In this article, we add a third dimension to partial redundancy elimination by considering code size as a further optimization goal in addition to the more classical consideration of computation costs and register pressure. This results in a family of sparse code motion algorithms coming as modular extensions of the algorithms for busy and lazy code motion. Each of them optimally captures a predefined choice of priority between these three optimization goals, e.g. code size can be minimized while (1) guaranteeing at least the performance of the argument program, or (2) even computational optimality. Each of them can further be refined to simultaneously reduce the lifetimes of temporaries to a minimum. These algorithms are well-suited for size-critical application areas like smart cards and embedded systems, as they provide a handle to control the code replication problem of classical code motion techniques. In fact, we believe that our systematic, priority-based treatment of trade-offs between optimization goals may substantially decrease development costs of size-critical applications: users may “play” with the priorities until the algorithm automatically delivers a satisfactory solution.

Assignment motion (AM) and expression motion (EM) are the basis of powerful and at the first sight incomparable techniques for removing partially redundant code from a program. Whereas AM aims at the elimination of complete assignments, a transformation which is always desirable, the more flexible EM requires temporaries to remove partial redundancies. Based on the observation that a simple program transformation enhances AM to subsume EM, we develop an algorithm that for the first time captures all second order effects between AM and EM transformations. Under usual structural restrictions, the worst case time complexity of our algorithm is essentially quadratic, a fact which explains the promising experience with our implementation. Topics: data flow analysis, program optimization, partially redundant assignment and expression elimination, code motion, assignment motion, bit-vector data flow analyses. 1 Motivation A major source for improving the runtime efficiency of a program is...

A new aggressive algorithm for the elimination of partially dead code is presented, i.e., of code which is only dead on some program paths. Besides being more powerful than the usual approaches to dead code elimination, this algorithm is optimal in the following sense: partially dead code remaining in the resulting program cannot be eliminated without changing the branching structure or the semantics of the program, or without impairing some program executions.

An implementation-oriented algorithm for lazy code motion is presented that minimizes the number of computations in programs while suppressing any unnecessary code motion in order to avoid superfluous register pressure. In particular, this variant of the original algorithm for lazy code motion works on flowgraphs whose nodes are basic blocks rather than single statements, since this format is standard in optimizing compilers. The theoretical foundations of the modified algorithm are given in the first part, where t -refined flowgraphs are introduced for simplifying the treatment of flow graphs whose nodes are basic blocks. The second part presents the “basic block” algorithm in standard notation and gives directions for its implementation in standard compiler environments.

We present a bit-vector algorithm for the optimal and economical placement of computations within flow graphs, which is as efficient as standard uni-directional analyses. The point of our algorithm is the decomposition of the bi-directional structure of the known placement algorithms into a sequence of a backward and a forward analysis, which directly implies the efficiency result. Moreover, the new compositional structure opens the algorithm for modification: two further uni-directional analysis components exclude any unnecessary code motion. This laziness of our algorithm minimizes the register pressure, which has drastic effects on the run-time behaviour of the optimized programs in practice, where an economical use of registers is essential.