We define the fundamental practices and criteria for evaluating and using the Meta Llama 3 and OpenAI ChatGPT 3.5 and 4o large language models (LLMs) to translate parallel scientific Fortran + OpenMP and Fortran + OpenACC codes to C/C++ codes that can leverage vendor-specific libraries (CUDA, HIP) for GPU acceleration in addition to other performance-portable programming models (e.g., Kokkos, OpenMP, OpenACC). In this study, LLMs are used to translate 11 different parallel Fortran codes with some of the most popular and widely used kernels/proxies in high-performance computing (HPC): AXPY, GEMV, GEMM, Jacobi, SpMV, and the >200-line Hartree-Fock application proxy, which implements a solver for quantum many-body systems. In all, we analyze the correctness and reproducibility of more than 1,650 AI-generated parallel C/C++ codes. Additionally, we evaluate the performance of Fortran codes and AI-generated C/C++ codes on two modern HPC architectures—one AMD EPYC Rome CPU with 64 cores and one NVIDIA Ampere A100 GPU. We use multi-modal prompting and fine-tuning techniques for LLMs to produce parallel scientific C/C++ codes with high levels of correctness (more than 95% of the codes are well ported) and speedups of up to an order of magnitude versus Fortran + OpenMP and Fortran + OpenACC codes on the same system.

In this paper, we propose Long-Short Term Memory (LSTM)-based mosquito’s genus classification, in which the time-frequency features are extracted from the wingbeat sound of mosquitos of three genera, Aedes, Anopheles and Culex. The extracted features are fed into the proposed LSTM-based classifier. We evaluated three time-frequency features, which are: Mel Spectrogram, Log-Mel spectrogram, and Mel-frequency Cepstral Coefficients (MFCC). The proposed scheme is composed by two LSTM layers and one Fully Connected layer connected to a SoftMax activation function. The classification accuracies using the three features are 92.97(±0.2)%, 96.71(±0.2)% and 96.65(±0.2)%, respectively. The Area Under Curve (AUC) of the Receiver Operating Characteristics (ROC) for each feature are also obtained, which are 0.9944, 0.9986 and 0.9987, respectively. The proposed classifier requires approximately 62,000 trainable parameters. This number is much smaller than that required for the state-of-arts CNNs, such as AlexNet and Vgg16. This compact configuration of the proposed scheme takes advantage of the mobile and IoT implementation, because the number of trainable parameters is directly proportional to the amount of memory and CPU required.