Memory Mangement in the PoSSo Solver

AbstractA uniform general purpose garbage collector may not always provide optimal performance. Sometimes an algorithm exhibits a predictable pattern of memory usage that could be exploited, delaying as much as possible the intervention of the collector. This requires a collector whose strategy can be customized to the need of an algorithm. We present a dynamic memory management framework which allows such customization, while preserving the convenience of automatic collection in the normal case. The Customizable Memory Management (CMM) organizes memory in multiple heaps, each one encapsulating a particular storage discipline. The default heap for collectable objects uses the technique of mostly copying garbage collection, providing good performance and memory compaction. Customization of the collector is achieved through object orientation by specialising the collector methods for each heap class. We describe how the CMM has been exploited in the implementation of the Buchberger algorithm, by using a special heap for temporary objects created during polynomial reduction. The solution drastically reduces the overall cost of memory allocation in the algorithm

Compiler architecture using a portable intermediate language

The back end of a compiler performs machine-dependent tasks and low-level optimisations that are laborious to implement and difficult to debug. In addition, in languages that require run-time services such as garbage collection, the back end must interface with the run-time system to provide those services. The net result is that building a compiler back end entails a high implementation cost. In this dissertation I describe reusable code generation infrastructure that enables the construction of a complete programming language implementation (compiler and run-time system) with reduced effort. The infrastructure consists of a portable intermediate language, a compiler for this language and a low-level run-time system. I provide an implementation of this system and I show that it can support a variety of source programming languages, it reduces the overall eort required to implement a programming language, it can capture and retain information necessary to support run-time services and optimisations, and it produces efficient code