Abstract
Recently, random linear network coding has been widely applied in peer-to-peer network applications. Instead of sharing the raw data with each other, peers in the network produce and send encoded data to each other. As a result, the communication protocols have been greatly simplified, and the applications experience higher end-to-end throughput and better robustness to network churns.Since it is difficult to verify the integrity of the encoded data, such systems can suffer from the famous pollution attack, in which a malicious node can send bad encoded blocks that consist of bogus data. Consequently, the bogus data will be propagated into the whole network at an exponential rate. Homomorphic hash functions (HHFs) have been designed to defend systems from such pollution attacks, but with a new challenge: HHFs require that network coding must be performed in GF(q), where q is a very large prime number. This greatly increases the computational cost of network coding, in addition to the already computational expensive HHFs. This paper exploits the potential of the huge computing power of Graphic Processing Units (GPUs) to reduce the computational cost of network coding and homomorphic hashing. With our network coding and HHF implementation on GPU, we observed significant computational speedup in comparison with the best CPU implementation. This implementation can lead to a practical solution for defending against the pollution attacks in distributed systems.
This research was supported in part by Hong Kong RGC under grant HKBU 210406, and FRG grant HKBU FRG/07-08/II-36.
Chapter PDF
Similar content being viewed by others
References
NVIDIA CUDA Compute Unified Device Architecture: Programming Guide, Version 2.0beta2 (June 2008)
AMD CTM Guide: Technical Reference Manual (2006), http://ati.amd.com/companyinfo/researcher/documents/ATI_CTM_Guide.pdf
Seiler, L., et al.: Larrabee: a many-core x86 architecture for visual computing. ACM Transactions on Graphics 27(3) (August 2008)
GNU MP Arithmetic Library, http://gmplib.org/
Brickell, E.F., Gordon, D.M., McCurley, K.S., Wilson, D.B.: Fast exponentiation with precomputation: algorithms and lower bound. In: Rueppel, R.A. (ed.) EUROCRYPT 1992. LNCS, vol. 658, pp. 200–207. Springer, Heidelberg (1993)
Ahlswede, R., Cai, N., Li, S.R., Yeung, R.W.: Network information flow. IEEE Transactions on Information Theory 46(4), 1204–1216 (2000)
Koetter, R., Medard, M.: An algebraic approach to network coding. IEEE/ACM Transactions on Networking 11(5), 782–795 (2003)
Ho, T., Koetter. R., Médard, M., Karger, D.R., Effros, M.: The benefits of coding over routing in a randomized setting. In: Proceedings of IEEE ISIT (2003)
Li, S.-Y.R., Yueng, R.W., Cai, N.: Linear network coding. IEEE Transactions on Information Theory 49, 371–381 (2003)
Gkantsidis, C., Rodriguez, P.: Network coding for large scale content distribution. In: Proceedings of IEEE INFOCOM 2005 (2005)
Dimakis, A.G., Godfrey, P.B., Wainwright, M.J., Ramchandran, K.: Network coding for distributed storage systems. In: Proceedings of IEEE INFOCOM 2007 (2007)
Wang, M., Li, B.: Lava: a reality check of network coding in peer-to-peer live streaming. In: Proceedings of IEEE INFOCOM 2007 (2007)
Krohn, M., FreedMan, M., Mazieres, D.: On-the-fly verification of rateless erasure codes for efficient content distribution. In: Proceedings of IEEE Symposium on Security and Privacy, Berkeley, CA (2004)
Gkantsidis, C., Rodriguez, P.: Cooperative security for network coding file distribution. In: Proceedings of IEEE INFOCOM 2006 (2006)
Li, Q., Chiu, D.-M., Lui, J.C.S.: On the practical and security issues of batch content distribution via network coding. In: Proceedings of IEEE ICNP 2006, pp. 158–167 (2006)
Yu, Z., Wei, Y., Ramkumar, B., Guan, Y.: An efficient signature-based scheme for securing network coding against pollution attacks. In: Proceedings of IEEE INFOCOM 2008 (April 2008)
Shojania, H., Li, B.: Parallelized progressive network coding with hardware acceleration. In: Proceedings of the 15th International Workshop on Quality of Service, IWQoS (2007)
Chu, X., Zhao, K., Wang, M.: Massively parallel network coding on GPUs. In: Proceedings of the 27th IEEE IPCCC (December 2008)
Ryoo, S., Rodrigues, C.I., Baghsorkhi, S.S., Stone, S.S., Kirk, D.B., Hwu, W.: Optimization principles and application performance evaluation of a multithreaded GPU using CUDA. In: Proceedings of ACM PPoPP 2008 (Feburary 2008)
Owens, J.D., Houston, M., Luebke, D., Green, S., Stone, J.E., Phillips, J.C.: GPU computing. In: IEEE Proceedings, May 2008, pp. 879–899 (2008)
Katti, S., Katabi, D., Balakrishna, H., Medard, M.: Symbol-level network coding for wireless mesh networks. In: Proceedings of ACM Sigcomm 2008 (August 2008)
Shojania, H., Li, B., Wang, X.: Nuclei: GPU-accelerated Many-core Network Coding. In: Proceedings of IEEE INFOCOM 2009 (April 2009)
Volkov, V., Demmel, J.W.: Benchmarking GPUs to tune dense linear algebra. In: Dolev, S., Haist, T., Oltean, M. (eds.) OSC 2008. LNCS, vol. 5172. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 IFIP International Federation for Information Processing
About this paper
Cite this paper
Chu, X., Zhao, K., Wang, M. (2009). Practical Random Linear Network Coding on GPUs. In: Fratta, L., Schulzrinne, H., Takahashi, Y., Spaniol, O. (eds) NETWORKING 2009. NETWORKING 2009. Lecture Notes in Computer Science, vol 5550. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01399-7_45
Download citation
DOI: https://doi.org/10.1007/978-3-642-01399-7_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01398-0
Online ISBN: 978-3-642-01399-7
eBook Packages: Computer ScienceComputer Science (R0)