Speaker
Description
The increasing computational power of HPC systems fosters the
development of complex numerical simulations of phenomena in different
domains, such as medicine [1], Oil & Gas [2] and many other fields [3,4,5].
In such applications, a huge amount of data in the form of multidimensional
arrays is produced and need to be analyzed and visualized enabling
researchers to gain insights about the phenomena being studied.
Scientists also generate huge multidimensional arrays through
environmental observations, measurements of physical conditions and
other types of sensors. For instance, satellite data for Earth's weather,
oceans, atmosphere and land [6] are kept in the form of multidimensional
arrays in scientific file formats. Data collected by sensors in physics
experiments, such as the ones conducted in the photon studies by SLAC
National Accelerator Laboratory [7], are also represented and processed in
the form of multidimensional arrays.
Machine learning is another context in which multidimensional arrays are
present. They are the basic input format for the heavily optimized linear
algebra algorithms implemented in deep learning frameworks, such as:
TensorFlow, Keras and Torch. Deep Learning algorithms were able to
achieve superhuman performance for image recognition problems in the
past few years [8], and they are among the most promising alternative for
tackling difficult problems in Natural Language Processing, Image and
Video Recognition, Medical Image Analysis, Recommendation Systems
and many others. Thus, managing these large arrays in the context of deep
learning is a very important task.
The traditional approach for managing data in multidimensional arrays in
scientific experiments is to store them using file formats, such as netCDF
and HDF5. The use of file formats, and not a database management
systems (DBMS), in storing scientific data has been the traditional choice
due to the fact that DBMSs are considered inadequate for scientific data
management. Even specialized scientific data management systems, such
as SciDB [10], are not very well accepted for a myriad of reasons listed in
● the impedance mismatch problem [12,13], that makes the process of
ingesting data into a DBMS very slow.
● the inability to directly access data from visualization tools like
Paraview Catalyst [14] and indexing facilities like FastQuery [13].
● the Inability to directly access data from custom code, which is
necessary for domain specific optimized data analysis.
However, by completely dismissing DBMSs, some nice features also
become unavailable. Including the access for out-the-box parallel
declarative data processing with the usage of query languages and query
optimization, and management of dense and sparse matrices. In this talk,
we will present SAVIME, a Database Management System for Simulation
Analysis and Visualization in-Memory. SAVIME implements a
multi-dimensional array data model and a functional query language. The
system is extensible to support data analytics requirements of numerical
simulation applications.
References
[1] Pablo J. Blanco, M. R. Pivello, S. A. Urquiza, and Raúl A. Feijóo. 2009.
On the potentialities of 3D-1D coupled models in hemodynamics
simulations.Journal of Biomechanics 42, 7 (2009), 919–930.
[2] Cong Tan et al. 2017. CFD Analysis of Gas Diffusion and Ventilation
Protection in Municipal Pipe Tunnel. In Proceedings of the 2017
International Conference on Data Mining, Communications and Information
Technology (DMCIT ’17). ACM, New York, NY, USA, Article 28, 6 pages.
Https: //doi.org/10.1145/3089871.3089904
[3] Igor Adamovich et al. 2015. Kinetic mechanism of molecular energy
transfer and chemical reactions in low-temperature air-fuel plasmas.
Philosophical transactions. Series A, Mathematical, physical, and
engineering sciences 373 (08 2015).
https://doi.org/10.1098/rsta.2014.0336
[4] Mollona, Edoardo, Computer simulation in social sciences, Journal of
Management & Governance, May, 2008, N(2) V(12).
[5] Mitsuo Yokokawa, Ken’ichi Itakura, Atsuya Uno, Takashi Ishihara, and
Yukio Kaneda. 2002. 16.4Tflopss Direct Numerical Simulation of
Turbulence by a Fourier Spectral Method on the Earth Simulator. In
Proceedings of the 2002 ACM/IEEE Conference on Supercomputing (SC
’02). IEEE Computer Society Press, Los Alamitos, CA, USA, 1–17.
http://dl.acm.org/citation.cfm?id=762761.762808 .
[6] R. Ullman and M. Denning. 2012. HDF5 for NPP sensor and
environmental data records. In 2012 IEEE International Geoscience and
Remote Sensing Symposium. 1100–110
[7] Jack Becla, Daniel Wang, and Kian-Tat lim. 2011. Using SciDB to
Support Photon Science Data Analysis. (01 2011).
[8] Dan Ciresan, Alessandro Giusti, Luca M. Gambardella, and Jürgen
Schmidhuber. 2012. Deep Neural Networks Segment Neuronal
Membranes in Electron Microscopy Images. In Advances in Neural
Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou,
and K. Q. Weinberger (Eds.). Curran Associates, Inc., 2843–2851.
[9] Z Zhao. 2014. Automatic library tracking database at NERSC. (2014).
https://www.nersc.gov/assets/altdatNERSC.pdf, J. ACM, Vol. 37, No. 4,
Article 111. Publication date: August 2019.
[10] Paradigm4. 2019. SciDB. http://www.paradigm4.com/ [Online;
accessed 01-sep-2019].
[11] Haoyuan Xing, Sofoklis Floratos, Spyros Blanas, Suren Byna, Prabhat,
Kesheng Wu, and Paul Brown. 2017. ArrayBridge: Interweaving declarative
array processing with high-performance computing. arXiv e-prints , Article
arXiv:1702.08327 (Feb 2017).
[12] Spyros Blanas, Kesheng Wu, Surendra Byna, Bin Dong, and Arie
Shoshani. 2014. Parallel Data Analysis Directly on Scientific File Formats.
In Proceedings of the 2014 ACM SIGMOD International Conference on
Management of Data (SIGMOD ’14) . ACM, New York, NY, USA, 385–396.
[13] Luke Gosink, John Shalf, Kurt Stockinger, Kesheng Wu, and Wes
Bethel. 2006. HDF5-FastQuery: Accelerating Complex Queries on HDF
Datasets Using Fast Bitmap Indices. In Proceedings of the
18thInternational Conference on Scientific and Statistical Database
Management (SSDBM ’06). IEEE Computer Society, Washington, DC,
USA, 149–158
[14] Utkarsh Ayachit, Andrew Bauer, Berk Geveci, Patrick O’Leary,
Kenneth Moreland, Nathan Fabian,and Jeffrey Mauldin. 2015. ParaView
Catalyst: Enabling In Situ Data Analysis and Visualization. In Proceedings
of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale
Analysis and Visualization (ISAV2015) . ACM, New York, NY, USA, 25–29
