Optimization of redundancy in branched water distribution systems

In developing countries many water distribution systems are branched networks with little redundancy. If any component in the distribution system fails, many users are left relying on secondary water sources. These sources oftentimes do not provide potable water and prolonged use leads to increased cases of water borne illnesses. Increasing redundancy in branched networks increases the reliability of the networks, but is oftentimes viewed as unaffordable. This paper presents a procedure for water system managers to use to determine which loops when added to a branch network provide the most benefit for users. Two methods are presented, one ranking the loops based on total number of users benefited, and one ranking the loops of number of vulnerable users benefited. A case study is presented using the water distribution system of Medina Bank Village, Belize. It was found that forming loops in upstream pipes connected to the main line had the potential to benefit the most users.

Future value based single assignment program representations and optimizations

An optimizing compiler internal representation fundamentally affects the clarity, efficiency and feasibility of optimization algorithms employed by the compiler. Static Single Assignment (SSA) as a state-of-the-art program representation has great advantages though still can be improved. This dissertation explores the domain of single assignment beyond SSA, and presents two novel program representations: Future Gated Single Assignment (FGSA) and Recursive Future Predicated Form (RFPF). Both FGSA and RFPF embed control flow and data flow information, enabling efficient traversal program information and thus leading to better and simpler optimizations. We introduce future value concept, the designing base of both FGSA and RFPF, which permits a consumer instruction to be encountered before the producer of its source operand(s) in a control flow setting. We show that FGSA is efficiently computable by using a series T1/T2/TR transformation, yielding an expected linear time algorithm for combining together the construction of the pruned single assignment form and live analysis for both reducible and irreducible graphs. As a result, the approach results in an average reduction of 7.7%, with a maximum of 67% in the number of gating functions compared to the pruned SSA form on the SPEC2000 benchmark suite. We present a solid and near optimal framework to perform inverse transformation from single assignment programs. We demonstrate the importance of unrestricted code motion and present RFPF. We develop algorithms which enable instruction movement in acyclic, as well as cyclic regions, and show the ease to perform optimizations such as Partial Redundancy Elimination on RFPF.