One of the most fundamental properties of signals is frequency. As with the other libraries we already discussed, to help with getting familiar with cuSignal, we provide a cuSignal cheatsheet. In this post, we will introduce and showcase the most common functionality of RAPIDS cuSignal. In the sixth post, the functionality of cuStreamz, we introduced how GPUs can be used to process streaming data.In the fifth post, the use of RAPIDS cuGraph, we introduced a GPU framework for processing and analyzing cyber logs.In the fourth post, the functionality of cuML, we introduced the machine learning library of RAPIDS.In the third post, querying data using SQL we introduced BlazingSQL, a SQL engine that runs on GPU.The second post compared similarities between cuDF DataFrame and pandas DataFrame.In the first post, python pandas tutorialwe introduced cuDF, the RAPIDS DataFrame framework for processing large amounts of data on an NVIDIA GPU.However, unlike scikit-learn, cuSignal brings the power of NVIDIA GPUs to signal processing resulting in orders-of-magnitude increase in speed of computations. It is aimed at analyzing and processing signals in any form and is modeled closely after the scikit-learn signal library. Even images or stock market time series can be seen and processed as signals.ĬuSignal is a newer addition to the RAPIDS ecosystem of libraries. Some of these waves are man-made, many are produced naturally. Signals are abundant: audio, radio or other electromagnetic waves (like gamma, infrared or visible light), wireless communications, ocean wave, and so on. That signal was most likely encoded by the sender end so it could carry the information and the receiver side decoded it for further usage.
You may or may not be aware that every bit of information your computer has received from a server miles away, every pixel your screen has shown, or every tune your speakers has produced was some form of a signal that was sent over a ‘wire’.
The series explores and discusses various aspects of RAPIDS that allow its users solve ETL (Extract, Transform, Load) problems, build ML (Machine Learning) and DL (Deep Learning) models, explore expansive graphs, process signal and system log, or use SQL language via BlazingSQL to process data. This post is the seventh installment of the series of articles on the RAPIDS ecosystem.
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