This chapter describes a grab-bag of miscellaneous linker techniques that don't fit very well anywhere else.
The second is global initializers and destructors, routines that need to be run before the main routine starts and after the main routine exits. This requires that the linker collect the pieces of initializer and destructor code, or at least pointers to them, into one place so that startup and exit code can run it all.
The third, and by far the most complex issue involves templates and "extern inline" procedures. A C++ template defines an infinite family of procedures, with each family member being the template specialized by a type. For example, a template might define a generic hash table, with family members being a hash table of integers, of floating point numbers, of character strings, and of pointers to various sorts of structures. Since computer memories are finite, the compiled program needs to contain all of the members of the family that are actually used in the program, but shouldn't contain any others. If the C++ compiler takes the traditional approach of treating each source file separately, it can't tell when it compiles a file that uses templates whether some of the template family members are used in other source files. If the compiler takes a conservative approach and generates code for each family member used in each file, it will usually end up with multiple copies of each family member, wasting space. If it doesn't generate that code, it risks having no copy at all of a required family member.
Inline functions present a similar problem. Normally, inline functions are expanded like macros, but in some cases the compiler generates a conventional out-of-line version of the function. If several different files use a single header file that contains an inline function and some of them require an out-of-line version, the same problem of code duplication arises.
Some compilers have used approaches that change the source language to help produce object code that can be linked by ``dumb'' linkers. Many recent C++ systems have addressed the problem head-on, either by making the linker smarter, or by integrating the linker with other parts of the program development system. We look briefly at these latter approaches.
Figure 1: Trial linking
input files pass through linker to trial output plus errors, then inputs plus info from errors plus maybe more generated objects pass through linker to final object
On Unix systems, if the linker can't resolve all of the undefined references in a link job, it can still optionally can also produce an output file which can be used as the input to a subsequent link job. The linker uses its usual library search rules during the link, so the output file contains needed library routines as well as information from the input file. Trial linking solves all of the C++ problems above in a slow but effective way.
For global initializers and destructors, the C++ compiler creates in each input file routines that do the initialization and destruction. The routines are logically anonymous, but the compiler gives them distinctive names. For example, the GNU C++ compiler creates routines named _GLOBAL_.I.__4junk and _GLOBAL_.D.__4junk to do initialization and destruction of variables in a class called junk. After the trial link, the linker driver examines the symbol table of the output file and makes lists of the global initializer and destructor routines, writes a small source file with those lists in arrays (in either C or assembler). Then in the relink the C++ startup and exit code uses the contents of the arrays to call all of the appropriate routines. This is essentially the same thing that C++-aware linkers do, just implemented outside the linker.
For templates and extern inlines, the compiler initially doesn't generate any code for them at all. The trial link has undefined symbols for all of the templates and extern inlines actually used in the program, which the compiler driver can use to re-run the compiler and generate code for them, then re-link.
One minor issue is to find the source code for the missing templates, since it can be lurking in any of a potentially very large number of source files. Cfront used a simple ad-hoc technique, scanning the header files, and guessing that a template declared in foo.h is defined in foo.cc. Recent versions of GCC use a ``repository'' that notes the locations of template definitions in small files created during the compilation process. After the trial link, the compiler driver needs only scan those small files to find the source to the templates.
Figure 2: Duplicate elimination
Input files with redundant sections pass into the linker which collapses them into a single result (sub)section
This approach does a good job of producing executables with one copy of each routine, at the cost of very large object files with many copies of templates. It also offers at least the possibility of smaller final code than the other approaches. In many cases, code generated when a template is expanded for different types is identical. For example, a template that implemented a bounds-checked array of <TYPE> would generally expand to identical code for all pointer types, since in C++ pointers all have the same representation. A linker that's already deleting redundant sections could check for sections with identical contents and collapse multiple identical sections to one. Some Windows linkers do this.
IBM mainframes have always had a ``linkage editor,'' rather than a linker. In the IBM object format, the segments in each input file (IBM calls the segments control sections or CSECTs) retain their individual identities in the output file. One can re-edit a linked program and replace or delete control sections. This feature was widely used in the 1960s and early 1970s when compiling and linking were slow enough that it was worth the manual effort needed to arrange to relink a program, replacing just the CSECTS that had been recompiled. The replacement CSECTs need not be the same size as the originals; the linker adjusts all of the relocation information in the output file as needed to account for the different locations of CSECTs than have moved.
In the mid to late 1980s, Quong and Linton at Stanford did experiments with incremental linking in a UNIX linker, to try and speed up the compile-link-debug cycle. The first time their linker runs, it links a conventional statically linked executable, then stays active in the background as a daemon with the program's symbol table remaing in memory. On subseqent links, it only treats the input files that have changed, replacing their code in-place in the output file but leaving everything else alone other than fixing up references to symbols that have moved. Since segment sizes in the recompiled files usually don't change very much from one link to the next, they build the initial version of the output file with a small amount of slop space between the input file segments, Figure 3. On each subsequent link, so long as the changed input files' segments haven't grown more than the slop amount, the changed files' segments replace the previous versions in the output file. If they have grown past the end of the slop space, the linker moves the subsequent segments in the output file using their slop space. If more than a small number of segments need to be moved, the linker gives up and relinks from scratch.
Figure 3: Incremental linking
picture of inclink-ed object file with slop between segments, and new version's segments pointing to replace old ones
The authors did considerable instrumentation to collect data on the number of files compiled between linker runs in typical development activities and the change in segment sizes. They found that typically only one or two files change, and the segments grow only by a few bytes if at all. By putting 100 bytes of slop between segments, they avoided almost all relinking. They also found that creating the output file's symbol table, which is essential for debugging, was as much work as creating the segments, and used similar techniques to update the symbol table incrementally. Their performance results were quite dramatic, with links that took 20 or 30 seconds to do conventionally dropping to half a second for an incremental link. The primary drawback of their scheme was that the linker used about eight megabytes to keep all of the symbols and other information about the output file, which at the time was a lot of memory (workstations rarely had more than 16MB.)
Some modern systems do incremental linking in much the same way that Quong and Linton did. The linker in Microsoft's visual studio links incrementally by default. It leaves slop between modules and also can in some circumstances move an updated moduls from one part of the executable to another, putting in some glue code at the old address.
Most program source and object files contain more than one procedure. If a compiler marks the boundaries between procedures, the linker can determine what symbols each procedure defines, and what symbols each procedure references. Any procedure with no references at all is unused and can safely be discarded. Each time a procedure is discarded, the linker should recompute the def/ref list, since the procedure just discarded might have had the only reference to some other procedure which can in turn be discarded.
One of the earlier systems to do link-time garbage collection is IBM's AIX. The XCOFF object files put each procedure in a separate section. The linker uses symbol table entries to tell what symbols are defined in each section, and relocation entries to tell what symbols are referenced. By default, all unreferenced procedures are discarded, although the programmer can use linker switches to tell it not to garbage collect at all, or to protect specific files or sections from collection.
Several Windows linkers, including Codewarrior, the Watcom linker, and linker in recent versions of Microsoft's Visual C++ can also garbage collect. A optional compiler switch creates objects with "packaged" functions, each procedure in a separate section of the object file. The linker looks for sections with no references and deletes them. In most cases, the linker looks at the same time for multiple procedures with identical contents (usually from template expansions, mentioned above) and collapses them as well.
An alternative to a garbage collecting linker is more extensive use of libraries. A programmer can turn each of the object files linked into a program into a library with one procedure per library member, then link from those libraries so the linker pulls in procedures as needed, but skips the ones with no references. The hardest part is to make each procedure a separate object file. It typically requires some fairly messy preprocessing of the source code to break multi-procedure source files into several small single procedure files, replicating the the data declarations and "include" lines for header files in each one, and renaming internal procedures to prevent name collisions. The result is a minimum size executable, at the cost of considerably slower compiling and linking. This is a very old trick; the DEC TOPS-10 assembler in the late 1960s could be directed to generate an object file with multiple independent sections that the linker would treat as a searchable library.
A more aggressive approach is to perform standard global optimizations on an entire program at link time. Srivastava and Wall wrote an optimizing linker that decompiled RISC architecture object code into an intermediate form, applied high-level optimizations such as inlining and low-level optimizations such as substituting a faster but more limited instruction for a slower and more general one, then regenerated the object code. Particularly on 64 bit architectures, the speedups from these optimizations can be quite significant. On the 64 bit Alpha architecture, the general way to address any static or global data, or any procedure, is to load an address pointer to the item from a pointer pool in memory into a register, then use the register as a base register. (The pointer pool is addressed by a global pointer register.) Their OM optimizing linker looked for situations where a sequence of instructions refer to several global or static variables that are located close enough to each other that they can all be addressed relative to the same pointer, and rewrites object code to remove many pointer loads from the global pool. It also looks for procedure calls that are within the 32 bit address range of the branch-to-subroutine instruction and substitutes that for a load and indirect call. It also can rearrange the allocation of common blocks to place small blocks togther, to increase the number of places where a single pointer can be used for multiple references. Using these and some other standard optimizations, OM achieves significant improvements in executables, removing as many as 11% of all instructions in some of the SPEC benchmarks.
The Tera computer compilation suite does very aggressive link time optimization to support the Tera's high-performance highly parallel architecture. The C compiler is little more than a parser that creates "object files" containing tokenized versions of the source code. The linker resolves all of the references among modules and generates all of the object code. It aggressively in-lines procedures, both within a single module and among modules, since the code generator handles the entire program at once. To get reasonable compilation performance, the system uses incremental compilation and linking. On a recompile, the linker starts with the previous version of the executable, rewrites the code for the source files that have changed (which, due to the optimization and in-lining, may be in code generated from files that haven't changed) and creates a new, updated, executable. Few of the compilation or linking techniques in the Tera system are new, but to date it's unique in its combination of so many aggressive optimization techniques in a single system.
Other linkers have done other architecture-specific optimizations. The Multiflow VLIW machine had a very large number of registers, and register saves and restores could be a major bottleneck. An experimental tool used profile data to figure out what routines frequently called what other routines. It modified the registers used in the code to minimize the overlapping registers used by both a calling routine and its callee, thereby minimizing the number of saves and restores.
The Srivastava and Wall optimizing linker starts by decompiling object files back into intermediate code. In most cases, if the linker wants intermediate code, it'd be just as easy for compilers to skip the code generation step, create object files of intermediate code, and let the linker do the code generation. That's actually what the Fernandez optimizer described above did. The linker can take all the intermediate code, do a big optimization pass over it, then generate the object code for the output file.
There's a couple of reasons that production linkers rarely do code generation from intermediate code. One is that intermediate languages tend to be related to the compiler's source language. While it's not too hard to devise an intermediate language that can handle several Fortran-like languages including C and C++, it's considerably harder to devise one that can handle those and also handle less similar languages such as Cobol and Lisp. Linkers are generally expected to link object code from any compiler or assembler, making language-specific intermediates problematical.
Cohn et al. at DEC wrote Spike, a Windows optimization tool for Alpha NT executables. It performed both instrumentation, to add profiling code to executables and DLLs, as well as optimization, using the profile data to improve register allocation and to reorganize executables to improve cache locality.
Assembler is close enough to machine language that any compiler can generate it, while still being high enough level to permit useful optimizations including dead code elimination, code rearrangement, and some kinds of strength reduction, as well as standard assembler optimization such as choosing the smallest version of an instruction that has enough bits to handle a particular operand.
Such a system could be fast, since assembly can be very fast, particularly if the object language is really a tokenized assembler rather than full assembler source. (In assemblers, as in most othter compilers, the initial tokenizing is often the slowest part of the entire process.)
Slim binaries were originally created to support Oberon, a strongly typed Pascal-like language, on the Macintosh and later Windows for the x86, and they apparently work quite well on those platforms. The authors also expect that slim binaries will work equally well with other source languages and other architectures. This is a much less credible claim; Oberon programs tend to be very portable due to the strong typing and the consistent runtime environment, and the three target machines are quite similar with identical data and pointer formats except for byte order on the x86. A long series of "universal intermediate language" projects dating back to the UNCOL project in the 1950s have failed after promising results with a small number of source and target languages, and there's no reason to think that slim binaries wouldn't meet the same result. But as a distribution format for a set of similar target environments, e.g. Macs with 68K or PPC, or Windows with x86, Alpha, or MIPS, it should work well.
The IBM System/38 and AS/400 have used a similar technique for many years to provide binary program compatibility among machines with different hardware architectures. The defined machine language for the S/38 and AS/400 is a virtual architecture with a very large single level address space, never actually implemented in hardware. When a S/38 or AS/400 binary program is loaded, the loader translates the virtual code into the actual machine code for whatever processor the machine on which it is running contains. The translated code is cached to speed loading on subsequent runs of the program. This has allowed IBM to evolve the S/38 and then AS/400 line from a midrange system with multi-board CPUs to a deskside system using a power PC CPU, maintaining binary compatibility throughout. The virtual architecture is very tightly specified and the translations very complete, so programers can debug their program at the virtual architecture level without reference to the physical CPU. This scheme probably wouldn't have worked without a single vendor's complete control over the virtual architecture and all of the models of the computers on which it runs, but it's a very effective way to get a lot of performance out of modestly priced hardware.
Java organizes a program into classes, with each class in a program compiled into a separate logical (and usually physical) binary object code file. Each class defines the fields that each class members contains, possibly some static variables, and a set of procedures (methods) that manipulate class members. Java uses single inheritance, so each class is a subclass of some other class, with all classes being desendants from the universal base class Object. A class inherits all of the fields and methods from its superclass, and can add new fields and methods, possibly overriding existing methods in the superclass.
Java loads one class at a time. A Java program starts by loading an initial class in an implementation-dependent way. If that class refers to other classes, the other classes are loaded on demand when they are needed. A Java application can either use the built-in bootstrap class loader which loads clases from files on the local disk, or it can provide its own class loader which can create or retrieve classes any way it wants. Most commonly a custom class loader retrieves class files over a network connection, but it could equally well generate code on the fly or extract code from compressed or encrypted files. When a class is loaded due to a reference from another class, the system uses same loader that loaded the referring class. Each class loader has its own separate name space, so even if an application run from the disk and one run over the net have identically named classes or class members, there's no name collision.
The Java definition specifies the loading and linking process in considerable detail. When the virtual machine needs to use a class, first it loads the class by calling the class loader. Once a class is loaded, the linking process includes verification that the binary code is valid, and preparation, allocating the static fields of the class. The final step of the process is initialization, running any routines that initialize the static fields, which happens the first time that an instance of the class is created or a static function of the class is run.
Figure 4: Loading and linking a Java class file
crawling up and down the tree
The next step, verification, makes a variety of static correctness checks, such as ensuring that each virtual instruction has a valid opcode, that the target of each branch is a valid instruction, and that each instruction handles the appropriate data type for the values it references. This speeds program execution since these checks need not be made when the code is run. If verification finds errors, it throws an exception. Then preparation allocates storage for all of the static members of the class, and intitializes them to standard default values, typically zero. Most Java implementations create a method table at this point that contains pointers to all of the methods defined for this class or inherited from a superclass.
The final stage of Java linking is resolution, which is analogous to dynamic linking in other languages. Each class includes a constant pool that contains both conventional constants such as numbers and strings, and the references to other classes. All references in a compiled class, even to its superclass, are symbolic, and are resolved after the class is loaded. (The superclass might have been changed and recompiled after the class was, which is valid so long as every field and method to which the class refers remains defined in a compatible way.) Java allows implementations to resolve references at any time from the moment after verification, to the moment when an instruction actually uses the reference, such as calling a function defined in a superclass or other class. Regardless of when it actually resolves a reference, a failed reference doesn't cause an exception until it's used, so the program behaves as though Java uses lazy just-in-time resolution. This flexibility in resolution time permits a wide variety of possible implementations. One that translated the class into native machine code could resolve all of the references immediately, so the addresses and offsets could be embedded into the translated code, with jumps to an exception routine at any place where a reference couldn't be resolved. A pure interpreter might instead wait and resove references as they're encountered as the code is interpreted.
The effect of the loading and linking design is that classes are loaded and resolved as needed. Java's garbage collection applies to classes the same as it applies to all other data, so if all references to a class are deleted, the class itself can get unloaded.
The Java loading and linking model is the most complex of any we've seen in this book. But Java attempts to satisfy some rather contradictory goals, portable type-safe code and also reasonably fast execution. The loading and linking model supports incremental loading, static verification of most of the type safety criteria, and permits class-at-a-time translation to machine code for systems that want programs to run fast.
Look at the generated code from a compiler for C++ or another object oriented language. How much better could a link time optimizer make it? What info could the compiler put in the object module to make it easier for the linker to do interesting optimizations? How badly do shared libraries mess up this plan?
Sketch out a tokenized assembler language for your favorite CPU to use as an object language. What's a good way to handle symbols in the program?
The AS/400 uses binary translation to provide binary code compatibility among different machine models. Other architectures including the IBM 360/370/390, DEC VAX, and Intel x86 use microde to implement the same instruction set on different underlying hardware. What are the advantages of the AS/400 scheme? Of microcoding? If you were defining a computer architecture today, which would you use?
Improve the garbage collector to make it iterative. After each pass, update the def/ref structure to remove references from logically deleted segments and run it again, repeating until nothing is deleted.