مقاله انگلیسی رایگان در مورد پیاده سازی سخت افزار و نرم افزار با برنامه های رمزنگاری – الزویر ۲۰۱۸

With the advancements in high-performance computing, algorithms have a wide range of efficient implementation options. Current computers can be equipped with multi-core processors, Graphics Processing Units (GPUs), and high-end programmable devices, such as, FPGAs. The variety of processing options are supported by a wealth of co-design tools that facilitates hardware and software implementations [1,2]. Nevertheless, several questions remain on what algorithm is the best to suite an implementation option, and vice-versa. How would an algorithm perform within hybrid processing systems, and how to make an evaluation based on heterogeneous performance measurements? The core of any performance measurement includes measures, metrics, and indicators. Indicators are defined as qualitative or quantitative factors, or variables that provide simple and reliable means to measure achievement. A qualitative performance indicator is a descriptive characteristic, an opinion, a property or a trait. However, a quantitative performance indicator is a specific numerical measurements resulted by counting, adding, averaging numbers or other computations [3]. Qualitative and quantitative measurements can be combined to define measurement frameworks and benchmarks [4]. There is a large number of hardware and software benchmarks in the literature. Yet, limited research work is reported to address developing analysis frameworks for heterogeneous hardware and software implementations. In this paper, we present a statistical analysis framework for performance profiling of related algorithms running under different hardware and software subsystems. The framework comprises criteria, indicators, and measurements obtained from heterogeneous sources. The measurements are statistically combined to produce indicators that capture the algorithmic, software, and hardware characteristics of the assessed algorithms. The developed framework enables the deep and thorough reasoning about each hardware and software subsystem, and combines heterogeneous characteristics to provide overall ratings, rankings, and classifications. The proposed framework is customizable for any hybridization of processing systems and can target any model of computation or area of application.