In the age of AI, algorithms must efficiently cope with vast data sets. We propose a performance-portable implementation of Locality-Sensitive Hashing (LSH), an approximate k-nearest neighbors algorithm to speed up classification on heterogeneous hardware.
Our new library provides a hardware-independent, yet efficient and distributed implementation of the LSH algorithm using SYCL and MPI.
The results show that our library can scale on multiple GPUs achieving a speedup of up to 7.6 on 8 GPUs. It supports different SYCL implementations—ComputeCpp, hipSYCL, DPC++—to target different hardware.