Abstract
In many scientific and industrial applications GPGPU (General-Purpose Computing on Graphics Processing Units) programming reported excellent speed-up when compared to traditional CPU (central processing unit) based libraries. However, for data intensive applications this benefit may be much smaller or may completely disappear due to time consuming memory transfers. Up to now, gain from processing on the GPU was noticeable only for problems where data transfer could be compensated by calculations, which usually mean large data sets and complex computations. This paper evaluates a new method of data decompression directly in GPU shared memory which minimizes data transfers on the path from disk, through main memory, global GPU device memory, to GPU processor. The method is successfully applied to pattern matching problems. Results of experiments show considerable speed improvement for large and small data volumes which is a significant step forward in GPGPU computing.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Zukowski, M., Heman, S., Nes, N., Boncz, P.: Super-scalar ram-cpu cache compression. In: Proc. of the 22nd Intern. Conf. on Data Engineering, ICDE 2006, pp. 59–59. IEEE (2006)
Wu, L., Storus, M., Cross, D.: Cs315a: Final project cuda wuda shuda: Cuda compression project. Technical report. Stanford University (March 2009)
Kim, C., Chhugani, J., Satish, N., Sedlar, E., Nguyen, A.D., Kaldewey, T., Lee, V.W., Brandt, S.A., Dubey, P.: Fast: fast architecture sensitive tree search on modern cpus and gpus. In: Proc. of the 2010 Intern. Conf. on Management of Data, pp. 339–350. ACM (2010)
Yan, H., Ding, S., Suel, T.: Inverted index compression and query processing with optimized document ordering. In: Proc. of the 18th Intern. Conf. on World Wide Web, pp. 401–410. ACM (2009)
Delbru, R., Campinas, S., Samp, K., Tummarello, G.: Adaptive frame of reference for compressing inverted lists. Technical report. DERI – Digital Enterprise Research Institute (December 2010)
Harvard IIC. Data and search interface, time sries center (2012), http://timemachine.iic.harvard.edu/
Integral. Truefx (2012), http://www.truefx.com/
Hyndman, R.J.: Time series data library (2012), http://robjhyndman.com/tsdl
Goldberger, A.L., et al.: Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation 101(23), e215-e220
Goldstein, J., Ramakrishnan, R., Shaft, U.: Compressing relations and indexes. In: Proc. of the 14th Intern. Conf. on Data Engineering, pp. 370–379. IEEE (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Przymus, P., Kaczmarski, K. (2012). Improving Efficiency of Data Intensive Applications on GPU Using Lightweight Compression. In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds) On the Move to Meaningful Internet Systems: OTM 2012 Workshops. OTM 2012. Lecture Notes in Computer Science, vol 7567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33618-8_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-33618-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33617-1
Online ISBN: 978-3-642-33618-8
eBook Packages: Computer ScienceComputer Science (R0)