Performance and Optimization Abstractions for Large Scale Heterogeneous Systems in the Cactus/Chemora Framework

We describe a set of lower-level abstractions to improve performance on modern large scale heterogeneous systems. These provide portable access to system- and hardware-dependent features, automatically apply dynamic optimizations at run time, and target stencil-based codes used in finite differencing, finite volume, or block-structured adaptive mesh refinement codes. These abstractions include a novel data structure to manage refinement information for block-structured adaptive mesh refinement, an iterator mechanism to efficiently traverse multi-dimensional arrays in stencil-based codes, and a portable API and implementation for explicit SIMD vectorization. These abstractions can either be employed manually, or be targeted by automated code generation, or be used via support libraries by compilers during code generation. The implementations described below are available in the Cactus framework, and are used e.g. in the Einstein Toolkit for relativistic astrophysics simulations

A NEW WAY TO DEFINE FACES DEPENDS ON MULTI-METRIC GRAPHS

Facial detection plays an important role in many applications, such as video surveillance, sex classification, facial recognition. In this paper, we present a new way of determining the face based on a multi-faceted graph. The proposed method uses a multi-level graph to represent the face, so as to improve computational efficiency, making the procedure suitable for large data multimedia databases. While most of the current methods focus on indexing high-dimensional visual features and also focus on scalability limits, using this system to the peer system, image-based retrieval of content in the pouch system to a partner is feasible. Word templates should update the notebook periodically during such an atmosphere, rather than installing storage. Within this paper, we introduce the universal coded system as a single dynamic generation approach, which considers the balance between both discrimination and workload. In addition, dynamic peer-to-peer networks often evolve, and become less stable to retrieve a fixed codebook. In order to improve recovery performance and lower network costs, the printing technology index has been developed. Unlike central environments, the main challenge is that you will be able to get an efficient global codec, such as images distributed over peer-to-peer networks

Approximate Discrete Probability Distribution Representation using a Multi-ResolutionBinary Tree

Computing and storing probabilities is a hard problem as soon as one has to deal with complex distributions over multiples random variables. The problem of efficient representation of probability distributions is central in term of computational efficiency in the field of probabilistic reasoning. The main problem arises when dealing with joint probability distributions over a set of random variables: they are always represented using huge probability arrays. In this paper, a new method based on a binary-tree representation is introduced in order to store efficiently very large joint distributions. Our approach approximates any multidimensional joint distributions using an adaptive discretization of the space. We make the assumption that the lower is the probability mass of a particular region of feature space, the larger is the discretization step. This assumption leads to a very optimized representation in term of time and memory. The other advantages of our approach are the ability to refine dynamically the distribution every time it is needed leading to a more accurate representation of the probability distribution and to an anytime representation of the distribution