| The original K design was laid out in the 1980s when the constraints were even tighter than what they are today. The utilization of very short operators means not only the interpreter easily fits into cache but also the custom function definitions you have written will as well. When dealing with high performance computing or real time processing of high volumes of data, any fetch to RAM for loading a function call to dispatch is going to have _some_ impact in a tight loop. Add that up for all the libraries you have loaded for your application verses a ground up implementation in K... Does that whole thing live in L3 along with the VM or intepreter + dependencies underneath it? It's doubtful. My experience was simply using their Kx's free Developer IDE and experiencing the performance differential on datasets myself. YMMV but my (admittedly limited) experience leads me to believe that there is a serious case to be made for the performance advantages of having all your computational logic living as close to your computational cores as possible. See also the PhD by author of the OP article where he presents language where: "The entire source code to the compiler written in this method requires only 17 lines of simple code compared to roughly 1000 lines of equivalent code in the domain-specific compiler construction framework, Nanopass, and requires no domain specific techniques, libraries, or infrastructure support." Linked from the article, available here: https://scholarworks.iu.edu/dspace/handle/2022/24749 |