A formally verified compiler back-end

This article describes the development and formal verification (proof of semantic preservation) of a compiler back-end from Cminor (a simple imperative intermediate language) to PowerPC assembly code, using the Coq proof assistant both for programming the compiler and for proving its correctness. Such a verified compiler is useful in the context of formal methods applied to the certification of critical software: the verification of the compiler guarantees that the safety properties proved on the source code hold for the executable compiled code as well

From constraint programming to heterogeneous parallelism

The scaling limitations of multi-core processor development have led to a diversification of the processor cores used within individual computers. Heterogeneous computing has become widespread, involving the cooperation of several structurally different processor cores. Central processor (CPU) cores are most frequently complemented with graphics processors (GPUs), which despite their name are suitable for many highly parallel computations besides computer graphics. Furthermore, deep learning accelerators are rapidly gaining relevance. Many applications could profit from heterogeneous computing but are held back by the surrounding software ecosystems. Heterogeneous systems are a challenge for compilers in particular, which usually target only the increasingly marginalised homogeneous CPU cores. Therefore, heterogeneous acceleration is primarily accessible via libraries and domain-specific languages (DSLs), requiring application rewrites and resulting in vendor lock-in. This thesis presents a compiler method for automatically targeting heterogeneous hardware from existing sequential C/C++ source code. A new constraint programming method enables the declarative specification and automatic detection of computational idioms within compiler intermediate representation code. Examples of computational idioms are stencils, reductions, and linear algebra. Computational idioms denote algorithmic structures that commonly occur in performance-critical loops. Consequently, well-designed accelerator DSLs and libraries support computational idioms with their programming models and function interfaces. The detection of computational idioms in their middle end enables compilers to incorporate DSL and library backends for code generation. These backends leverage domain knowledge for the efficient utilisation of heterogeneous hardware. The constraint programming methodology is first derived on an abstract model and then implemented as an extension to LLVM. Two constraint programming languages are designed to target this implementation: the Compiler Analysis Description Language (CAnDL), and the extended Idiom Detection Language (IDL). These languages are evaluated on a range of different compiler problems, culminating in a complete heterogeneous acceleration pipeline integrated with the Clang C/C++ compiler. This pipeline was evaluated on the established benchmark collections NPB and Parboil. The approach was applicable to 10 of the benchmark programs, resulting in significant speedups from 1.26× on “histo” to 275× on “sgemm” when starting from sequential baseline versions. In summary, this thesis shows that the automatic recognition of computational idioms during compilation enables the heterogeneous acceleration of sequential C/C++ programs. Moreover, the declarative specification of computational idioms is derived in novel declarative programming languages, and it is demonstrated that constraint programming on Single Static Assignment intermediate code is a suitable method for their automatic detection

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 25th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2022, which was held during April 4-6, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 23 regular papers presented in this volume were carefully reviewed and selected from 77 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 23rd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 31 regular papers presented in this volume were carefully reviewed and selected from 98 submissions. The papers cover topics such as categorical models and logics; language theory, automata, and games; modal, spatial, and temporal logics; type theory and proof theory; concurrency theory and process calculi; rewriting theory; semantics of programming languages; program analysis, correctness, transformation, and verification; logics of programming; software specification and refinement; models of concurrent, reactive, stochastic, distributed, hybrid, and mobile systems; emerging models of computation; logical aspects of computational complexity; models of software security; and logical foundations of data bases.

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Efficient local search for Pseudo Boolean Optimization

Algorithms and the Foundations of Software technolog

Analyses and optimizations of timing-constrained embedded systems considering resource synchronization and machine learning approaches

Nowadays, embedded systems have become ubiquitous, powering a vast array of applications from consumer electronics to industrial automation. Concurrently, statistical and machine learning algorithms are being increasingly adopted across various application domains, such as medical diagnosis, autonomous driving, and environmental analysis, offering sophisticated data analysis and decision-making capabilities. As the demand for intelligent and time-sensitive applications continues to surge, accompanied by growing concerns regarding data privacy, the deployment of machine learning models on embedded devices has emerged as an indispensable requirement. However, this integration introduces both significant opportunities for performance enhancement and complex challenges in deployment optimization. On the one hand, deploying machine learning models on embedded systems with limited computational capacity, power budgets, and stringent timing requirements necessitates additional adjustments to ensure optimal performance and meet the imposed timing constraints. On the other hand, the inherent capabilities of machine learning, such as self-adaptation during runtime, prove invaluable in addressing challenges encountered in embedded systems, aiding in optimization and decision-making processes. This dissertation introduces two primary modifications for the analyses and optimizations of timing-constrained embedded systems. For one thing, it addresses the relatively long access times required for shared resources of machine learning tasks. For another, it considers the limited communication resources and data privacy concerns in distributed embedded systems when deploying machine learning models. Additionally, this work provides a use case that employs a machine learning method to tackle challenges specific to embedded systems. By addressing these key aspects, this dissertation contributes to the analysis and optimization of timing-constrained embedded systems, considering resource synchronization and machine learning models to enable improved performance and efficiency in real-time applications with stringent constraints

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 25th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2022, which was held during April 4-6, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 23 regular papers presented in this volume were carefully reviewed and selected from 77 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems