A Composable Array Function Interface for Heterogeneous Computing in Java

Heterogeneous computing has now become mainstream with virtually every desktop machines featuring accelerators such as Graphics Processing Units (GPUs). While heterogeneity offers the promise of high-performance and high-efficiency, it comes at the cost of huge programming difficulties. Languages and interfaces for programming such system tend to be low-level and require expert knowledge of the hardware in order to achieve its potential. A promising approach for programming such heterogeneous systems is the use of array programming. This style of programming relies on well known parallel patterns that can be easily translated into GPU or other accelerator code. However, only little work has been done on integrating such concepts in mainstream languages such as Java. In this work, we propose a new Array Function interface implemented with the new features from Java 8. While similar in spirit to the new Stream API of Java, our API follows a different design based on reusability and composability. We demonstrate that this API can be used to generate OpenCL code for a simple application. We present encouraging preliminary performance results showing the potential of our approach

Parallel and Distributed Execution of Model Management Programs

The engineering process of complex systems involves many stakeholders and development artefacts. Model-Driven Engineering (MDE) is an approach to development which aims to help curtail and better manage this complexity by raising the level of abstraction. In MDE, models are first-class artefacts in the development process. Such models can be used to describe artefacts of arbitrary complexity at various levels of abstraction according to the requirements of their prospective stakeholders. These models come in various sizes and formats and can be thought of more broadly as structured data. Since models are the primary artefacts in MDE, and the goal is to enhance the efficiency of the development process, powerful tools are required to work with such models at an appropriate level of abstraction. Model management tasks – such as querying, validation, comparison, transformation and text generation – are often performed using dedicated languages, with declarative constructs used to improve expressiveness. Despite their semantically constrained nature, the execution engines of these languages rarely capitalize on the optimization opportunities afforded to them. Therefore, working with very large models often leads to poor performance when using MDE tools compared to general-purpose programming languages, which has a detrimental effect on productivity. Given the stagnant single-threaded performance of modern CPUs along with the ubiquity of distributed computing, parallelization of these model management program is a necessity to address some of the scalability concerns surrounding MDE. This thesis demonstrates efficient parallel and distributed execution algorithms for model validation, querying and text generation and evaluates their effectiveness. By fully utilizing the CPUs on 26 hexa-core systems, we were able to improve performance of a complex model validation language by 122x compared to its existing sequential implementation. Up to 11x speedup was achieved with 16 cores for model query and model-to-text transformation tasks