Abstract. Retargeting a compiler’s back end to a new architecture is a time-consuming process. This becomes an evident problem in the area of programmable graphics hardware (graphics processing units, GPUs) or embedded processors, where architectural changes are faster than elsewhere. We propose the object-oriented rewrite system OORS to overcome this problem. Using the OORS language, a compiler developer can express the code generation and optimization phase in terms of costannotated rewrite rules supporting complex non-linear matching and replacing patterns. Retargetability is achieved by organizing rules into profiles, one for each supported target architecture. Featuring a rule and profile inheritance mechanism, OORS makes the reuse of existing specification possible. This is an improvement regarding traditional approaches. Altogether OORS increases the maintainability of the compiler’s back end and thus both decreases the complexity and reduces the effort of the retargeting process. To show the potential of this approach, we have implemented a code generation and a code optimization pattern matcher supporting different target architectures using the OORS language and introduced them in a GPU compiler. 1

We show how to generate the back end of an optimizing compiler from a formal description of the syntax and semantics of machine instructions. Our generated back ends for x86, ARM, and PowerPC perform as well as their hand-written counterparts. Automatic generation is enabled by two new ideas: a model of machine-level computation that reduces back-end generation to the problem of finding implementations of about a hundred simple, machine-level operations; and an algorithm that finds these implementations by combining machine instructions. 1.

Despite years of work on retargetable compilers, creating a good, reliable back end for an optimizing compiler still entails a lot of hard work. Moreover, a critical component of the back end—the instruction selector—must be written by a person who is expert in both the compiler’s intermediate code and the target machine’s instruction set. By generating the instruction selector from declarative machine descriptions we have (a) made it unnecessary for one person to be both a compiler expert and a machine expert, and (b) made creating an optimizing back end easier than ever before. Our achievement rests on two new results. First, finding a mapping from intermediate code to machine code is an undecidable problem. Second, using heuristic search, we can find mappings for machines of practical interest, in at most a few minutes of CPU time. Our most significant new idea is that heuristic search should be controlled by algebraic laws. Laws are used not only to show when a sequence of instructions implements part of an intermediate code, but also as the primary heuristic for limiting the search: we drop a sequence of instructions not when it gets too long or when it computes too complicated a result, but when too much reasoning will be required to show that the result computed might be useful. 1.

Despite years of work on retargetable compilers, creating a good, reliable back end for an optimizing compiler still entails a lot of hard work. Moreover, a critical component of the back end—the instruction selector—must be written by a person who is expert in both the compiler’s intermediate code and the target machine’s instruction set. By generating the instruction selector from declarative machine descriptions we have (a) made it unnecessary for one person to be both a compiler expert and a machine expert, and (b) made creating an optimizing back end easier than ever before. Our achievement rests on two new results. First, finding a mapping from intermediate code to machine code is an undecidable problem. Second, using heuristic search, we can find mappings for machines of practical interest in at most a few minutes of CPU time. Our most significant new idea is that heuristic search should be controlled by algebraic laws. Laws are used not only to show when a sequence of instructions implements part of an intermediate code, but also to limit the search: we drop a sequence of instructions not when it gets too long or when it computes too complicated a result, but when too much reasoning will be required to show that the result computed might be useful.

Programming languages, compilers, and systems

Abstract. Retargeting a compiler’s back end to a new architecture is a time-consuming process. This becomes an evident problem in the area of programmable graphics hardware (graphics processing units, GPUs) or embedded processors, where architectural changes are faster than elsewhere. We propose the object-oriented rewrite system OORS to overcome this problem. Using the OORS language, a compiler developer can express the code generation and optimization phase in terms of costannotated rewrite rules supporting complex non-linear matching and replacing patterns. Retargetability is achieved by organizing rules into profiles, one for each supported target architecture. Featuring a rule and profile inheritance mechanism, OORS makes the reuse of existing specification possible. This is an improvement regarding traditional approaches. Altogether OORS increases the maintainability of the compiler’s back end and thus both decreases the complexity and reduces the effort of the retargeting process. To show the potential of this approach, we have implemented a code generation and a code optimization pattern matcher supporting different target architectures using the OORS language and introduced them in a GPU compiler. 1