Structured Parallelism by Composition - Design and implementation of a framework supporting skeleton compositionality

This thesis is dedicated to the efficient compositionality of algorithmic skeletons, which are abstractions of common parallel programming patterns. Skeletons can be implemented in the functional parallel language Eden as mere parallel higher order functions. The use of algorithmic skeletons facilitates parallel programming massively. This is because they already implement the tedious details of parallel programming and can be specialised for concrete applications by providing problem specific functions and parameters. Efficient skeleton compositionality is of particular importance because complex, specialised skeletons can be compound of simpler base skeletons. The resulting modularity is especially important for the context of functional programming and should not be missing in a functional language. We subdivide composition into three categories: -Nesting: A skeleton is instantiated from another skeleton instance. Communication is tree shaped, along the call hierarchy. This is directly supported by Eden. -Composition in sequence: The result of a skeleton is the input for a succeeding skeleton. Function composition is expressed in Eden by the ( . ) operator. For performance reasons the processes of both skeletons should be able to exchange results directly instead of using the indirection via the caller process. We therefore introduce the remote data concept. -Iteration: A skeleton is called in sequence a variable number of times. This can be defined using recursion and composition in sequence. We optimise the number of skeleton instances, the communication in between the iteration steps and the control of the loop. To this end, we developed an iteration framework where iteration skeletons are composed from control and body skeletons. Central to our composition concept is remote data. We send a remote data handle instead of ordinary data, the data handle is used at its destination to request the referenced data. Remote data can be used inside arbitrary container types for efficient skeleton composition similar to ordinary distributed data types. The free combinability of remote data with arbitrary container types leads to a high degree of flexibility. The programmer is not restricted by using a predefined set of distributed data types and (re-)distribution functions. Moreover, he can use remote data with arbitrary container types to elegantly create process topologies. For the special case of skeleton iteration we prevent the repeated construction and deconstruction of skeleton instances for each single iteration step, which is common for the recursive use of skeletons. This minimises the parallel overhead for process and channel creation and allows to keep data local on persistent processes. To this end we provide a skeleton framework. This concept is independent of remote data, however the use of remote data in combination with the iteration framework makes the framework more flexible. For our case studies, both approaches perform competitively compared to programs with identical parallel structure but which are implemented using monolithic skeletons - i.e. skeleton not composed from simpler ones. Further, we present extensions of Eden which enhance composition support: generalisation of overloaded communication, generalisation of process instantiation, compositional process placement and extensions of Box types used to adapt communication behaviour

Spectrum skeletonization : a new method for acoustic signal extraction

Vibration Analysis Tests (VAT) and Acoustic Emission tests (AE) are used in several industrial applications. Many of them perform analysis in the frequency domain. Peaks in the power density spectrum hold relevant information about acoustic events. In this paper we propose a novel method for feature extraction of vibration samples by analyzing the shape of their auto power spectrum density function. The approach uses skeletonization techniques in order to find the hierarchical structure of the spectral peaks. The proposed method can be applied as a preprocessing step for spectrum analysis of vibration signals

Pattern discovery for parallelism in functional languages

No longer the preserve of specialist hardware, parallel devices are now ubiquitous. Pattern-based approaches to parallelism, such as algorithmic skeletons, simplify traditional low-level approaches by presenting composable high-level patterns of parallelism to the programmer. This allows optimal parallel configurations to be derived automatically, and facilitates the use of different parallel architectures. Moreover, parallel patterns can be swap-replaced for sequential recursion schemes, thus simplifying their introduction. Unfortunately, there is no guarantee that recursion schemes are present in all functional programs. Automatic pattern discovery techniques can be used to discover recursion schemes. Current approaches are limited by both the range of analysable functions, and by the range of discoverable patterns. In this thesis, we present an approach based on program slicing techniques that facilitates the analysis of a wider range of explicitly recursive functions. We then present an approach using anti-unification that expands the range of discoverable patterns. In particular, this approach is user-extensible; i.e. patterns developed by the programmer can be discovered without significant effort. We present prototype implementations of both approaches, and evaluate them on a range of examples, including five parallel benchmarks and functions from the Haskell Prelude. We achieve maximum speedups of 32.93x on our 28-core hyperthreaded experimental machine for our parallel benchmarks, demonstrating that our approaches can discover patterns that produce good parallel speedups. Together, the approaches presented in this thesis enable the discovery of more loci of potential parallelism in pure functional programs than currently possible. This leads to more possibilities for parallelism, and so more possibilities to take advantage of the potential performance gains that heterogeneous parallel systems present