A practical challenge for RF signal processing is the length of the time records, the high-data rate, and the equivalent short processing time available for real-life applications. For learned dictionary methods, the two separate algorithm components that need to be optimized are the “learning stage” and the “classification stage.” Learning a dictionary of size $K$ can be computationally very expensive, depending on number of dictionary elements, length of elements (i.e., size of data window), and amount of training data. Since we use supervised learning, and in our case do not update the dictionary with every new test set, the learning stage becomes upfront computational overhead and can, in theory, be reduced by use of parallel computing hardware wherever possible. At a particular sequential update iteration, we can scatter the $K$ inner products between data and dictionary elements across multiple cores, resulting in $O(LNP)$ complexity, where $L$ is the sparsity factor, $N$ is the length of a dictionary element, and $P$ is the number of training data windows. For the K-SVD algorithm, the SVD decomposition in the dictionary update step is the computational bottleneck, as it can take up to $6\u2009\u2009s/dictionary$ element update at every learning iteration for our given training set size, using a 64-bit Win7 machine with multiple Xeon X5550 processors. For example, a single learning iteration (i.e., a full cycle of $C$) for a K-SVD dictionary with 1024 elements of length 10240 takes 3904.14 s on average and takes 1145.93 s on average for a K-SVD dictionary with 512 elements of length 20480. The Hebbian update is much faster and grows linearly with the length of the dictionary element (or size of data windows), as shown in Eq. (7).