Hardware evolves faster than software. While a hardware system might need replacement every one to five years, the average lifespan of a software system is a decade, with some instances living up to several decades. Inevitably, code outlives the platform it was developed for and may become legacy: development of the software stops, but maintenance has to continue to keep up with the evolving ecosystem. No new features are added, but the software is still used to fulfil its original purpose. Even in the cases where it is still functional (which discourages its replacement), legacy code is inefficient, costly to maintain, and a risk to security. This thesis proposes methods to leverage the expertise put in the development of legacy code and to extend its useful lifespan, rather than to throw it away. A novel methodology is proposed, for automatically exploiting platform specific optimisations when retargeting a program to another platform. The key idea is to leverage the optimisation information embedded in vector processing intrinsic functions. The performance of the resulting code is shown to be close to the performance of manually retargeted programs, however with the human labour removed. Building on top of that, the question of discovering optimisation information when there are no hints in the form of intrinsics or annotations is investigated. This thesis postulates that such information can potentially be extracted from profiling the data flow during executions of the program. A context-aware data dependence profiling system is described, detailing previously overlooked aspects in related research. The system is shown to be essential in surpassing the information that can be inferred statically, in particular about loop iterators. Loop iterators are the controlling part of a loop. This thesis describes and evaluates a system for extracting the loop iterators in a program. It is found to significantly outperform previously known techniques and further increases the amount of information about the structure of a program that is available to a compiler. Combining this system with data dependence profiling improves its results even more. Loop iterator recognition enables other code modernising techniques, like source code rejuvenation and commutativity analysis. The former increases the use of idiomatic code and as a result increases the maintainability of the program. The latter can potentially drive parallelisation and thus dramatically improve runtime performance.

The thesis describes movement optimization of a linear thermoelectric actuator that is used for operating floor heating valves in modern buildings. Featuring experimental nature, the thesis deals with a problem specified by an industrial company within the following steps; definition of possible solutions based on problem research, experimental verification of selected propositions, the best solution selection resulting from the analysis of executed measurements, final verification with all product versions, benchmarking.

<p>Minimum cycle mean (MCM) is an important concept in directed graphs. From clock period optimization, timing analysis to layout optimization, minimum cycle mean algorithms have found widespread use in VLSI system design optimization. With transistor size scaling to 10nm and below, complexities and size of the systems have grown rapidly over the last decade. Scalability of the algorithms both in terms of their runtime and memory usage is therefore important. </p> <p><br></p> <p>Among the few classical MCM algorithms, the algorithm by Young, Tarjan, and Orlin (YTO), has been particularly popular. When implemented with a binary heap, the YTO algorithm has the best runtime performance although it has higher asymptotic time complexity than Karp's algorithm. However, as an efficient implementation of YTO relies on data redundancy, its memory usage is higher and could be a prohibitive factor in large size problems. On the other hand, a typical implementation of Karp's algorithm can also be memory hungry. An early termination technique from Hartmann and Orlin (HO) can be directly applied to Karp's algorithm to improve its runtime performance and memory usage. Although not as efficient as YTO in runtime, HO algorithm has much less memory usage than YTO. We propose several improvements to HO algorithm. The proposed algorithm has comparable runtime performance to YTO for circuit graphs and dense random graphs while being better than HO algorithm in memory usage. </p> <p><br></p> <p>Minimum balancing of a directed graph is an application of the minimum cycle mean algorithm. Minimum balance algorithms have been used to optimally distribute slack for mitigating process variation induced timing violation issues in clock network. In a conventional minimum balance algorithm, the principal subroutine is that of finding MCM in a graph. In particular, the minimum balance algorithm iteratively finds the minimum cycle mean and the corresponding minimum-mean cycle, and uses the mean and cycle to update the graph by changing edge weights and reducing the graph size. The iterations terminate when the updated graph is a single node. Studies have shown that the bottleneck of the iterative process is the graph update operation as previous approaches involved updating the entire graph. We propose an improvement to the minimum balance algorithm by performing fewer changes to the edge weights in each iteration, resulting in better efficiency.</p> <p><br></p> <p>We also apply the minimum cycle mean algorithm in latency insensitive system design. Timing violations can occur in high performance communication links in system-on-chips (SoCs) in the late stages of the physical design process. To address the issues, latency insensitive systems (LISs) employ pipelining in the communication channels through insertion of the relay stations. Although the functionality of a LIS is robust with respect to the communication latencies, such insertion can degrade system throughput performance. Earlier studies have shown that the proper sizing of buffer queues after relay station insertion could eliminate such performance loss. However, solving the problem of maximum performance buffer queue sizing requires use of mixed integer linear programming (MILP) of which runtime is not scalable. We formulate the problem as a parameterized graph optimization problem where for every communication channel there is a parameterized edge with buffer counts as the edge weight. We then use minimum cycle mean algorithm to determine from which edges buffers can be removed safely without creating negative cycles. This is done iteratively in the similar style as the minimum balance algorithm. Experimental results suggest that the proposed approach is scalable. Moreover, quality of the solution is observed to be as good as that of the MILP based approach.</p><p><br></p>