Performance-Portable Distributed KNN Using LSH and SYCL*
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.
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 datasets.
- Distributed GPU and LSH Using SYCL on GitHub
- Article: Performance-Portable Distributed k-Nearest Neighbors Using Locality-Sensitive Hashing and SYCL
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