Automatic translation of programming languages has garnered renewed interest, driven by recent advancements in large language models (LLMs). Encoder-decoder transformer models, in particular, have shown promise in translating between different programming languages. However, translating between a language and its high-performance computing (HPC) extension remains underexplored due to inherent challenges like complex parallel semantics understanding. In this paper, we introduce CodeRosetta, an encoder-decoder transformer model explicitly designed for translating between programming languages and also their HPC extensions. CodeRosetta is evaluated on C++ to CUDA and Fortran to C++ translation. It employs a customized learning-based framework with tailored pretraining and training objectives that enable it to effectively capture code semantics and parallel structural nuances, allowing for bidirectional code translation. Our results show that CodeRosetta outperforms state-of-the-art baselines in C++ to CUDA translation by 2.9 BLEU and 1.72 CodeBLUE points while improving compilation accuracy by 6.05%. Compared to general closed-source LLMs, our proposed bidirectional learning-based method improves C++ to CUDA translation by 22.08 BLEU and 14.39 CodeBLUE with 2.75% higher compilation accuracy. Finally, CodeRosetta exhibits proficiency in Fortran to parallel C++ translation, marking it, to our knowledge, as the first encoder-decoder model for such a complex translation task, improving CodeBLEU at least by 4.63 points compared to closed-source LLMs and Open Code LLM.

The rapidly evolving scenario around cellular networks, Internet of Things (IoT), and the associated services ecosystem has generated a wide range of expectations for the future networks. However, the unavailability of sufficient bandwidth and severe interference prohibit the existing commercially used radio-frequency (RF) bands from supporting some of these services. In this respect, the millimeter wave (mmWave)-based systems are seen as promising alternatives for supporting various high data rate enhanced mobile broadband (eMBB) services. Nevertheless, the adverse wave propagation characteristics in these bands and the resulting media access control (MAC) layer problems of beam blockage restrict these systems from operating in a reliable manner. Therefore, in this article, we propose a MAC layer-based approach of control delegation to tackle the problem of beam blockage. The mechanism uses policy-based relinquishment of access point (AP) control from the default AP to other AP capable member nodes to improve node visibility and alleviate the blockage scenario without requiring any additional network infrastructure. To account for the dynamic behavior of the network, a discrete-time Markov decision process (MDP)-based model is proposed. It uses a dynamic optimization methodology to accommodate multiple reconfigurable performance metrics, which can then be adjusted to cater to different performance criteria. Using computer simulations, we evaluate the effectiveness of our proposed solution for some alternate static and dynamic control policies. Our simulation results show that our proposed solution can significantly reduce the blockage in a network while satisfying a variety of performance objectives over different classes of indoor IoT scenarios.