Storage Use Analysis and its

In this paper we present a new program analysis method which we call Storage Use Analysis. This analysis deduces how objects are used by the program and allows the optimization of their allocation. This analysis can be applied to both statically typed languages (e.g. ML) and latently typed languages (e.g. Scheme). It handles side-eeects, higher order functions, separate compilation and does not require cps transformation. We show the application of our analysis to two important optimizations: stack allocation and unbox-ing. The rst optimization replaces some heap allocations by stack allocations for user and system data storage (e.g. lists, vectors, procedures). The second optimization avoids boxing some objects. This analysis and associated optimizations have been implemented in the Bigloo Scheme/ML compiler. Experimental results show that for many allocation intensive programs we get a signiicant speedup. In particular, numerically intensive programs are almost 20 times faster because oating point numbers are unboxed and no longer heap allocated. 1 Introduction Modern strict functional languages such as Scheme and ML are still often much less eecient than traditional imperative languages such as Fortran and C. Few compilers for functional languages are able to produce executable programs whose eeciency is close to that of imperative ones 12]. To a large extent, this ineeciency is due to poor use of memory. Because read and write operations are much more expensive than arithmetic operations and control operations on modern computers, memory access is a major performance issue. Consequently, an eecient system must allocate as few objects as possible and must choose very carefully the location where the objects are allocated. Let's discuss these two points further.