Graphs are a ubiquitous part of technology we use daily in systems like GPS graphs help find the shortest path between two points and in social networks, which use them to help users find friends. We'll explain why analyzing these vast networks with possibly billions of entries requires the computing power of GPUs. We'll then discuss the performance of graph algorithms on the GPU and show benchmarking results from several graph frameworks. We'll also cover the RAPIDS roadmap that will help unify these frameworks and make them easy to use and simple to deploy.
This talk will present the results of running the following Graph500 and DARPA Graph Challenge benchmarks and highlight the improvements over other platforms: BFS Graph500 • Single Source Shortest Paths Graph500 • PageRank Pipeline Graph Challenge • Triangle Counting Graph Challenge • K-Truss Graph Challenge The tremendous performance advantages of the DGX-2 platform for deep-learning has recently gained a lot of publicity. However, that is not the only analytic environment that can take advantage of the DGX-2 architecture. Having sixteen fully connected 32GB Volta GPUs presents an intriguing platform for Graph Analytics. The 512GB of combined GPU memory and full NVLink connection between the GPUs offers a number of advantages over a distributed MPI-based approach.
We discuss some of common use cases for AmgX, our toolkit for fast linear solvers on the GPU. AmgX includes Algebraic Multi-Grid methods, Krylov methods, nesting preconditioners, and allows complex composition of the solvers and preconditioners. We also present some recent performance results on NVIDIA® Tesla® K20 and K40 GPUs for large-scale CFD problems of industrial relevance.