text: 286,58,1193946899142905857,3257473185,Marcus D. R. Klarqvist ⚪,"New paper by me, @pshufb, @lemire describing the novel positional population count operation using SIMD instructions. <LINK> a) The classic popcount operation computes the number of set bits in a machine word b) The positional popcount operation computes the bitwise histogram of an input stream of words <LINK> Associated code with paper <LINK> Our best approach uses up to 400 times fewer instructions and is up to 50 times faster than baseline code using only regular (non-SIMD) instructions.",https://arxiv.org/abs/1911.02696,"In several fields such as statistics, machine learning, and bioinformatics, categorical variables are frequently represented as one-hot encoded vectors. For example, given 8 distinct values, we map each value to a byte where only a single bit has been set. We are motivated to quickly compute statistics over such encodings. Given a stream of k-bit words, we seek to compute k distinct sums corresponding to bit values at indexes 0, 1, 2, ..., k-1. If the k-bit words are one-hot encoded then the sums correspond to a frequency histogram. This multiple-sum problem is a generalization of the population-count problem where we seek the sum of all bit values. Accordingly, we refer to the multiple-sum problem as a positional population-count. Using SIMD (Single Instruction, Multiple Data) instructions from recent Intel processors, we describe algorithms for computing the 16-bit position population count using less than half of a CPU cycle per 16-bit word. Our best approach uses up to 400 times fewer instructions and is up to 50 times faster than baseline code using only regular (non-SIMD) instructions, for sufficiently large inputs. ","Efficient Computation of Positional Population Counts Using SIMD