1Visualization of multidimensional data presents special challenges for the design of efficient out-of-core data access. Elements that are nearby in the visualization may not be nearby in the underlying data file, which can severely tax the operating system's disk cache. The Granite Scientific Database System can address these problems because it is aware of the organization of the data on disk, and it knows the visualization method's pattern of access. The access pattern is expressed using a toolkit of iterators that both describe the access pattern and perform the iteration itself. Because our system has knowledge of both the data organization and the access pattern, we are able to provide significant performance improvements while hiding the details of out-of-core access from the visualization programmer. This paper presents a brief description of our disk access system placing special emphasis on the benefits offered to a visualization application. We describe a simple demonstration application that shows dramatic performance improvements when used with the 39GB Visible Woman Dataset.

Sparse matrix computations are among the most important computational patterns, commonly used in geometry processing, physical simulation, graph algorithms, and other situations where sparse data arises. In many cases, the structure of a sparse matrix is known a priori, but the values may change or depend on inputs to the algorithm. We propose a new methodology for compile-time specialization of algorithms relying on mixing sparse and dense linear algebra operations, using an extension to the widely-used open source Eigen package. In contrast to library approaches optimizing individual building blocks of a computation (such as sparse matrix product), we generate reusable sparsity-specific implementations for a given algorithm, utilizing vector intrinsics and reducing unnecessary scanning through matrix structures. We demonstrate the effectiveness of our technique on a benchmark of artificial expressions to quantitatively evaluate the benefit of our approach over the state-ofthe-art library Intel MKL. To further demonstrate the practical applicability of our technique we show that our technique can improve performance, with minimal code changes, for mesh smoothing, mesh parametrization, volumetric deformation, optical flow, and computation of the Laplace operator.

We introduce a code generator that converts unoptimized C++ code operating on sparse data into vectorized and parallel CPU or GPU kernels. Our approach unrolls the computation into a massive expression graph, performs redundant expression elimination, grouping, and then generates an architecture-specific kernel to solve the same problem, assuming that the sparsity pattern is fixed, which is a common scenario in many applications in computer graphics and scientific computing. We show that our approach scales to large problems and can achieve speedups of two orders of magnitude on CPUs and three orders of magnitude on GPUs, compared to a set of manually optimized CPU baselines. To demonstrate the practical applicability of our approach, we employ it to optimize popular algorithms with applications to physical simulation and interactive mesh deformation.

Bioimpedance spectroscopy (BIS) has become an important clinical indicator for monitoring the pathological status of biological tissues, and multifrequency simultaneous measurement of BIS may provide more accurate diagnostic information compared with the traditional frequency-sweep measurement technology. This paper proposes a BIS multifrequency simultaneous measurement method based on multifrequency mixed (MFM) signal excitation and a Nuttall windowed interpolation FFT algorithm. Firstly, the excitation source adopts the nine-frequency MFM signalf(9,t), which has excellent spectral characteristic and is very suitable for BIS measurement. On this basis, a Nuttall window is adopted to truncate sample data, and an interpolation FFT algorithm based on Nuttall window is built to perform spectral analysis, in which the parameter correction formula is provided based on polynomial approximation. A BIS measurement simulation experiment is performed on an RC three-element equivalent circuit, and results on the 9 primary harmonic frequencies ranging from 3.9 kHz to 1 MHz show a high accuracy with the impedance amplitude relative error|Ez|<0.3%, and the phase absolute error|Ep|<0.1°. This paper validates the feasibility of BIS multifrequency simultaneous measurement method and establishes an algorithm foundation for the development of practical broadband BIS measurement system.

No abstract