Liveness-Based Garbage Collection for Lazy Languages

We consider the problem of reducing the memory required to run lazy first-order functional programs. Our approach is to analyze programs for liveness of heap-allocated data. The result of the analysis is used to preserve only live data---a subset of reachable data---during garbage collection. The result is an increase in the garbage reclaimed and a reduction in the peak memory requirement of programs. While this technique has already been shown to yield benefits for eager first-order languages, the lack of a statically determinable execution order and the presence of closures pose new challenges for lazy languages. These require changes both in the liveness analysis itself and in the design of the garbage collector. To show the effectiveness of our method, we implemented a copying collector that uses the results of the liveness analysis to preserve live objects, both evaluated (i.e., in WHNF) and closures. Our experiments confirm that for programs running with a liveness-based garbage collector, there is a significant decrease in peak memory requirements. In addition, a sizable reduction in the number of collections ensures that in spite of using a more complex garbage collector, the execution times of programs running with liveness and reachability-based collectors remain comparable

An Environment for Analyzing Space Optimizations in Call-by-Need Functional Languages

We present an implementation of an interpreter LRPi for the call-by-need calculus LRP, based on a variant of Sestoft's abstract machine Mark 1, extended with an eager garbage collector. It is used as a tool for exact space usage analyses as a support for our investigations into space improvements of call-by-need calculi.Comment: In Proceedings WPTE 2016, arXiv:1701.0023

Compile-Time Optimisation of Store Usage in Lazy Functional Programs

Functional languages offer a number of advantages over their imperative counterparts. However, a substantial amount of the time spent on processing functional programs is due to the large amount of storage management which must be performed. Two apparent reasons for this are that the programmer is prevented from including explicit storage management operations in programs which have a purely functional semantics, and that more readable programs are often far from optimal in their use of storage. Correspondingly, two alternative approaches to the optimisation of store usage at compile-time are presented in this thesis. The first approach is called compile-time garbage collection. This approach involves determining at compile-time which cells are no longer required for the evaluation of a program, and making these cells available for further use. This overcomes the problem of a programmer not being able to indicate explicitly that a store cell can be made available for further use. Three different methods for performing compile-time garbage collection are presented in this thesis; compile-time garbage marking, explicit deallocation and destructive allocation. Of these three methods, it is found that destructive allocation is the only method which is of practical use. The second approach to the optimisation of store usage is called compile-time garbage avoidance. This approach involves transforming programs into semantically equivalent programs which produce less garbage at compile-time. This attempts to overcome the problem of more readable programs being far from optimal in their use of storage. In this thesis, it is shown how to guarantee that the process of compile-time garbage avoidance will terminate. Both of the described approaches to the optimisation of store usage make use of the information obtained by usage counting analysis. This involves counting the number of times each value in a program is used. In this thesis, a reference semantics is defined against which the correctness of usage counting analyses can be proved. A usage counting analysis is then defined and proved to be correct with respect to this reference semantics. The information obtained by this analysis is used to annotate programs for compile-time garbage collection, and to guide the transformation when compile-time garbage avoidance is performed. It is found that compile-time garbage avoidance produces greater increases in efficiency than compile-time garbage collection, but much of the garbage which can be collected by compile-time garbage collection cannot be avoided at compile-time. The two approaches are therefore complementary, and the expressions resulting from compile-time garbage avoidance transformations can be annotated for compile-time garbage collection to further optimise the use of storage

Modelling Garbage Collection Algorithms --- Extend abstract

We show how abstract requirements of garbage collection can be captured using temporal logic. The temporal logic specification can then be used as a basis for process algebra specifications which can involve varying amounts of parallelism. We present two simple CCS specifications as an example, followed by a more complex specification of the cyclic reference counting algorithm. The verification of such algorithms is then briefly discussed

Benchmarking Implementations of Functional Languages with ``Pseudoknot'', a Float-Intensive Benchmark

Over 25 implementations of different functional languages are benchmarked using the same program, a floatingpoint intensive application taken from molecular biology. The principal aspects studied are compile time and execution time for the various implementations that were benchmarked. An important consideration is how the program can be modified and tuned to obtain maximal performance on each language implementation.\ud With few exceptions, the compilers take a significant amount of time to compile this program, though most compilers were faster than the then current GNU C compiler (GCC version 2.5.8). Compilers that generate C or Lisp are often slower than those that generate native code directly: the cost of compiling the intermediate form is normally a large fraction of the total compilation time.\ud There is no clear distinction between the runtime performance of eager and lazy implementations when appropriate annotations are used: lazy implementations have clearly come of age when it comes to implementing largely strict applications, such as the Pseudoknot program. The speed of C can be approached by some implemtations, but to achieve this performance, special measures such as strictness annotations are required by non-strict implementations.\ud The benchmark results have to be interpreted with care. Firstly, a benchmark based on a single program cannot cover a wide spectrum of 'typical' applications.j Secondly, the compilers vary in the kind and level of optimisations offered, so the effort required to obtain an optimal version of the program is similarly varied