Search

We engineer a GPU implementation of a B-Tree that supports concurrent queries (point, range, and successor) and updates (insertions and deletions). Our B-tree outperforms the state of the art, a GPU log-structured merge tree (LSM) and a GPU sorted array. In particular, point and range queries are significantly faster than in a GPU LSM (the GPU LSM does not implement successor queries). Furthermore, B-Tree insertions are also faster than LSM and sorted array insertions unless insertions come in batches of more than roughly 100k. Because we cache the upper levels of the tree, we achieve lookup throughput that exceeds the DRAM bandwidth of the GPU. We demonstrate that the key limiter of performance on a GPU is contention and describe the design choices that allow us to achieve this high performance.

Our goal is the registration of multiple 3D point clouds obtained from LIDAR scans of underground mines. Such a capability is crucial to the surveying and planning operations in mining. Often, the point clouds only partially overlap and initial alignment is unavailable. Here, we propose an interactive user-assisted point cloud registration system. Guided by the system, the user's role is simply to identify and search for overlapping regions across the point clouds. Specifically, given two point sets, the user clicks on a point in one set, then simply hovers the mouse on the other set to find a matching point. Each mouse position gives rise to a translation, and our system instantly optimises the rotation that aligns the point clouds. Assuming that each individual point set is horizontally levelled with the ground by the level compensator on the LIDAR device, given a candidate 3D translation only one angular parameter needs to be estimated to rotationally align two 3D point sets. We propose a fast rotation search algorithm that delivers globally optimal results in real time. Our method conducts branch-and-bound optimisation with a novel bounding function whose evaluation amounts to simple sorted array operations. This provides smooth and accurate feedback to the user's search for overlapping regions. Our system is intuitive and helps to accelerate the registration of multiple scans.

We describe the challenges arising from tracking dark matter particles in state of the art cosmological simulations. We are in the process of running the Indra suite of simulations, with an aggregate count of more than 35 trillion particles and 1.1PB of total raw data volume. However, it is not enough just to store the particle positions and velocities in an efficient manner -- analyses also need to be able to track individual particles efficiently through the temporal history of the simulation. The required inverted indices can easily have raw sizes comparable to the original simulation.We explore various strategies on how to create an efficient index for such data, using additional insight from the physical properties of the particle motions for a greatly compressed data representation. The basic particle data are stored in a relational database in course-grained containers corresponding to leaves of a fixed depth oct-tree labeled by their Peano-Hilbert index. Within each container the individual objects are sorted by their Lagrangian identifier. Thus each particle has a multi-level address: the PH key of the container and the index of the particle within the sorted array (the slot).Given the nature of the cosmological simulations and choice of the PH-box sizes, in consecutive snapshots particles can only cross into spatially adjacent boxes. Also, the slot number of a particle in adjacent snapshots is adjusted up or down by typically a small number. As a result, a special version of delta encoding over the multi-tier address already results in a dramatic reduction of data that needs to be stored. We follow next with an efficient bit-compression, adapting to the statistical properties of the two-part addresses, achieving a final compression ratio better than a factor of 9. The final size of the full inverted index is projected to be 22.5 TB for a petabyte ensemble of simulations.

In this paper we describe a Patricia tree-based B-tree variant suitable for OLTP. In this variant, each page of the B-tree contains a local Patricia tree instead of the usual sorted array of keys. It has been implemented in iAnywhere ASA Version 8.0. Preliminary experience has shown that these indexes can provide significant space and performance benefits over existing ASA indexes.

Segmented sort, as a generalization of classical sort, orders a batch of independent segments in a whole array. Along with the wider adoption of manycore processors for HPC and big data applications, segmented sort plays an increasingly important role than sort. In this paper, we present an adaptive segmented sort mechanism on GPUs. Our mechanisms include two core techniques: (1) a differentiated method for different segment lengths to eliminate the irregularity caused by various workloads and thread divergence; and (2) a register-based sort method to support N-to-M data-thread binding and in-register data communication. We also implement a shared memory-based merge method to support non-uniform length chunk merge via multiple warps. Our segmented sort mechanism shows great improvements over the methods from CUB, CUSP and ModernGPU on NVIDIA K80-Kepler and TitanX-Pascal GPUs. Furthermore, we apply our mechanism on two applications, i.e., suffix array construction and sparse matrix-matrix multiplication, and obtain obvious gains over state-of-the-art implementations.