The demand for high-performance computing (HPC) has been increasing since the invention of computing technology. This led to the invocation of sophisticated multi/many-core processors with high performance. Graphical processing units (GPUs) have emerged as an important innovation in the manycore era as it features a high number of processors. The GPU acts as a computational accelerator that can significantly reduce the computational time of the HPC, as it can offer standout parallelism for high-end computing applications such as graphics designing. However, increasing the resources has resulted in higher power consumption and heat dissipation which has been a challenging problem for modern HPC Units. On the other hand, because of the dynamic nature of workload, a large amount of parallelism, offered by these many-core processors, is often underutilized. An ideal system would be smart enough to efficiently utilize resources and save power where less workload is available. Reducing the resources dynamically has direct implications on the performance of the system. However, if less workload is available, reducing the resources would not harm the performance, rather it would save power with less to no trade-off in overall throughput of the system. In this paper, a smart power and performance efficient resource management controller for general purpose-GPU architecture is presented. The proposed controller, based on a feedback mechanism, keeps on analyzing the current frequency of central processing unit (CPU) and GPU, number of active cores of the CPU and utilization of CPU and GPU. On the basis of collected data, the proposed controller which features fuzzy type-2 as an optimizing mechanism tries to create a balance between performance and power consumption. The results are evaluated against various benchmarks on NVIDIA TK1 GPU kit and by using dynamic voltage and frequency scaling and core gating, up to 47 % reduction in power consumption has been achieved.
The liver is a vital human body organ and its functionality can be degraded by several diseases such as hepatitis, fatty liver disease, and liver cancer and so forth. Hence, the early diagnosis of liver diseases is extremely crucial for saving human lives. With the rapid development of multimedia technology, it is now possible to design and implement a non-invasive system that can chronic liver diseases. For this purpose, machine learning and Artificial Intelligence (AI) have been used within the past few years. In this regard, digital image processing supported by AI methods has been implemented in the diagnosis of diseases that also showed high reliability. Therefore, in this paper, an iris feature-based non-invasive technique is proposed by incorporating a novel machine-learning algorithm. The experimental setup involved data set for the models' training included 879 subjects from Pakistan, of which 453 subjects have chronic liver disease and 426 are healthy. The iris images were collected using an infrared camera that consists of a lens, a thermal sensor and digital electronics processing. The lens focuses on the infrared energy on the sensor, using distinctive forms of features twenty-two physiological and thirty-three iris features. The designed classification model for a non-invasive system combined eleven different classifiers and used cross-validation techniques for comparing the results. The overall performance of the model was analyzed using five parameters: accuracy, precision, F-score, specificity, and sensitivity. The results confirmed that the proposed non-invasive model is capable of predicting chronic liver diseases with 98% of accuracy.
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