Software plays a central role in all aspects of reversible computing systems, and a variety of reversible programming languages have been developed. This presentation highlights the principles and main ideas of reversible computing viewed from a programming language perspective with a focus on clean reversible languages. They are the building material for software that can reap the benefits of reversible hardware and interesting in their own right. Reversible computing is situated within programming languages in general, and the relevant concepts are elaborated, including computability, injectivization and reversibilization. Features representative for many reversible languages are presented, such as reversible updates, reversible iterations, and access to a program's inverse semantics. Metaprogramming methods of particular importance to reversible programming, are introduced, including program inversion and inverse interpretation. Our presentation is independent of a particular language, although primarily the reversible language, Janus, will be used in examples.

Discrete linear transformations are important tools in information processing. Many such transforms are injective and therefore prime candidates for a physically reversible implementation into hardware. We present here reversible integer cosine transformations on n input integers. The resulting reversible circuit is able to perform both the forward transform and the inverse transform. The detailed structure of such a reversible design strongly depends on the odd prime factors of the determinant of the transform: whether those are of the form 2 k ± 1 or of the form 2 k ± 2 l ± 1 or neither of these forms.

The energy efficiency at the algorithmic level on DVS systems and its analysis and optimization methods are presented. Given a problem the most energy efficient algorithm is not uniquely determined but dependend on multiple factors, including intratask dynamic voltage scaling (IntraDVS) policies, the size of intermediate data structure, and the size of inputs. We show that at the algorithmic level principles behind energy optimization and performance optimization are not identical. We propose a metric for evaluating optimal energy efficiency of static voltage scaling (SVS) and a few new effective IntraDVS policies employing data flow information. Experimental results on sorting algorithms show the existence of several tradeoffs in terms of energy consumption. Transforming algorithms by employing problem specific knowledge and data flow information successfully improves their energy efficiency.

Energy-aware real-time multiprocessor scheduling has been studied extensively so far. However, some of the constraints associated with the practical DVS applications have been ignored for simplicity. These constraints include discrete speed, idle power, inefficient speed, and application-specific power characteristics etc. This work targets energy-aware scheduling of periodic real-time tasks on the DVS-equipped multiprocessor systems with practical constraints. An adaptive minimal bound first-fit (AMBFF) algorithm with consideration of these realistic constraints is proposed for both dynamic-priority and fixed-priority multiprocessor scheduling. Simulation results on three commercial processor models show that our algorithm can save significantly more energy than existing algorithms.

A reversible programming language supports deterministic forward and backward computation. We formalize the programming language Janus and prove its reversibility. We provide a program inverter for the language and implement a self-interpreter that achieves deterministic forward and backward interpretation of Janus programs without using a computation history. As the self-interpreter is implemented in a reversible language, it is invertible using local program inversion. Many physical phenomena are reversible and we demonstrate the power of Janus by implementing a reversible program for discrete simulation of the Schrödinger wave equation that can be inverted as well as run forward and backward.