28 research outputs found
SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things
Time-series data has an increasingly growing usage in Industrial Internet of Things (IIoT) and large-scale scientific experiments. Managing time-series data needs a storage engine that can keep up with their constantly growing volumes while providing an acceptable query latency. While traditional ACID databases favor consistency over performance, many time-series databases with novel storage engines have been developed to provide better ingestion performance and lower query latency. To understand how the unique design of a time-series database affects its performance, we design SciTS, a highly extensible and parameterizable benchmark for time-series data. The benchmark studies the data ingestion capabilities of time-series databases especially as they grow larger in size. It also studies the latencies of 5 practical queries from the scientific experiments use case. We use SciTS to evaluate the performance of 4 databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and PostgreSQL
Reviewing GPU architectures to build efficient back projection for parallel geometries
Back-Projection is the major algorithm in Computed Tomography to reconstruct images from a set of recorded projections. It is used for both fast analytical methods and high-quality iterative techniques. X-ray imaging facilities rely on Back-Projection to reconstruct internal structures in material samples and living organisms with high spatial and temporal resolution. Fast image reconstruction is also essential to track and control processes under study in real-time. In this article, we present efficient implementations of the Back-Projection algorithm for parallel hardware. We survey a range of parallel architectures presented by the major hardware vendors during the last 10 years. Similarities and differences between these architectures are analyzed and we highlight how specific features can be used to enhance the reconstruction performance. In particular, we build a performance model to find hardware hotspots and propose several optimizations to balance the load between texture engine, computational and special function units, as well as different types of memory maximizing the utilization of all GPU subsystems in parallel. We further show that targeting architecture-specific features allows one to boost the performance 2–7 times compared to the current state-of-the-art algorithms used in standard reconstructions codes. The suggested load-balancing approach is not limited to the back-projection but can be used as a general optimization strategy for implementing parallel algorithms
Balancing Load of GPU Subsystems to Accelerate Image Reconstruction in Parallel Beam Tomography
On the initiation of lightning in thunderclouds
The relationship of lightning and elementary particle fluxes in the thunderclouds is not fully understood to date. Using the particle beams (the so-called Thunderstorm Ground Enhancements-TGEs) as a probe we investigate the characteristics of the interrelated atmospheric processes. The well-known effect of the TGE dynamics is the abrupt termination of the particle flux by the lightning flash. With new precise electronics, we can see that particle flux decline occurred simultaneously with the rearranging of the charge centers in the cloud. The analysis of the TGE energy spectra before and after the lightning demonstrates that the high-energy part of the TGE energy spectra disappeared just after lightning. The decline of particle flux coincides on millisecond time scale with first atmospheric discharges and we can conclude that Relativistic Runaway Electron Avalanches (RREA) in the thundercloud assist initiation of the negative cloud to ground lightning. Thus, RREA can provide enough ionization to play a significant role in the unleashing of the lightning flash
Low-latency Visual Previews of Large Synchrotron Micro-CT Datasets
The unprecedented rate at which synchrotron radiation facilities are
producing micro-computed (micro-CT) datasets has resulted in an overwhelming
amount of data that scientists struggle to browse and interact with in
real-time. Thousands of arthropods are scanned into micro-CT within the NOVA
project, producing a large collection of gigabyte-sized datasets. In this work,
we present methods to reduce the size of this data, scaling it from gigabytes
to megabytes, enabling the micro-CT dataset to be delivered in real-time. In
addition, arthropods can be identified by scientists even after implementing
data reduction methodologies. Our initial step is to devise three distinct
visual previews that comply with the best practices of data exploration.
Subsequently, each visual preview warrants its own design consideration,
thereby necessitating an individual data processing pipeline for each. We aim
to present data reduction algorithms applied across the data processing
pipelines. Particularly, we reduce size by using the multi-resolution
slicemaps, the server-side rendering, and the histogram filtering approaches.
In the evaluation, we examine the disparities of each method to identify the
most favorable arrangement for our operation, which can then be adjusted for
other experiments that have comparable necessities. Our demonstration proved
that reducing the dataset size to the megabyte range is achievable without
compromising the arthropod's geometry information
A Heterogeneous FPGA/GPU Architecture for Real-Time Data Analysis and Fast Feedback Systems
We propose a versatile and modular approach for a real- time data acquisition and evaluation system for monitoring and feedback control in beam diagnostic and photon sci- ence experiments. Our hybrid architecture is based on an FPGA readout card and GPUs for data processing. To in- crease throughput, lower latencies and reduce overall system strain, the FPGA is able to write data directly into the GPU’s memory. After real-time data analysis the GPU writes back results back to the FPGA for feedback systems or to the CPU host system for subsequent processing. The communication and scheduling processing units are handled transparently by our processing framework which users can customize and extend. Although the system is designed for real-time capability purposes, the modular approach also allows stan- dalone usage for high-speed off-line analysis. We evaluated the performance of our solution measuring both processing times of data analysis algorithms used with beam instrumen- tation detectors as well as transfer times between FPGA and GPU. The latter suggests system throughputs of up to 6 GB/s with latencies down to the microsecond range, thus making it suitable for fast feedback systems
Crystalline phase discriminating neutron tomography using advanced reconstruction methods
Time-of-flight neutron imaging offers complementary attenuation contrast to
X-ray computed tomography (CT), coupled with the ability to extract additional
information from the variation in attenuation as a function of neutron energy
(time of flight) at every point (voxel) in the image. In particular Bragg edge
positions provide crystallographic information and therefore enable the
identification of crystalline phases directly. Here we demonstrate Bragg edge
tomography with high spatial and spectral resolution. We propose a new
iterative tomographic reconstruction method with a tailored regularisation term
to achieve high quality reconstruction from low-count data, where conventional
filtered back-projection (FBP) fails. The regularisation acts in a separated
mode for spatial and spectral dimensions and favours characteristic piece-wise
constant and piece-wise smooth behaviour in the respective dimensions. The
proposed method is compared against FBP and a state-of-the-art regulariser for
multi-channel tomography on a multi-material phantom. The proposed new
regulariser which accommodates specific image properties outperforms both
conventional and state-of-the-art methods and therefore facilitates Bragg edge
fitting at the voxel level. The proposed method requires significantly shorter
exposure to retrieve features of interest. This in turn facilitates more
efficient usage of expensive neutron beamline time and enables the full
utilisation of state-of-the-art high resolution detectors
Visualisation of Ultrasound Computer Tomography Breast Dataset
Medical visualisation plays a vital role in diagnosing and detecting early symptoms. In particular, visualising the anatomy of breast model allows doctors or practitioners to identify first signs of the breast cancer. However, despite the advancement in visualisation techniques, most standard visualisation approaches in the medical field still rely on analysing 2D images which lack spatial information. In this paper, we present an interactive web-based 3D visualisation tool for ultrasound computer tomography (USCT) breast dataset. We base our implementation on the Web-based Graphics Language (WebGL) technology that utilises the GPU parallel architecture. The tool serves as a common platform among research collaborators to analyse
and share findings on their dataset. We render the data using state-of-the-art algorithms of interactive computer graphics and produce results with quality comparable to the desktop application. Aside from that, our tool enables researchers to perform arbitrary view slicing, modality thresholding and multiple rendering modes. In the evaluation, our tool maintains an interactive frame rate above 30 fps on a standard desktop
Ultrafast linear array detector for real-time imaging
KALYPSO is a novel detector operating at line rates above 10 Mfps. It consists of a detector board connected to FPGA based readout card for real time data processing. The detector board holds a Si or InGaAs linear array sensor, with spectral sensitivity ranging from 400 nm to 2600 nm, which is connected to a custom made front-end ASIC. A FPGA readout framework performs the real time data processing. In this contribution, we present the detector system, the readout electronics and the heterogeneous infrastructure for machine learning processing. The detector is currently in use at several synchrotron facilities for beam diagnostics as well as for single-pulse laser characterizations. Thanks to the shot-to-shot capability over long time scale, new attractive applications are open up for imaging in biological and medical research
Quantification of the uncertainty due to the geometrical influence factors for dimensional measurements using computed tomography
status: accepte