7,808 research outputs found
Performance of VIDEBAS in an operational environment
VIDEBAS is a relational database management system in which a database consists of two parts, namely a “real-only” and an “update” part. The first part remains unmodified until the next reorganization and exploits redundancy to achieve fast access to data. A prototype of VIDEBAS has been built. In this paper a performance comparison between this relational system and a DBTG-system (UDS) is made. The used external memory and the number of page accesses to retrieve and update tuples is estimated. Although it is commonly assumed that in an operational environment relational systems are slower than network systems the opposite appears. On the other hand UDS needs less external memory
Dynamic Animations of Journal Maps: Indicators of Structural Changes and Interdisciplinary Developments
The dynamic analysis of structural change in the organization of the sciences
requires methodologically the integration of multivariate and time-series
analysis. Structural change--e.g., interdisciplinary development--is often an
objective of government interventions. Recent developments in multi-dimensional
scaling (MDS) enable us to distinguish the stress originating in each
time-slice from the stress originating from the sequencing of time-slices, and
thus to locally optimize the trade-offs between these two sources of variance
in the animation. Furthermore, visualization programs like Pajek and Visone
allow us to show not only the positions of the nodes, but also their relational
attributes like betweenness centrality. Betweenness centrality in the vector
space can be considered as an indicator of interdisciplinarity. Using this
indicator, the dynamics of the citation impact environments of the journals
Cognitive Science, Social Networks, and Nanotechnology are animated and
assessed in terms of interdisciplinarity among the disciplines involved
Importance of Explicit Vectorization for CPU and GPU Software Performance
Much of the current focus in high-performance computing is on
multi-threading, multi-computing, and graphics processing unit (GPU) computing.
However, vectorization and non-parallel optimization techniques, which can
often be employed additionally, are less frequently discussed. In this paper,
we present an analysis of several optimizations done on both central processing
unit (CPU) and GPU implementations of a particular computationally intensive
Metropolis Monte Carlo algorithm. Explicit vectorization on the CPU and the
equivalent, explicit memory coalescing, on the GPU are found to be critical to
achieving good performance of this algorithm in both environments. The
fully-optimized CPU version achieves a 9x to 12x speedup over the original CPU
version, in addition to speedup from multi-threading. This is 2x faster than
the fully-optimized GPU version.Comment: 17 pages, 17 figure
Generating optimized Fourier interpolation routines for density function theory using SPIRAL
© 2015 IEEE.Upsampling of a multi-dimensional data-set is an operation with wide application in image processing and quantum mechanical calculations using density functional theory. For small up sampling factors as seen in the quantum chemistry code ONETEP, a time-shift based implementation that shifts samples by a fraction of the original grid spacing to fill in the intermediate values using a frequency domain Fourier property can be a good choice. Readily available highly optimized multidimensional FFT implementations are leveraged at the expense of extra passes through the entire working set. In this paper we present an optimized variant of the time-shift based up sampling. Since ONETEP handles threading, we address the memory hierarchy and SIMD vectorization, and focus on problem dimensions relevant for ONETEP. We present a formalization of this operation within the SPIRAL framework and demonstrate auto-generated and auto-tuned interpolation libraries. We compare the performance of our generated code against the previous best implementations using highly optimized FFT libraries (FFTW and MKL). We demonstrate speed-ups in isolation averaging 3x and within ONETEP of up to 15%
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