In this paper, we demonstrate how new technology from Oracle can be utilized to provide big data analytics acceleration in a streamlined fashion. Specifically, our approach leverages the acceleration capabilities of the Data Analytics Accelerator (DAX) unit provided by Oracle's T7/M7/S7 SPARC processors and the Java Stream API to seamlessly accelerate Java applications by up to 20X, while using drastically fewer resources.

Adaptive mesh refinement (AMR) numerical methods utilizing octree data structures are an important class of HPC applications, in particular the solution of partial differential equations. Much effort goes into the implementation of efficient versions of these types of programs, where the emphasis is often on increasing multi-node performance when utilizing GPUs and coprocessors. By contrast, our analysis aims to characterize these workloads on traditional CPUs, as we believe that single-threaded intra-node performance of critical kernels is still a key factor for achieving performance at scale. Especially irregular workloads such as AMR methods, however, exhibit severe underutilization on general purpose processors. In this paper, we analyze the single core performance of two state-of-the-art, highly scalable adaptive mesh refinement codes, one based on the Fast Multipole Method (FMM) and one based on the Finite Element Method (FEM), when running on a x86 CPU. We examined both scalar and vectorized implementations to identify performance bottlenecks. We demonstrate that vectorization can provide a significant benefit in achieving high performance. The greatest bottleneck to peak performance is the high fraction of non-floating point instructions in the kernels.