Multi-Application and Large Shared Memory in a Mechatronic System for Massive Computation

Abstract:

Recent mechatronic systems, such as inspection machines or 3D imaging apparatuses, acquire and compute massive data for final results. A host in the mechatronic system is commonly composed of multiple hardware devices which interface with high-speed external signals. The host and the devices usually have large memory, so efficient data management is important due to data storage and transfer. In our software structure, each device is managed by respective application and large shared memory (LSM) is allocated in the host for the massive data. The shared memory is accessible from the device applications. Actions of the mechatronic system are driven by combining and broadcasting events through and inter-process communication (IPC). The model with LSM and IPC was applied to a 3D RF imaging system. We expect the proposed model can also be applied to machine vision with big image and engineering simulation with hardware accelerators.

• Periodical: Applied Mechanics and Materials (Volume 307)
• Pages: 18-22
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.307.18
• Online since: February 2013
• Authors: Hyung Tae Kim, Kyung Chan Jin
• Keywords: 3D Image, Big Data Manipulation, Digitizer, Graphic Processing Unit (GPU), Inter-Process Communication, Large Shared Memory, Multiple Devices, Reconstruction, RF Imaging

References

[1] S. Roy, V.C. Chaudary, S. Jia and P. Menon, in: Application based Evaluation of Distributed Shared Memory Versus Message Passing Proc. of 12th ISCA IC on Parallel and Distributed Computing Systems (1999), p.15.

[2] I.K. Park, N. Singhal, M.H. Lee, S. Cho, and C.W. Kim, in: Design and Performance Evaluation of Image Processing Algorithms on GPUs IEEE Transactions on Parallel and Distributed Systems, Vol. 22-1 (2011), p.91.

DOI: 10.1109/tpds.2010.115

[3] D. Houzet, A. Fatni and J.L. Basille, in: Image Processing BCI-based Shared Memory Architecture Design Int. work. On Computer Architecture for Machine perception (1997), p.244.

DOI: 10.1109/camp.1997.632030

[4] T.C. S Sean and B. Schmidt, in: Combining Message-passing and Inter-process communication for Efficient Parallel Medical Image Analysis Proc. of the SPIE Vol. 5558 (2004), p.181.

DOI: 10.1117/12.559610

[5] J.S. Kole and F.J. Beekman, in: Statistical image reconstruction for cone-beam X-ray CT on a shared memory computation platform Nuclear Sci. Symp. Conf. Record Vol. 4 (2004), p.2498.

DOI: 10.1109/nssmic.2004.1462762

[6] Y. Zheng and P.C. Doerschuk, in: A Parallel Software Toolkit for Statistical 3-D Virus Reconstructions from Cryo Electron Microscopy Images Using Computer Clusters with Multi-core Shared-Memory Nodes Proc. of Int. Symp. On Parallel and Distributed Processing (2009).

DOI: 10.1109/ipdps.2008.4536242

[7] R. Mahmoudi and M. Akil, in: Enhanced computation method of topological smoothing on shared memory parallel machines J. on Image and Video Processing Vol. 2011-16 (2011).

DOI: 10.1186/1687-5281-2011-16

[8] R. Barrera, Image fusion: Shared memory supports flexible, multiple sensor imaging systems Embedded Computing Design (2006).

[9] O. Meynberg, D. Rosenbaum, F. Kurz, J. Leitloff and U. Thomas, in: Efficient Image Data Processing Based on an Airborne Distributed System Architecture Proc. of ISPRS Vol. XXXVIII-1 (2010).

[10] D.R. Jones, E.R. Jurrus, B.D. Moon, and K.A. Perrine, in: Gigapixel-size Real-time Interactive Image Processing with Parallel Computers Proc. of the Int. Parallel and Distributed Processing Symp. (2003).

DOI: 10.1109/ipdps.2003.1213426

[11] Information on http: /www. makelinux. net/alp/035 , 5. 1. 2 The Memory Model.

[12] D. Sheen, D. McMakin and T.E. Hall, Cylindrical millimeter wave technique for concealed weapon detection Proc. of AIPR Workshop Vol. SPIE-3240 (1998), p.242.

DOI: 10.1117/12.300061

[13] V.K. Veligatla, in: GPUs for Radio Imaging GPU Tech. Conf. (2012).