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Abstract Growing constraints on memory utilization, power consumption, and I/O throughput have increasingly become limiting factors to the advancement of high performance computing (HPC) and edge computing applications. IEEE‐754 floating‐point types have been the de facto standard for floating‐point number systems for decades, but the drawbacks of this numerical representation leave much to be desired. Alternative representations are gaining traction, both in HPC and machine learning environments. Posits have recently been proposed as a drop‐in replacement for the IEEE‐754 floating‐point representation. We survey the state‐of‐the‐art and state‐of‐the‐practice in the development and use of posits in edge computing and HPC. The current literature supports posits as a promising alternative to traditional floating‐point systems, both as a stand‐alone replacement and in a mixed‐precision environment. Development and standardization of the posit type is ongoing, and much research remains to explore the application of posits in different domains, how to best implement them in hardware, and where they fit with other numerical representations. 

Due to improvements in high-performance computing (HPC) capabilities, many of today’s applications produce petabytes worth of data, causing bottlenecks within the system. Importance-based sampling methods, including our spatio-temporal hybrid data sampling method, are capable of resolving these bottlenecks. While our hybrid method has been shown to outperform existing methods, its effectiveness relies heavily on user parameters, such as histogram bins, error threshold, or number of regions. Moreover, the throughput it demonstrates must be higher to avoid becoming a bottleneck itself. In this article, we resolve both of these issues. First, we assess the effects of several user input parameters and detail techniques to help determine optimal parameters. Next, we detail and implement accelerated versions of our method using OpenMP and CUDA. Upon analyzing our implementations, we find 9.8× to 31.5× throughput improvements. Next, we demonstrate how our method can accept different base sampling algorithms and the effects these different algorithms have. Finally, we compare our sampling methods to the lossy compressor cuSZ in terms of data preservation and data movement.