Optimizing HPC systems based on performance factors and bottlenecks is essential for designing an HPC infrastructure with the best characteristics and at a reasonable cost. Such insight can only be achieved through a detailed analysis of existing HPC systems and the execution of their workloads. The “Quinde I” is the only and most powerful supercomputer in Ecuador and is currently listed third on the South America. It was built with the IBM Power 8 servers. In this work, we measured its performance using different parameters from High-Performance Computing (HPC) to compare it with theoretical values and values obtained from tests on similar models. To measure its performance, we compiled and ran different benchmarks with the specific optimization flags for Power 8 to get the maximum performance with the current configuration in the hardware installed by the vendor. The inputs of the benchmarks were varied to analyze their impact on the system performance. In addition, we compile and compare the performance of two algorithms for dense matrix multiplication SRUMMA and DGEMM.

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For agricultural productivity, one of the major concerns is the early detection of diseases for their crops. Recently, some researchers have begun to explore Convolutional Neural Networks (CNNs) in agricultural field for leaves diseases identification. A CNN is a category of deep artificial neural networks that has demonstrated great success in computer vision applications, such as video and image analysis. However, their drawbacks are the demand of huge quantity of data with a wide range of conditions, as well as a carefully fine-tuning to work properly. This work explores and compares the most outstanding five CNNs architectures to determine their ability to correctly classify a leaf image as healthy and unhealthy. Experimental tests are performed referring to an unbalanced and small dataset composed by healthy and diseased leaves. In order to achieve a high accuracy on the explored CNN models, a fine-tuning of their hyperparameters is performed. Furthermore, some variations are done on the raw dataset to increase the quality and variety of the leaves images. Preliminary results provide a point-of-view for selecting CNNs architectures for leaves diseases identification based on accuracy, precision, recall and F1 metrics. The comparison demonstrates that without considerably lengthening the training, ZFNet achieves a high accuracy and increases it by 10% after 50 K iterations being a suitable CNN model for identification of diseased leaves using datasets with a small variation, number of classes and dataset sizes.