In the age of artificial intelligence, algorithms must efficiently cope with vast datasets. We propose a performance-portable implementation of locality-sensitive hashing (LSH), which is an approximate k-nearest neighbor ( KNN) algorithm to speed up the classification on heterogeneous hardware.

Our new library provides a hardware independent, yet efficient and distributed implementation of the LSH algorithm using SYCL and message passing interface (MPI).

The results show that our library can scale on multiple GPUs, achieving a speedup of up to 7.6x on eight GPUs. It supports different SYCL implementations—ComputeCpp, hipSYCL, DPC++—to target different hardware.