Scalable Inference for Sparse Deep Neural Networks using Kokkos Kernels

Abstract

Over the last decade, hardware advances have led to the feasibility of training and inference for very large deep neural networks. Sparsified deep neural networks (DNNs) can greatly reduce memory costs and increase throughput of standard DNNs, if loss of accuracy can be controlled. The IEEE HPEC Sparse Deep Neural Network Graph Challenge serves as a testbed for algorithmic and implementation advances to maximize computational performance of sparse deep neural networks. We base our sparse network for DNNs, KK-SpDNN, on the sparse linear algebra kernels within the Kokkos Kernels library. Using the sparse matrix-matrix multiplication in Kokkos Kernels allows us to reuse a highly optimized kernel. We focus on reducing the single node and multi-node runtimes for 12 sparse networks. We test KK-SpDNN on Intel Skylake and Knights Landing architectures and see 120-500x improvement on single node performance over the serial reference implementation. We run in data-parallel mode with MPI to further speed up network inference, ultimately obtaining an edge processing rate of 1.16e+12 on 20 Skylake nodes. This translates to a 13x speed up on 20 nodes compared to our highly optimized multithreaded implementation on a single Skylake node.