Performing compilation optimisation procedures in an optimisation compiler

Abstract: The present invention relates to a method, in an optimising compiler using specified optimisation procedures for identifying ways of improving the quality of compiled code, for performing preliminary optimising procedures on a program to be compiled, prior to carrying out any specified optimisation procedure. According to the invention the preliminary optimising procedures comprise: (a) developing a control flowgraph representing all possible execution paths for the program; (b) identifying subgraphs in the program; (c) performing the steps of: (1) selecting a subgraph to be examined for the optimisation procedure, the first subgraph on a first iteration being the entire program; (2) by examining the code sequences in the subgraph, determining the number of entities in the subgraph which are relevant to each dimension of arrays used in the specified procedure to represent data flow equations; (3) determining the amount of memory required to contain the arrays; (4) if the amount of memory exceeds a predetermined memory usage limit for the compilation thereby denoting an unsuccessful attempt at the optimisation procedure, applying step (5) to the subgraph, otherwise applying the optimisation procedure to the subgraph, and (5) applying steps (2) to (4) for every subgraph contained in the subgraph for which insufficient memory was found in step (4).

Description: This invention relates to the improvement of the compilation process performed in an optimisation compiler which uses global optimisation algorithms (herein called "specified procedures") to improve the quality of the program object code that it compiles. Optimising compilers are important tools for programmers to improve the effectiveness and efficiency of the target CPU. The goal of an optimising compiler is to generate the smallest and fastest set of object code possible that exactly duplicates the function of the program as it was written, for example in source code. In order to generate compact and efficient object code for computer programs written in high level languages, compilers used for such languages must utilise sophisticated global optimisers which generally use various specified procedures for reducing the run time or the memory requirements of a program. For example, a compiler may perform any or all of: common sub-expression elimination, code motion, strength reduction (replacing a slow operation by an equivalent fast operation), store motion and useless code sequences removal. Descriptions of some of these optimisations can be found in: J. T. Schwartz, On Programming - An Interim Report on the SETL Language. Installment II: The SETL Language and Examples of its Use, Courant Institute of Math Sciences, NYU, 1983, pp. 293-310. E. Morel and C. Renvoise, Global Optimisation by Suppression of Partial Redundancies, CACM, Vol. 22, No. 2, pp. 96-103, 1979. A. Aho and J. Ullman, Principles of Compiler Design, Addison-Wesley, 1977. Each of these optimising specified procedures transforms an intermediate language (IL) program into a semantically equivalent but more efficient IL program. Intermediate level language, as its name implies, lies between a high level source program and machine code in complexity and sophistication. An intermediate level language can be especially useful in preparing compilers that are to be capable of translating any of several high level languages into machine code targeted to any of several machines; it reduces markedly the number of products that must be developed to cover a wide range both of machine types and of programming languages, because all may translate through a common intermediate level language. It is at the intermediate language level that most optimisations are commonly performed. The most important optimisation procedures performed in an optimising compiler are carried out globally, that is, on a program-wide level, rather than on a localised or basic block level. In performing each of these optimisation procedures, a series of data flow equations must be solved. In doing so, the compiler gathers information about the expressions in the program being compiled; such information is dependent upon the flow of control in the program. For its own unique code transformation, each optimisation procedure must have a method of tracking when and how any given expression is available throughout the program as compiled. This information is derived from the control flowgraph (or flow diagram) which is a directed graph depicting the possible execution paths of the program. In a low order flowgraph, the nodes represent basic blocks of a program and these are connected by directed edges representing paths along which control in the program flows. In a high order flowgraph the nodes comprise basic blocks and/or strongly connected regions. In the present specification, the term "basic block" means any set of instructions in a computer program, whether in object or source code, which is a straight-line sequence of code into which branches reach only its first instruction, and from which control leaves the basic block only after the last instruction. The term "strongly connected region" means a set of nodes among which there is a path that can be repeatedly followed by the program control without passing through a node outside the region. Strongly connected regions are well known in the art of compiler design. A "single entry strongly connected region" is a strongly connected region which has only one node that is reached from outside the single entry strongly connected region. Hereafter in this specification, the term "region" means a single entry strongly connected region. The term "subgraph" means any combination of nodes within the flowgraph. All strongly connected regions are also subgraphs, but not all subgraphs are strongly connection regions. The term "entities" refers to the components of an intermediate representation which are used to describe a program as it is being compiled. These include variable entries, dictionary entries, results, expressions, instructions, and basic blocks of a program. The larger and more complex a program is, the larger and more convoluted is its flowgraph, the greater is the number of calculations involved and the greater is the number of expressions for which dataflow equations must be solved. Memory requirements and processing time for the compilation tend to increase quadratically as a function of source program size for global optimisation. When a situation arises where the compiler cannot optimise an entire program because of a space restriction, in the past the optimisation procedure has had to be abandoned. Attempts have been made to improve the quality of optimisation procedures in the past. A small number of patents has been granted on inventions in this area. US-A-4,506,325 discloses a method of decreasing the storage requirements of a compiler by encoding operators and operands using an information theoretic encoding technique, applied to segments of a program. The disclosure does not deal with how a program is segmented. US-A-4,571,678 discloses a method of utilising the limited number of registers in a target computer by improving register allocation procedures. It does not disclose any way of handling large programs that exceeds the general memory availability of the target computer. There remains a need for a program compilation technique that does not simply give up the struggle if an optimisation procedure cannot be performed within the constraints of the hardware or computer on which the program is being compiled. The object of the present invention is to provide an improved method and apparatus for performing optimisation procedures on a program. The present invention as defined in claim 1 relates to a method, in an optimising compiler using specified optimisation procedures for identifying ways of improving the quality of compiled code, for performing preliminary optimising procedures on a program to be compiled, prior to carrying out any specified optimisation procedure. According to the invention the preliminary optimising procedures comprise: (a) developing a control flowgraph representing all possible execution paths for the program; (b) identifying subgraphs in the program; (c) performing the steps of: (1) selecting a subgraph to be examined for the optimisation procedure, the first subgraph on a first iteration being the entire program; (2) by examining the code sequences in the subgraph, determining the number of entities in the subgraph which are relevant to each dimension of arrays used in the specified procedure to represent data flow equations; (3) determining the amount of memory required to contain the arrays; (4) if the amount of memory exceeds a predetermined memory usage limit for the compilation thereby denoting an unsuccessful attempt at the optimisation procedure, applying step (5) to the subgraph, otherwise applying the optimisation procedure to the subgraph, and (5) applying steps (2) to (4) for every subgraph contained in the subgraph for which insufficient memory was found in step (4). The invention also relates to an optimising compiler as defined in claim 10 comprising means for performing the above method. In order that the invention may be more readily understood an embodiment will now be described with reference to the accompanying drawings, in which: Figure 1 is a depiction of a typical flowgraph of a portion of a program, as would be built by a compiler according to the method of the invention, and Figure 2 is a block diagram of data processing apparatus for performing the method of the invention. The first step in implementing the method of compilation to be described is to construct a flowgraph of the program to be optimised. Flowgraph construction is well-known in the art and, for the purposes of the description, can De implemented by standard techniques. Then all of the appropriate subgraphs are identified within the flowgraph. The choice of subgraphs depends on the optimisation procedure to be performed. Strongly connected regions are often useful, but intervals, hammocks and trees can also be used, and, in extremely contained situations, a subgraph can consist of a single node. In the following description the subgraphs are taken to be the collection of strongly connected regions. The number and dimensions of arrays to be formed during the optimisation procedures must be known in order to write the specified procedure for each particular optimisation procedure. Their dimensions will be based upon the quantities of certain entities, for example instructions, results, expressions, dictionary entries and basic blocks in the code sequence. The particular entities that are constant for each subgraph of a program and the amount of memory needed can be easily calculated before the optimisation procedure is performed. The memory limit that is imposed on the compiler can be pre-defined by the compiler writer at one or more levels or it can be dynamically determined as the compiler assesses the system's resource usage. One reason to constrain the amount of memory available to a selected procedure is the need to constrain the execution time of the selected procedure. The memory requirements having been determined for applying the specified procedure to the entire program, the memory requirement is compared with the memory available and, if the available memory is sufficient, then the optimisation procedure is carried out. If the memory is insufficient, then an attempt is made to apply the specified procedure to each of the maximal individual subgraphs within the subgraph for which there was insufficient memory space. For each contained subgraph for which there is still insufficient memory available, its contained subgraphs are optimised; if these contain subgraphs which cannot be optimised, then each of the contained subgraphs will be further broken down in turn into its contained subgraphs. This iteration continues until a subgraph is examined whose optimisation procedure memory requirements are sufficiently small that an optimisation procedure can be carried out within the allotted memory, and the optimisation procedure is then performed. The method of the invention can be used with one or more optimisation procedures, and, when a plurality of optimisation procedures are performed, they may be performed in any sequence. An example of a specific optimisation procedure is the optimisation technique known as common sub-expression elimination and code motion. For this optimisation procedure, the subgraphs into which the flowgraph is broken down are the strongly connected regions. One of the sets of data to be computed is the set of expressions that are available at the exit of each basic block and region within the outermost region being optimised (the "avail" set). In this case, two types of entities must be counted to determine the size of the "avail" set: the number of different expressions, and the number of basic blocks and regions including the region being commoned. The availability information is collected by going through the code in the basic blocks of the outermost region being optimised (i.e. the low order control flowgraph) and marking which expressions are computed and not killed by any subsequent redefinitions of their arguments. This information is then propagated outward through the outer level regions to establish which expressions are available at the exits of every region. After this step, the assumption is made that nothing is available upon entry to the outermost region and this new information is propagated back into the inner regions. The need to process data flow information into and out of the regions of the flowgraph comes from the nature of the loops which form the regions. Clearly, common sub-expression elimination benefits from partitioning for large and complicated programs. The processing time for propagating information into and out of the basic blocks and regions of an entire program can be very large, and, further, the space requirements of the arrays can be large. By partitioning the program according to the method being described, not only the second dimension of the "avail" set, but also its first dimension, are affected. In most cases, the universe representing the set of expressions which appear in an inner region is a subset of the universe for the set of expressions found in its outer regions. Thus, by optimising on the next level of regions in a high order flowgraph, the size of an array can be significantly reduced because it is highly probable that more than one dimension is affected. A preferred embodiment of the invention will now be described with reference to Figure 1, in which a flowgraph representing a portion of a typical program to be compiled is shown and Figure 2, which illustrates data processing apparatus for performing the method. The type of subgraph used is the strongly connected region. Nodes 1 through 15 represent basic blocks of one or more lines of code having one entry and one exit point. The entire flowgraph shown in Figure 1 is a strongly connected region SCR 20; this is defined as being true, even though it does not fit the classical definition because no return path exists from within the flowgraph to node 1. Searching for the next inner strongly connected region can be done by methods known in the art, and is not critical to the method being described. In the present example, node 2 has a path returning from node 12 but no return path from any later node. The next inner strongly connected region in the flowgraph is region SCR 19, including nodes 2 through 12. The next contained strongly connected region within region SCR 19 is analysed similarly. Thus a path can be traced including nodes 3 through 11 repeatedly, and that defines region SCR 18. Similarly, the other two strongly connected regions in the flowgraph of Figure 1 are region SCR 17, consisting of nodes 8, 9 and 10, and region SCR 16, consisting of node 7 which has its own internal return path. Nodes 13 to 15 do not constitute a strongly connected region because there is no return path within the group of nodes that would permit a repeated flow of control without passing to a node outside the group. The method being described is carried out on the program by, first, building the flowgraph shown in Figure 1. Then the entire program is tested for an optimisation procedure. As the compiler works through the entire program, a count of the entities used by the optimising procedure is kept, including the number of entities which determine the size of the arrays which represent the data flow equations. As the counts are incremented, the unit size of each dimension of each array is noted and the total number of elements in the array is calculated. This is then compared with the allotted memory. If the requirements exceed the allotted memory, then the next smaller strongly connected region is found as outlined above, and examined in the same manner. When a region is found that is sufficiently small to enable a successful optimisation procedure to be performed, the optimisation procedure is performed on that region. The control flowgraph may optionally be rebuilt prior to other specified optimising procedures. In some cases, it may be desirable, after a subgraph susceptible of being optimised has been treated, to attempt to optimise a larger subgraph. Since application of the selected procedure has reduced the size of the code in the subgraph to which it was applied, it may now be possible to treat the containing subgraph as a unit. However, such re-examination increases compilation time, whereas the thrust of the invention is to limit the compilation time and space during the optimisation procedure. If carried out, such a step is performed in the same manner as the previous examination of the code sequences, except that successively higher-level regions are examined in turn. Another example of one way of implementing the method described is given by the pseudo-code fragment in Appendix 1. The code is adapted from an actual program, which operates as follows: The optimiser gets as input the memory limits which will constrain the optimisation procedures. It resets the maps which are to be created to describe the universe for the sets of entities to be used to solve the dataflow equations for the specified optimisation procedures, which here are commoning and code motion. It maps and counts the basic blocks which are contained in the given region, and the number of dictionary entries and different symbolic register uses found in the given region. It computes the size of the arrays used by the specified procedures. It compares this total with the allotted memory, and, if the memory requirement exceeds the allotment, then returns the number which corresponds to the region whose nodes were most recently mapped. Else, it finds the regions at the next contained level. If such a region is found then it repeats the steps that were performed previously on the contained region. If the region contains no regions itself, then the optimisation procedure searches for the next region at the same level. If there is no region at the same level, then it looks for regions at the next higher level. It repeats ascending through the flowgraph levels until a region is found or else there are no more levels to ascend. If there are no more levels, it reports to the specified procedure that no regions to be optimised are found in the program. Comments within the code also help to illustrate the method of the invention. The method described may be used on any data processing apparatus using an intermediate level language in the process of compilation, including Reduced Instruction Set Computers (known generically as RISC computers). It may also be used with a compiler that is in turn used with many different computer systems, by adjusting the memory limits to be utilised by the optimiser. By the same means, the user can obtain varied levels of optimisation of the same source program. Because the optimising occurs at the intermediate language level, it can also be used with optimising compilers dealing with any high level language, including Fortran, PL/I, Cobol, C, SETL and Pascal. Through the use of the method described, at least partial optimisation procedures can be performed upon very large programs that would be impossible to optimise because of memory constraints in the computer being used to carry out the optimisation procedures. A further advantage of the method is the alleviation of register pressures which occur when optimising large programs. For example, when many expressions are moved outside of loops, they exceed the number of registers available to contain them. The code which performs register allocation then requires a great deal of processing time and memory space in trying to accommodate these register requirements and may result in having to put register values into temporary storage to be retrieved later when the values are used. With partitioned optimisation procedures in large programs, code movement of the type just described is restricted to inner sub-regions, and, thus, a register's assignment is placed closer to its uses. Whilst this may not be the theoretically optimal choice if an expression which is moved to the beginning of an inner loop is also a constant in the outer region or regions, it frequently avoids the wasted processing of having to reverse the effects of a previous optimisation operation which results in poorer code quality. Figure 2 is a block diagram of data processing apparatus which can be used to perform the above described method. The actual details of the units of the apparatus will be well known to those skilled in the art and do not form part of the invention. They are therefore not described. The data processing apparatus includes a unit 21 for forming a flowgraph of a program to be compiled, a unit 22 for identifying subgraphs contained in the program and a unit 23 for selecting a subgraph to be examined for an optimisation procedure. The apparatus also includes a unit 24 for determining the amount of memory needed to perform the optimisation procedure on the selected subgraph, a unit 25 for determining whether that amount of memory exceeds an amount of memory available. The units may be controlled in a suitable program. The units illustrated in Figure 2 will be associated with an optimisation computer of known type.

Claims: In an optimising compiler using specified optimisation procedures for identifying ways of improving the quality of compiled code, a method of performing preliminary optimising procedures on a program to be compiled prior to carrying out any specified optimisation procedures, said preliminary optimising procedures comprising: a. developing a control flowgraph representing all possible execution paths for said program; b. identifying subgraphs (SCR 16, 17, 18, 19, 20) contained within said program; c. performing the steps of: 1) defining a subgraph to be examined for said optimisation procedure, the first subgraph on a first iteration being the entire program (SCR 20); 2) by examining the code sequences in said subgraph, determining the number of entities in said subgraph which are relevant to each dimension of arrays used in said optimisation procedure to represent data flow equations; 3) determining the amount of memory required to contain said arrays; 4) if said amount of memory exceeds a predetermined memory usage limit for said optimisation procedure thereby denoting an unsuccessful attempt at applying said optimisation procedure, applying step 5) to said subgraph, otherwise applying said optimisation procedure to said subgraph, and 5) for every subgraph contained in said subgraph for which insufficient memory was available as determined in step 4), applying steps 2) to 4). A method as claimed in Claim 1, in which a plurality of specified optimisation procedures are performed. A method as claimed in Claim 1, in which said subgraphs are strongly connected regions within said program. A method as claimed in Claim 1, in which said specified optimisation procedures are selected from the group consisting of code motion, store motion, removing redundant code sequences, removing useless code sequences, and simplifying expressions. A method as claimed in Claim 1, in which said entities are selected from the group consisting of instructions, results, expressions, dictionary entries, regions in a source code program, basic blocks in a source code program and combinations thereof. A method as claimed in Claim 1, wherein the amount of memory required to contain all entities generated by said compiler is calculated by: a) for each array, multiplying the unit size of each dimension to obtain the total number of elements in the array; b) multiplying the number of array elements by the size of one array element to obtain the number of memory units which would be occupied by the array; and c) summing the memory requirements of the arrays to derive the total amount of memory needed by the particular optimisation procedure. A method as claimed in Claim 1, further comprising the step of, after a successful optimisation procedure has been defined and performed, examining a subgraph containing the subgraph for which said successful optimisation procedure was performed to determine whether it can be optimised by said specified procedure. A method as claimed in Claim 1, further comprising the step of, after a successful optimisation procedure has been defined and performed, examining the containing strongly connection region to determine whether it can be optimised by said specified procedure. A method as claimed in Claim 1, further comprising the steps of: a. after a successful optimisation procedure has been defined and performed on at least one inner subgraph, examining its containing subgraph to determine whether it can be optimised by said specified procedure; b. if said containing subgraph can be optimised, examining its containing strongly connected region in the same manner and, if it can be optimised, performing said optimisation. An optimising compiler using specified optimisation procedures for identifying ways of improving the quality of compiled code, and comprising means for performing preliminary optimising procedures on a program to be compiled prior to carrying out any specified optimisation procedure characterised in that said means for performing preliminary optimisation procedures comprises: a) means (21) for developing a control flowgraph representing all possible execution paths for said program; b) means (22) for identifying subgraphs contained within said program; c) means (23) for defining a subgraph to be examined for said optimisation procedure, the first subgraph on a first iteration being the entire program; d) means for, by examining the code sequences in said subgraph, determining the number of entities in said subgraph which are relevant to each dimension of arrays used in said optimisation procedure to represent data flow equations; e) means (24) for determining the amount of memory required to contain said arrays; f) means (25) for, if said amount of memory exceeds a predetermined memory usage limit for said optimisation procedure thereby denoting an unsuccessful attempt at applying said optimisation procedure, applying means g) to said subgraph, and otherwise for applying said optimisation procedure to said subgraph; and g) means for applying means d) e) and f) for every subgraph contained in said subgraph for which insufficient memory was available as determined by means f).

Inventors: JANCZYN JOYCE MARIE, MARKSTEIN PETER WILLY, JANCZYN, JOYCE MARIE, MARKSTEIN, PETER WILLY
Applicants: IBM, INTERNATIONAL BUSINESS MACHINES CORPORATION
Application year: 1990
Main classification: G06F9
IPC codes: G06F9