Using hybrid GPU/CPU kernel splitting to accelerate spherical convolutions

We present a general method for accelerating by more than an order of magnitude the convolution of pixelated functions on the sphere with a radially-symmetric kernel. Our method splits the kernel into a compact real-space component and a compact spherical harmonic space component. These components can then be convolved in parallel using an inexpensive commodity GPU and a CPU. We provide models for the computational cost of both real-space and Fourier space convolutions and an estimate for the approximation error. Using these models we can determine the optimum split that minimizes the wall clock time for the convolution while satisfying the desired error bounds. We apply this technique to the problem of simulating a cosmic microwave background (CMB) anisotropy sky map at the resolution typical of the high resolution maps produced by the Planck mission. For the main Planck CMB science channels we achieve a speedup of over a factor of ten, assuming an acceptable fractional rms error of order 1.e-5 in the power spectrum of the output map.Comment: 9 pages, 11 figures, 1 table, accepted by Astronomy & Computing w/ minor revisions. arXiv admin note: substantial text overlap with arXiv:1211.355

An integrated visualization environment for the virtual observatory: Current status and future directions

Visual exploration and discovery applications are invaluable tools to provide prompt and intuitive insights into the intrinsic data characteristics of modern astronomy and astrophysics datasets. Due to the massively large and highly complex datasets, various technical challenges are involved to reach, e.g. interactivity, integration, navigation and collaboration. This paper describes a number of approaches to address these challenges, and focuses on the current status of VisIVO (Visualization Interface for the Virtual Observatory) concentrating on the provided tools ranging from a desktop application to a science gateway and a mobile application. We emphasize the latest developments made in the context of past and current international European funded projects and highlight planned future developments towards further integration within the framework of the Virtual Observatory

OCTOPUS: Efficient Query Execution on Dynamic Mesh Datasets

Scientists in many disciplines use spatial mesh models to study physical phenomena. Simulating natural phenomena by changing meshes over time helps to understand and predict future behavior of the phenomena. The higher the precision of the mesh models, the more insight do the scientists gain and they thus continuously increase the detail of the meshes and build them as detailed as their instruments and the simulation hardware allow. In the process, the data volume also increases, slowing down the execution of spatial range queries needed to monitor the simulation considerably. Indexing speeds up range query execution, but the overhead to maintain the indexes is considerable because almost the entire mesh changes unpredictably at every simulation step. Using a simple linear scan, on the other hand, requires accessing the entire mesh and the performance deteriorates as the size of the dataset grows. In this paper we propose OCTOPUS, a strategy for executing range queries on mesh datasets that change unpredictably during simulations. In OCTOPUS we use the key insight that the mesh surface along with the mesh connectivity is sufficient to retrieve accurate query results efficiently. With this novel query execution strategy, OCTOPUS minimizes index maintenance cost and reduces query execution time considerably. Our experiments show that OCTOPUS achieves a speedup between 7.2x and 9.2x compared to the state of the art and that it scales better with increasing mesh dataset size and detail

Perturbative and Non-Perturbative Studies of Reheating in the Curvaton Scenario

Siirretty Doriast

Succinct data structures for dynamics strings

A new simple algorithm for optimal embedding of complete binary trees into hypercubes as well as a node-by-node algorithm for embedding of nearly complete binary trees into hypercubes are presented