Because of tight power and energy constraints, industry is progressively shifting toward heterogeneous system-on-chip (SoC) architectures composed of a mix of general-purpose cores along with a number of accelerators. However, such SoC architectures can be very challenging to efficiently program for the vast majority of programmers, due to numerous programming approaches and languages. Libraries, on the other hand, provide a simple way to let programmers take advantage of complex architectures, which does not require programmers to acquire new accelerator-specific or domain-specific languages. Increasingly, library-based, also called algorithm-centric, programming approaches propose to generalize the usage of libraries and to compose programs around these libraries, instead of using libraries as mere complements. In this article, we present a software framework for achieving performance portability by leveraging a gen-eralized library-based approach. Inspired by the notion of a component, as employed in software engineering and HW/SW codesign, we advocate nonexpert programmers to write simple wrapper code around existing libraries to provide simple but necessary semantic information to the runtime. To achieve performance portability, the runtime employs machine learning (simulated annealing) to select the most appropriate ac-celerator and its parameters for a given algorithm. This selection factors in the possibly complex composition of algorithms used in the application, the communication among the various accelerators, and the tradeoff

Heterogeneity is omnipresent in today’s commodity computational systems, which comprise at least one multi-core Central Processing Unit (CPU) and one Graphics Processing Unit (GPU). Nonetheless, all this computing power is not being harnessed in mainstream computing, as the programming of these systems entails many details of the underlying architecture and of its distinct execution models. Current research on parallel programming is addressing these issues but, still, the system’s heterogeneity is exposed at language level. This paper proposes a uniform framework, grounded on the Single Operation Multiple Data model, for the programming of such heterogeneous systems. The model is declarative, empowering the compiler to generate code for multiple architectures from the same source. To this extent, we designed a simple extension of the Java programming language that embodies the model, and developed a compiler that generates code for both multi-core CPUs and GPUs. A performance evaluation attests the validity of the approach that, despite being based on a simple programming model, is able to deliver performance gains on par with hand-tuned data parallel multi-threaded Java applications.

Abstract—Chapel is an emerging parallel programming language that originated under the DARPA High Productivity Computing Systems (HPCS) program. Although the HPCS program is now complete, the Chapel language and project remain very much alive and well. Under the HCPS program, Chapel generated sufficient interest among HPC user communities to warrant continuing its evolution and development over the next several years. In this paper, we reflect on the progress that was made with Chapel under the auspices of the HPCS program, noting key decisions made during the project’s history. We also summarize the current state of Chapel for programmers who are interested in using it today. And finally, we describe current and ongoing work to evolve it from prototype to productiongrade; and also to make it better suited for execution on nextgeneration systems. Keywords-Chapel; parallel languages; project status I.