Developing and optimizing software applications for high performance and energy efficiency is a very challenging task, even when considering a single target machine. For instance, optimizing for multicore-based computing systems requires in-depth knowledge about programming languages, application programming interfaces (APIs), compilers, performance tuning tools, and computer architecture and organization. Many of the tasks of performance engineering methodologies require manual efforts and the use of different tools not always part of an integrated toolchain. This paper presents Pegasus, a performance engineering approach supported by a framework that consists of a source-to-source compiler, controlled and guided by strategies programmed in a Domain-Specific Language, and an autotuner. Pegasus is a holistic and versatile approach spanning various decision layers composing the software stack, and exploiting the system capabilities and workloads effectively through the use of runtime autotuning. The Pegasus approach helps developers by automating tasks regarding the efficient implementation of software applications in multicore computing systems. These tasks focus on application analysis, profiling, code transformations, and the integration of runtime autotuning. Pegasus allows developers to program their strategies or to automatically apply existing strategies to software applications in order to ensure the compliance of non-functional requirements, such as performance and energy efficiency. We show how to apply Pegasus and demonstrate its applicability and effectiveness in a complex case study, which includes tasks from a smart navigation system.

Enhancing performance is crucial when developing applications for high-performance and embedded computing. It requires sophisticated techniques and in-depth knowledge of the application domain and target architecture. Typically, developers prioritize the application's functional requirements over extra-functional requirements. Thus, a large part of the optimization effort is shifted to performance engineers, who rely on manual effort, alongside many analysis and optimization tools that need integration. This paper focuses on memoization, which caches results of pure computations and retrieves them if a function is called with repeating arguments. We propose a methodology for allowing developers and performance engineers to apply memoization straightforwardly by automating code analysis, code transformations, and memoization-specific profiling. It helps developers with no optimization expertise to quickly set up memoization and, simultaneously, it provides performance engineers with highly customizable analysis and memoization. We provide a concrete implementation supported by a DSL, a source-to-source compiler, and a memoization framework. We evaluate the methodology and framework with publicly available benchmarks. We show how one can analyze applications to select functions with performance improvement potential, which the experiments reveal might be challenging to find, and improve some applications with minimal effort.

The ANTAREX project developed an approach to the performance tuning of High Performance applications based on an Aspect-oriented Domain Specific Language (DSL), with the goal to simplify the enforcement of extra-functional properties in large scale applications. The project aims at demonstrating its tools and techniques on two relevant use cases, one in the domain of computational drug discovery, the other in the domain of online vehicle navigation. In this paper, we present an overview of the project and of its main achievements, as well as of the large scale experiments that have been planned to validate the approach.

Designing and optimizing applications for energy-efficient High Performance Computing systems up to the Exascale era is an extremely challenging problem. This paper presents the toolbox developed in the ANTAREX European project for autotuning and adaptivity in energy efficient HPC systems. In particular, the modules of the ANTAREX toolbox are described as well as some preliminary results of the application to two target use cases. 1

Configuring program parallelism and selecting optimal compiler options according to the underlying platform architecture is a difficult task. Tipically, this task is either assigned to the programmer or done by a standard one-fits-all policy generated by the compiler or runtime system. A runtime selection of the best configuration requires the insertion of a lot of glue code for profiling and runtime selection. This represents a programming wall for application developers. This paper presents a structured approach, called SOCRATES, based on an aspect-oriented language (LARA) and a runtime autotuner (mARGOt) to mitigate this problem. LARA has been used to hide the glue code insertion, thus separating the pure functional application description from extra-functional requirements. mARGOT has been used for the automatic selection of the best configuration according to the runtime evolution of the application.1

The ANTAREX project relies on a Domain Specific Language (DSL) based on Aspect Oriented Programming (AOP) concepts to allow applications to enforce extra functional properties such as energy-efficiency and performance and to optimize Quality of Service (QoS) in an adaptive way. The DSL approach allows the definition of energy-efficiency, performance, and adaptivity strategies as well as their enforcement at runtime through application autotuning and resource and power management. In this paper, we present an overview of the ANTAREX DSL and some of its capabilities through a number of examples, including how the DSL is applied in the context of one of the project use cases.

Runtime adaptability is expected to adjust the application and the mapping of computations according to usage contexts, operating environments, resources availability, etc. However, extending applications with adaptive features can be a complex task, especially due to the current lack of programming models and compiler support. One of the run-time adaptability possibilities is the use of specialized code according to data workloads and environments. Traditional approaches use multiple code versions generated offline and, during runtime, a strategy is responsible to select a code version. Moving code generation to runtime can achieve important improvements but may impose unacceptable overhead. This paper presents an aspect-oriented programming approach for runtime adaptability. We focus on a separation of concerns (strategies vs. application) promoted by a domain-specific language for programming runtime strategies. Our strategies allow runtime specialization based on contextual information. We use a template-based runtime code generation approach to achieve program specialization. We demonstrate our approach with examples from image processing, which depict the benefits of runtime specialization and illustrate how several factors need to be considered to efficiently adapt the application.

This paper describes MATISSE, a MATLAB to C compiler targeting embedded systems that is based on Strategic and Aspect-Oriented Programming concepts. MATISSE takes as input: (1) MATLAB code and (2) LARA aspects related to types and shapes, code insertion/removal, and specialization based directives defining default variable values. In this paper we also illustrate the use of MATISSE in leveraging data types and shapes to generate customized C code suitable for high-level hardware synthesis tools. The preliminary experimental results presented here reveal the described approach to yield performance results for the resulting hardware and software references implementations that are comparable in terms of performance with hand-crafted solutions but derived automatically at a fraction of the cost.