A trustworthy framework for resource-aware embedded programming

Funding: EU Horizon 2020 project, TeamPlay (https://www.teamplay-h2020.eu), grant number 779882; UK EPSRC Discovery, grant number EP/P020631/1.Systems with non-functional requirements, such as Energy, Time and Security (ETS), are of increasing importance due to the proliferation of embedded devices with limited resources such as drones, wireless sensors, and tablet computers. Currently, however, there are little to no programmer supported methodologies or frameworks to allow them to reason about ETS properties in their source code. Drive is one such existing framework supporting the developer by lifting non-functional properties to the source-level through the Contract Specification Language (CSL), allowing non-functional properties to be first-class citizens, and supporting programmer-written code-level contracts to guarantee the non-functional specifications of the program are met. In this paper, we extend the Drive system by providing rigorous implementations of the underlying proof-engine, modeling the specification of the annotations and assertions from CSL for a representative subset of C, called Imp. We define both an improved abstract interpretation that automatically derives proofs of assertions, and define inference algorithms for the derivation of both abstract interpretations and the context over which the interpretation is indexed. We use the dependently-typed programming language, Idris, to give a formal definition, and implementation, of our abstract interpretation. Finally, we show our well-formed abstract interpretation over some representative exemplars demonstrating provable assertions of ETS.Postprin

COMPROF and COMPLACE : shared-memory communication profiling and automated thread placement via dynamic binary instrumentation

Funding: This work was generously supported by UK EPSRC Energise, grant number EP/V006290/1.This paper presents COMPROF and COMPLACE, a novel profiling tool and thread placement technique for shared-memory architectures that requires no recompilation or user intervention. We use dynamic binary instrumentation to intercept memory operations and estimate inter-thread communication overhead, deriving (and possibly visualising) a communication graph of data-sharing between threads. We then use this graph to map threads to cores in order to optimise memory traffic through the memory system. Different paths through a system's memory hierarchy have different latency, throughput and energy properties, COMPLACE exploits this heterogeneity to provide automatic performance and energy improvements for multi-threaded programs. We demonstrate COMPLACE on the NAS Parallel Benchmark (NPB) suite where, using our technique, we are able to achieve improvements of up to 12% in the execution time and up to 10% in the energy consumption (compared to default Linux scheduling) while not requiring any modification or recompilation of the application code.Postprin

Restoration of legacy parallelism : transforming pthreads into farm and pipeline patterns

Funding: This work was generously supported by the EU Horizon 2020 project, TeamPlay (https://www.teamplay-h2020.eu), grant number 779882, and UK EPSRC Discovery, grant number EP/P020631/1.Parallel patterns are a high-level programming paradigm that enables non-experts in parallelism to develop structured parallel programs that are maintainable, adaptive, and portable whilst achieving good performance on a variety of parallel systems. However, there still exists a large base of legacy-parallel code developed using ad-hoc methods and incorporating low-level parallel/concurrency libraries such as pthreads without any parallel patterns in the fundamental design. This code would benefit from being restructured and rewritten into pattern-based code. However, the process of rewriting the code is laborious and error-prone, due to typical concurrency and pthreading code being closely intertwined throughout the business logic of the program. In this paper, we present a new software restoration methodology, to transform legacy-parallel programs implemented using pthreads into structured farm and pipeline patterned equivalents. We demonstrate our restoration technique on a number of benchmarks, allowing the introduction of patterned farm and pipeline parallelism in the resulting code; we record improvements in cyclomatic complexity and speedups on a number of representative benchmarks.Publisher PDFPeer reviewe

Proving renaming for Haskell via dependent types : a case-study in refactoring soundness

We present a formally verified refactoring framework for a subset of Haskell 98. Our framework is implemented in the dependently-typed language, Idris, which allows us to encode soundness proofs as an integral part of the implementation. We give the formal definition of our static semantics for our Haskell 98 subset, which we encode as part of the AST, ensuring that only well-formed programs may be represented and transformed. This forms a foundation upon which refactorings can be formally specified. We then define soundness of refactoring implementations as conformity to their specification. We demonstrate our approach via renaming, a canonical and well-understood refactoring, giving its implementation alongside its formal specification and soundness proof.Postprin

Restoration of legacy parallelism in C and C++ applications

Parallel patterns are a high-level programming paradigm that enables non-experts in parallelism to develop structured parallel programs that are maintainable, adaptive, and portable whilst achieving good performance on a variety of parallel systems. However, there still exists a large base of legacy-parallel code developed using ad-hoc methods and incorporating low-level parallel/concurrency libraries such as pthreads without any parallel patterns in the fundamental design. This code would benefit from being restructured and rewritten into pattern-based code. However, the process of rewriting the code is laborious and error-prone, due to typical concurrency and pthreading code being closely intertwined throughout the business logic of the program. In this paper, we present a new software restoration methodology, to transform legacy-parallel programs implemented using e.g. pthreads into structured patterned equivalents. We demonstrate our restoration technique on a number of benchmarks, allowing the introduction of patterned parallelism in the resulting code; we record improvements in cyclomatic complexity and speedups.PostprintPeer reviewe

Challenges and solutions for Latin named entity recognition

Although spanning thousands of years and genres as diverse as liturgy, historiography, lyric and other forms of prose and poetry, the body of Latin texts is still relatively sparse compared to English. Data sparsity in Latin presents a number of challenges for traditional Named Entity Recognition techniques. Solving such challenges and enabling reliable Named Entity Recognition in Latin texts can facilitate many down-stream applications, from machine translation to digital historiography, enabling Classicists, historians, and archaeologists for instance, to track the relationships of historical persons, places, and groups on a large scale. This paper presents the first annotated corpus for evaluating Named Entity Recognition in Latin, as well as a fully supervised model that achieves over 90% F-score on a held-out test set, significantly outperforming a competitive baseline. We also present a novel active learning strategy that predicts how many and which sentences need to be annotated for named entities in order to attain a specified degree of accuracy when recognizing named entities automatically in a given text. This maximizes the productivity of annotators while simultaneously controlling quality

Towards semi-automatic data-type translation for parallelism in Erlang

As part of our ongoing research programme into programmer-in-the-loop parallelisation, we are studying the problem of introducing alternative data structures to support parallelism. Automated support for data structure transformations makes it easier to produce the best parallelisation for some given program, or even to make parallelisation feasible. We use a refactoring approach to choose and introduce these transformations for specific algorithmic skeletons, structured forms of parallelism that capture common patterns of parallelism. Our approach integrates with the Wrangler refactoring tool for Erlang, and uses the advanced Skel [4] skeleton library for Erlang. This library has previously been shown to give good parallelisations for a number of applications, including a multi-agent system [1] where we have achieved speedups of up to 142.44 on a 61-core machine with 244 threads. We have investigated three widely-used Erlang data structures: lists, binary structures and ETS (Erlang Term Storage) tables. In general, we have found that ETS tables deliver the best parallel performance for the examples that we have considered. However, our results show that simple lists may deliver similar performance to the use of ETS tables, and better performance than using binary structures. This means that we cannot blindly choose to implement a single optimisation as part of the compilation process. Our approach also allows the use of new (possibly user-defined) data structures and other transformations in future, giving a high level of flexibility and generality.Postprin