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Real-Time Data Compression for Data Acquisition Systems Applied to the ITER Radial Neutron Camera.
- Source :
- IEEE Transactions on Nuclear Science; Jul2019, Vol. 66 Issue 7, p1324-1329, 6p
- Publication Year :
- 2019
-
Abstract
- To achieve the aim of the international thermonuclear experimental reactor (ITER) radial neutron camera diagnostic, the data acquisition prototype must be compliant with a sustained 2-MHz peak event per each channel. The data are acquired and processed using an IPFN FPGA Mezzanine Card (FMC-AD2-1600) with two digitizer channels of 12-bit resolution and a sampling rate up to 1.6 GSamples/s mounted in an peripheral component interconnect express (PCIe) evaluation board from Xilinx (KC705) installed in the host PC. The acquired data in the event-based data path are streamed to the host through the PCIe $\times 8$ direct memory access with a maximum data throughput per channel $\approx 0.5$ GB/s of raw data (event base), $\approx 1$ GB/s per digitizer, and up to 1.6 GB/s in continuous mode. The prototype architecture comprises a host PC with two KC705 modules and four channels, producing up to 2 GB/s in event mode and up to 3.2 GB/s in continuous mode. To reduce the produced data throughput from host to ITER archiving system, the real-time data compression was evaluated using the LZ4 lossless compression algorithm, which provides compression speed up to 400 MB/s per core. This paper presents the architecture, implementation, and test of the parallel real-time data compression system running in multiple isolated cores. The average space saving and the performance results for long-term acquisitions up to 30 min, using different data block sizes and different number of CPUs, are also presented. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00189499
- Volume :
- 66
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Nuclear Science
- Publication Type :
- Academic Journal
- Accession number :
- 137646647
- Full Text :
- https://doi.org/10.1109/TNS.2019.2899319