Engineers (IEEE) standard 754 for binary floating-point numbers.) 
     In spite of their limitations, floating-point numbers are generally used to perform most computational tasks. 
     One limitation is that machine-representable floating-point numbers have a fixed-size word length, which limits their accuracy. Note that a floating-point number is typically encoded using a 32, 64 or 128-bit binary number, which means that there are only 2 32 , 2 64  or 2 128  possible symbols that can be used to specify a floating-point number. Hence, most real number values can only be approximated with a corresponding floating-point number. This creates estimation errors that can be magnified through even a few computations, thereby adversely affecting the accuracy of a computation. 
     A related limitation is that floating-point numbers contain no information about their accuracy. Most measured data values include some amount of error that arises from the measurement process itself. This error can often be quantified as an accuracy parameter, which can subsequently be used to determine the accuracy of a computation. However, floating-point numbers are not designed to keep track of accuracy information, whether from input data measurement errors or machine rounding errors. Hence, it is not possible to determine the accuracy of a computation by merely examining the floating-point number that results from the computation. 
     Interval arithmetic has been developed to solve the above-described problems. Interval arithmetic represents numbers as intervals specified by a first (left) endpoint and a second (right) endpoint. For example, the interval [a, b], where a&lt;b, is a closed, bounded subset of the real numbers, R, which includes a and b as well as all real numbers between a and b. Arithmetic operations on interval operands (interval arithmetic) are defined so that interval results always contain the entire set of possible values. The result is a mathematical system for rigorously bounding numerical errors from all sources, including measurement data errors, machine rounding errors and their interactions. (Note that the first endpoint normally contains the “infimum”, which is the largest number that is less than or equal to each of a given set of real numbers. Similarly, the second endpoint normally contains the “supremum”, which is the smallest number that is greater than or equal to each of the given set of real numbers.) 
     However, computer systems are presently not designed to efficiently handle intervals and interval computations. Consequently, performing interval operations on a typical computer system can be hundreds of times slower than performing conventional floating-point operations. In addition, without a special representation for intervals, interval arithmetic operations fail to produce results that are as narrow as possible. 
     What is needed is a method and an apparatus for efficiently performing arithmetic operations on intervals with results that are as narrow as possible. (Interval results that are as narrow as possible are said to be “sharp”.) 
     One problem in performing interval computations arises from optimizations that are often performed during the compilation process. One common optimization is “expression folding,” in which a first expression is substituted for a variable within a second expression and the resulting second expression is simplified. For example, if a program includes the instruction X=A+B, followed by the instruction Z=X−A, some compilers will substitute A+B for X in the expression for Z and will simplify, Z=(A+B)−A=B. As can be seen from this simple example, expression folding can potentially eliminate unnecessary computational operations. 
     However, expression folding can also create problems, especially for interval computations. For example, suppose a program must compute the summation of a number of intervals x i .        X   =       ∑     i   =   1     n                     x   i                              
     Next, suppose that the program subsequently computes Y j =X−x j . If the compiler performs expression folding by substituting the summation for X, and then simplifies to eliminate x j , the program ends up computing the following partial sum.          Y   j     =       ∑       i   =   1       i   ≠   j       n                     x   i                              
     Note that computing this partial sum is much slower than simply computing Y j =X−x j , which simply involves performing a single subtraction operation. Hence, when possible, it is desirable not to use expression folding. However, note that if x j &gt;&gt;Y j , expression folding prevents rounding errors caused by a large x j  from undermining the accuracy of Y j . In this case it is desirable to use expression folding. 
     Hence, what is needed is a method and an apparatus that facilitates expression folding in cases where expression folding is advantageous, and that facilitates disabling expression folding in cases where expression folding is not advantageous. 
     SUMMARY 
     One embodiment of the present invention provides a system that selectively enables expression folding during compilation of a program, wherein the compilation converts the program from source code into executable code. The system operates by forming an expression tree for an expression within the source code which includes an assignment operator. If the assignment operator is a first assignment operator that is a value assignment, only the computed value can be used in subsequent expressions, thereby disabling expression folding during the compilation process. On the other hand, if the assignment operator is a second assignment operator that specifies an expression assignment, the entire expression can be used in place of the variable on the left of the expression assignment, thereby enabling expression folding during the compilation process. 
     In one embodiment of the present invention, the expression includes a mathematical interval. 
     In one embodiment of the present invention, the expression folding involves substituting of a first expression for a variable within a second expression, and then simplifying the resulting second expression through mathematically-equivalent symbolic manipulation. 
     In one embodiment of the present invention, the first assignment operator is represented as “:=” within the source code. 
     In one embodiment of the present invention, the second assignment operator is represented as “=” within the source code. 
     In one embodiment of the present invention, if the assignment operator is an expression assignment operator, expression folding is required if the expression on the right-hand-side of the expression assignment operator contains a non-degenerate literal interval constant. 
    
    
     BRIEF DESCRIPTION OF THE FIGURES 
     FIG. 1 illustrates a computer system in accordance with an embodiment of the present invention. 
     FIG. 2 illustrates the process of compiling and using code for interval computations in accordance with an embodiment of the present invention. 
     FIG. 3 illustrates an arithmetic unit for interval computations in accordance with an embodiment of the present invention. 
     FIG. 4 is a flow chart illustrating the process of performing an interval computation in accordance with an embodiment of the present invention. 
     FIG. 5 illustrates four different interval operations in accordance with an embodiment of the present invention. 
     FIG. 6 illustrates a compiler for interval code in accordance with an embodiment of the present invention. 
     FIG. 7 is a flow chart illustrating how different assignment operators are used to selectively enable expression folding in accordance with an embodiment of the present invention. 
    
    
     DETAILED DESCRIPTION 
     The following description is presented to enable any person skilled in the art to make and use the invention, and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present invention. For example, in different computer languages, different symbols may be more appropriate to distinguish the value and expression assignment operators. Thus, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein. 
     The data structures and code described in this detailed description are typically stored on a computer readable storage medium, which may be any device or medium that can store code and/or data for use by a computer system. This includes, but is not limited to, magnetic and optical storage devices such as disk drives, magnetic tape, CDs (compact discs) and DVDs (digital versatile discs or digital video discs), and computer instruction signals embodied in a transmission medium (with or without a carrier wave upon which the signals are modulated). For example, the transmission medium may include a communications network, such as the Internet. 
     Computer System 
     FIG. 1 illustrates a computer system  100  in accordance with an embodiment of the present invention. As illustrated in FIG. 1, computer system  100  includes processor  102 , which is coupled to a memory  112  and a peripheral bus  110  through bridge  106 . Bridge  106  can generally include any type of circuitry for coupling components of computer system  100  together. 
     Processor  102  can include any type of processor, including, but not limited to, a microprocessor, a mainframe computer, a digital signal processor, a personal organizer, a device controller and a computational engine within an appliance. Processor  102  includes an arithmetic unit  104 , which is capable of performing computational operations using floating-point numbers. 
     Processor  102  communicates with storage device  108  through bridge  106  and peripheral bus  110 . Storage device  108  can include any type of non-volatile storage device that can be coupled to a computer system. This includes, but is not limited to, magnetic, optical, and magneto-optical storage devices, as well as storage devices based on flash memory and/or battery-backed up memory. 
     Processor  102  communicates with memory  112  through bridge  106 . Memory  112  can include any type of memory that can store code and data for execution by processor  102 . As illustrated in FIG. 1, memory  112  contains computational code for intervals  114 . Computational code  114  contains instructions for the interval operations to be performed on individual operands, or interval values  115 , which are also stored within memory  112 . This computational code  114  and these interval values  115  are described in more detail below with reference to FIGS. 2-5. 
     Note that although the present invention is described in the context of computer system  100  illustrated in FIG. 1, the present invention can generally operate on any type of computing device that can perform computations involving floating-point numbers. Hence, the present invention is not limited to the computer system  100  illustrated in FIG.  1 . 
     Compiling and Using Interval Code 
     FIG. 2 illustrates the process of compiling and using code for interval computations in accordance with an embodiment of the present invention. The system starts with source code  202 , which specifies a number of computational operations involving intervals. Source code  202  passes through compiler  204 , which converts source code  202  into executable code form  206  for interval computations. Processor  102  retrieves executable code  206  and uses it to control the operation of arithmetic unit  104 . 
     Processor  102  also retrieves interval values  115  from memory  112  and passes these interval values  115  through arithmetic unit  104  to produce results  212 . Results  212  can also include interval values. 
     Note that the term “compilation” as used in this specification is to be construed broadly to include pre-compilation and just-in-time compilation, as well as use of an interpreter that interprets instructions at run-time. Hence, the term “compiler” as used in the specification and the claims refers to pre-compilers, just-in-time compilers and interpreters. 
     Arithmetic Unit for Intervals 
     FIG. 3 illustrates arithmetic unit  104  for interval computations in more detail accordance with an embodiment of the present invention. Details regarding the construction of such an arithmetic unit are well known in the art. For example, see U.S. patent application Ser. Nos. 5,687,106 and 6,044,454, which are hereby incorporated by reference in order to provide details on the construction of such an arithmetic unit. Arithmetic unit  104  receives intervals  302  and  312  as inputs and produces interval  322  as an output. 
     In the embodiment illustrated in FIG. 3, interval  302  includes a first floating-point number  304  representing a first endpoint of interval  302 , and a second floating-point number  306  representing a second endpoint of interval  302 . Similarly, interval  312  includes a first floating-point number  314  representing a first endpoint of interval  312 , and a second floating-point number  316  representing a second endpoint of interval  312 . Also, the resulting interval  322  includes a first floating-point number  324  representing a first endpoint of interval  322 , and a second floating-point number  326  representing a second endpoint of interval  322 . 
     Note that arithmetic unit  104  includes circuitry for performing the interval operations that are outlined in FIG.  5 . This circuitry enables the interval operations to be performed efficiently. 
     However, note that the present invention can also be applied to computing devices that do not include special-purpose hardware for performing interval operations. In such computing devices, compiler  204  converts interval operations into a executable code that can be executed using standard computational hardware that is not specially designed for interval operations. 
     FIG. 4 is a flow chart illustrating the process of performing an interval computation in accordance with an embodiment of the present invention. The system starts by receiving a representation of an interval, such as first floating-point number  304  and second floating-point number  306  (step  402 ). Next, the system performs an arithmetic operation using the representation of the interval to produce a result (step  404 ). The possibilities for this arithmetic operation are described in more detail below with reference to FIG.  5 . 
     Interval Operations 
     FIG. 5 illustrates four different interval operations in accordance with an embodiment of the present invention. These interval operations operate on the intervals X and Y. The interval X includes two endpoints, 
       x  denotes the lower bound of X, and 
     {overscore (x)} denotes the upper bound of X. 
     The interval X is a closed, bounded subset of the real numbers R (see line  1  of FIG.  5 ). Similarly the interval Y also has two endpoints and is a closed, bounded subset of the real numbers R (see line  2  of FIG.  5 ). 
     Note that an interval is a point or degenerate interval if X=[x, x]. Also note that the left endpoint of an interior interval is always less than or equal to the right endpoint. The set of extended real numbers, R* is the set of real numbers, R, extended with the two ideal points minus infinity and plus infinity:          R   *   R     ⋃     {     -   ∞     }     ⋃       {     +   ∞     }     .                            
     In the equations that appear in FIG. 5, the up arrows and down arrows indicate the direction of rounding in the next and subsequent operations. Directed rounding (up or down) is applied if the result of a floating-point operation is not machine-representable. 
     The addition operation X+Y adds the left endpoint of X to the left endpoint of Y and rounds down to the nearest floating-point number to produce a resulting left endpoint, and adds the right endpoint of X to the right endpoint of Y and rounds up to the nearest floating-point number to produce a resulting right endpoint. 
     Similarly, the subtraction operation X−Y subtracts the right endpoint of Y from the left endpoint of X and rounds down to produce a resulting left endpoint, and subtracts the left endpoint of Y from the right endpoint of X and rounds up to produce a resulting right endpoint. 
     The multiplication operation selects the minimum value of four different terms (rounded down) to produce the resulting left endpoint. These terms are: the left endpoint of X multiplied by the left endpoint of Y; the left endpoint of X multiplied by the right endpoint of Y; the right endpoint of X multiplied by the left endpoint of Y; and the right endpoint of X multiplied by the right endpoint of Y. This multiplication operation additionally selects the maximum of the same four terms (rounded up) to produce the resulting right endpoint. 
     Similarly, the division operation selects the minimum of four different terms (rounded down) to produce the resulting left endpoint. These terms are: the left endpoint of X divided by the left endpoint of Y; the left endpoint of X divided by the right endpoint of Y; the right endpoint of X divided by the left endpoint of Y; and the right endpoint of X divided by the right endpoint of Y. This division operation additionally selects the maximum of the same four terms (rounded up) to produce the resulting right endpoint. For the special case where the interval Y includes zero, X/Y is an exterior interval that is nevertheless contained in the interval R*. 
     Note that the result of any of these interval operations is the empty interval if either of the intervals, X or Y, are the empty interval. Also note, that in one embodiment of the present invention, extended interval operations never cause undefined outcomes, which are referred to as “exceptions” in the IEEE 754 standard. 
     Compiler for Interval Code 
     FIG. 6 illustrates the internal structure of the compiler  204  for interval code from FIG. 2 in accordance with an embodiment of the present invention. Compiler  204  includes a number of components, including syntactic and semantic analyzer  602 , expression tree generator  604 , optimizer  608  and code generator  610 . 
     Compiler  204  receives source code  202  and passes it through syntactic and semantic analyzer  602  to determine whether or not source code  202  adheres to the rules of the programming language in which it is written. If not, the system outputs an error message. 
     Next, source code  202  is passed through expression tree generator  604 , which converts source code  202  into intermediate form  606 . This intermediate form  606  includes expression trees as is described below with reference to FIGS.  7  and  8 A-E. 
     Intermediate form  606  then passes through optimizer  608 , which makes performance-improving transformations on the code. 
     Finally, the output of optimizer  608  passes through code generator  610 , which produces executable code  206 . Executable code  206  can include code that is written in a native instruction set, as well as platform-independent instructions, such as bytecodes defined in the JAVA™ programming language. (Sun, the Sun logo, Sun Microsystems, and Java are trademarks or registered trademarks of Sun Microsystems, Inc. in the United States and other countries.) 
     Process of Selectively Enabling Expression Folding 
     FIG. 7 is a flow chart illustrating how different assignment operators are used to selectively enable expression folding in accordance with an embodiment of the present invention. Compiler  204  first receives source code  202  (see FIG. 2) (step  702 ). Next, after syntactic and semantic analyses take place, expression tree generator  604  generates expression trees for statements within source code  202  (see FIG.  6 ). This expression tree generation process includes forming an expression tree for a given expression associated with a given assignment operator (step  704 ). 
     If the given assignment operator specifies a value assignment, the system disables expression folding for the given expression during the compilation process (step  706 ). On the other hand, if the given assignment operator specifies an expression assignment, the system enables expression folding for the given expression (step  708 ). 
     In one embodiment of the present invention, the “=” symbol is used to represent an expression assignment. This usage is compatible with existing compilers, which typically uses the “=” symbol to denote an assignment operation, and which typically allow expression folding for all assignment operations under high levels of performance optimization. 
     In one embodiment of the present invention, “:=” is used to represent a value assignment. Although “:=” is a convenient representation, any symbol or string that is not reserved for other purposes within a programming language can be used instead of“:=”. 
     Note that in an interval context, a value assignment, such as X:=[1,2] disallows substitution of the constant expression, [1,2]. In this case, if there are two instances of the variable, X, each instance is associated with the same underlying point variable. Hence, Z=X−X=0. 
     In contrast, an expression assignment, X=[1,2], forces expression substitution. In this case, since X is a placeholder for the constant [1,2], if there are two instances of X, each instance is associated with the same interval constant [1,2]. Hence, Z=X−X=[1,2]−[1−2]=[−1,1]. 
     In another embodiment of the present invention, the two different assignment operators are used within PARAMETER statements in a version of FORTRAN  90  programming language that has been augmented to include the two different assignment operators. In this embodiment, the expression assignment operator, “=”, defines a named constant. In this case, “PARAMETER X=[1,2]” indicates that [1,2] must be substituted for all occurrences of X. 
     In contrast, the value assignment operator, “:=” defines a read-only variable. In this case, “PARAMETER X:=[1,2]” indicates that the read-only variable X can be symbolically manipulated by the compiler because multiple occurrences of the interval variable, X, are dependent. 
     In another example, suppose we have the expression assignment Y=X+[−1,1]. In this case, Z=Y−Y=X+[−1,1]−X−[−1,1]=[−2,2], because X is an interval variable and [−1,1] is an interval constant. 
     On the other hand, suppose we have the value assignment Y:=X+[−1,1]. In this case, Z=Y−Y=[0,0], because every occurrence of the variable, Y, depends on the same underlying point variable. 
     The last example illustrates the fact that expression folding is required after expression assignments if the expression contains non-degenerate literal or named interval constants (step  710 ). 
     The foregoing descriptions of embodiments of the present invention have been presented for purposes of illustration and description only. They are not intended to be exhaustive or to limit the present invention to the forms disclosed. Accordingly, many modifications and variations will be apparent to practitioners skilled in the art. Additionally, the above disclosure is not intended to limit the present invention. The scope of the present invention is defined by the appended claims.