111,767 research outputs found
Geographica: A Benchmark for Geospatial RDF Stores
Geospatial extensions of SPARQL like GeoSPARQL and stSPARQL have recently
been defined and corresponding geospatial RDF stores have been implemented.
However, there is no widely used benchmark for evaluating geospatial RDF stores
which takes into account recent advances to the state of the art in this area.
In this paper, we develop a benchmark, called Geographica, which uses both
real-world and synthetic data to test the offered functionality and the
performance of some prominent geospatial RDF stores
MP-STREAM: A Memory Performance Benchmark for Design Space Exploration on Heterogeneous HPC Devices
Sustained memory throughput is a key determinant
of performance in HPC devices. Having an accurate estimate of
this parameter is essential for manual or automated design space
exploration for any HPC device. While there are benchmarks for
measuring the sustained memory bandwidth for CPUs and GPUs,
such a benchmark for FPGAs has been missing. We present
MP-STREAM, an OpenCL-based synthetic micro-benchmark for
measuring sustained memory bandwidth, optimized for FPGAs,
but which can be used on multiple platforms. Our main contribution
is the introduction of various generic as well as device-specific
parameters that can be tuned to measure their effect on memory
bandwidth. We present results of running our benchmark on a
CPU, a GPU and two FPGA targets, and discuss our observations.
The experiments underline the utility of our benchmark for
optimizing HPC applications for FPGAs, and provide valuable
optimization hints for FPGA programmers
Shape from Shading through Shape Evolution
In this paper, we address the shape-from-shading problem by training deep
networks with synthetic images. Unlike conventional approaches that combine
deep learning and synthetic imagery, we propose an approach that does not need
any external shape dataset to render synthetic images. Our approach consists of
two synergistic processes: the evolution of complex shapes from simple
primitives, and the training of a deep network for shape-from-shading. The
evolution generates better shapes guided by the network training, while the
training improves by using the evolved shapes. We show that our approach
achieves state-of-the-art performance on a shape-from-shading benchmark
Towards a property graph generator for benchmarking
The use of synthetic graph generators is a common practice among
graph-oriented benchmark designers, as it allows obtaining graphs with the
required scale and characteristics. However, finding a graph generator that
accurately fits the needs of a given benchmark is very difficult, thus
practitioners end up creating ad-hoc ones. Such a task is usually
time-consuming, and often leads to reinventing the wheel. In this paper, we
introduce the conceptual design of DataSynth, a framework for property graphs
generation with customizable schemas and characteristics. The goal of DataSynth
is to assist benchmark designers in generating graphs efficiently and at scale,
saving from implementing their own generators. Additionally, DataSynth
introduces novel features barely explored so far, such as modeling the
correlation between properties and the structure of the graph. This is achieved
by a novel property-to-node matching algorithm for which we present preliminary
promising results
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