Performance-Portable Distributed k-Nearest Neighbors using Locality-Sensitive Hashing and SYCL
TECH TALK
Performance-Portable Distributed k-Nearest Neighbors using Locality-Sensitive Hashing and SYCL
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.
Speaker(s)
Marcel Breyer
Marcel Breyer is a PhD student at the University of Stuttgart, Germany. His main field of research is on performance portability on heterogeneous hardware, which includes new applications of SYCL. He has contributed performance-portable k-Nearest Neighbors implementations for vast data sets.
