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Scientists without specific software programming skills are increasingly required to express their problems in terms of software to exploit the computational power of heterogeneous parallel hardware. Producing software for this hardware is very cumbersome for the experienced programmer; for the novice, it is just impracticable. We aim to grant scientists across disciplines access to heterogeneous hardware via a model-driven holistic approach. Via proper software modelling, we suppress the need to write resource-specific software functions and complex offloading and communication code; we will do this by devising a comprehensive modelling language that implicitly underpins multiple execution semantics (sequential, data-/task-parallel). Current code generators neglect model semantics while compilers expect in input too detailed software descriptions; we will devise an innovative semantics-aware model compiler with automatic parallelization. Overall, we aim at giving researchers and practitioners better tools to focus more on the problem to solve than learning how to master complex techniques and languages to describe it in terms of software. Moreover, the learning curve and technological hindrances for beginners approaching hybrid software will be pushed down dramatically., Conference paper; Export Date: 17 April 2024; Cited By: 0; Correspondence Address: F. Ciccozzi; Mälardalen University, School of Innovation, Design, and Engineering, Västerås, Sweden; email: federico.ciccozzi@mdu.se; Conference name: 2024 International Conference on Artificial Intelligence, Computer, Data Sciences, and Applications, ACDSA 2024; Conference date: 1 February 2024 through 2 February 2024; Conference code: 198277

Heterogeneous systems, consisting of CPUs and GPUs, offer the capability to address the demands of compute- and data-intensive applications. However, programming such systems is challenging, requiring knowledge of various parallel programming frameworks. This paper introduces COMPAR, a component-based parallel programming framework that enables the exposure and selection of multiple implementation variants of components at runtime. The framework leverages compiler directive-based language extensions to annotate the source code and generate the necessary glue code for the StarPU runtime system. COMPAR provides a unified view of implementation variants and allows for intelligent selection based on runtime context. Our evaluation demonstrates the effectiveness of COMPAR through benchmark applications. The proposed approach simplifies heterogeneous parallel programming and promotes code reuse while achieving optimal performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

The utilization of performance monitoring probes is a valuable tool for programmers to gather performance data. However, the manual insertion of these probes can result in an increase in code size, code obfuscation, and an added burden of learning different APIs associated with performance monitoring tools. To mitigate these issues, EDPM, an embedded domain-specific language, was developed to provide a higher level of abstraction for annotating regions of code that require instrumentation in C and C++ programs. This paper presents the design and implementation of EDPM and compares it to the well-known tool PAPI, in terms of required lines of code, flexibility in configuring regions, and performance overhead. The results of this study demonstrate that EDPM is a low-resolution profiling tool that offers a reduction in required lines of code and enables programmers to express various configurations of regions. Furthermore, the design of EDPM is such that its pragmas are ignored by the standard compiler, allowing for seamless integration into existing software processes without disrupting build systems or increasing the size of the executable. Additionally, the design of the EDPM pre-compiler allows for the extension of available performance counters while maintaining a high level of abstraction for programmers. Therefore, EDPM offers a promising solution to simplify and optimize performance monitoring in C and C++ programs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.