Dear Statisticians--- This is not even an R question, so please forgive me. I have so much ignorance in this matter that I do not know where to begin. I hope someone can point me to documentation and/or a sample.
I want to compute a covariance as quickly as non-humanly possible on an Intel core processor (up to SSE4) under linux. Alas, I have no idea how to engage CPU vectorization. Do I need to use special data types, or is "double" correct? Does SSE* understand NaN? Should I rely on gcc autodetection of the vectorized meaning of my code, or are there specific libraries that I should call? What I want to learn about is as simple as it gets: typedef double Double; // or whatever SSE* needs as close equivalent Double vector1[N], vector2[N]; // then fill them with stuff. vector3= vector_mult(vector1,vector2, N); vector4= sum(vector1, N); I just need a pointer and/or primer. PS: If someone knows of a superfast vectorized implementation of Gentleman's WLS algorithm, please point me to it, too. I am still using my old non-vectorized C routines. if this email offends as spam, apologies. regards, /iaw ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
