Abstract
This paper presents a pipeline algorithm for MPI_Reduce that uses a Run Length Encoding (RLE) scheme to improve the global reduction of sparse floating-point data. The RLE scheme is directly incorporated into the reduction process and causes only low overheads in the worst case. The high throughput of the RLE scheme allows performance improvements when using high performance interconnects, too. Random sample data and sparse vector data from a parallel FEM application is used to demonstrate the performance of the new reduction algorithm for an HPC Cluster with InfiniBand interconnects.
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References
Faraj, A., Yuan, X., Lowenthal, D.: STAR-MPI: Self Tuned Adaptive Routines for MPI Collective Operations. In: ICS 2006: Proc. of the 20th annual international conference on Supercomputing, pp. 199–208. ACM Press, New York (2006)
Pješivac-Grbović, J., Bosilca, G., Fagg, G.E., Angskun, T., Dongarra, J.J.: MPI collective algorithm selection and quadtree encoding. Parallel Computing 33(9), 613–623 (2007)
Worringen, J.: Pipelining and Overlapping for MPI Collective Operations. In: LCN 2003: Proc. of the 28th Annual IEEE International Conference on Local Computer Networks, pp. 548–557. IEEE Computer Soceity, Los Alamitos (2003)
Almási, G., et al.: Optimization of MPI Collective Communication on BlueGene/L Systems. In: ICS 2005: Proc. of the 19th annual international conference on Supercomputing, pp. 253–262 (2005)
Rabenseifner, R., Träff, J.L.: More Efficient Reduction Algorithms for Non-Power-of-Two Number of Processors in Message-Passing Parallel Systems. In: Kranzlmüller, D., Kacsuk, P., Dongarra, J. (eds.) EuroPVM/MPI 2004. LNCS, vol. 3241, pp. 36–46. Springer, Heidelberg (2004)
Calderón, A., García, F., Carretero, J., Fernández, J., Pérez, O.: New Techniques for Collective Communications in Clusters: A Case Study with MPI. In: ICPP 2001: Proc. of the Int. Conf. on Parallel Processing, pp. 185–194. IEEE Computer Society Press, Los Alamitos (2001)
Ke, J., Burtscher, M., Speight, E.: Runtime Compression of MPI Messages to Improve the Performance and Scalability of Parallel Applications. In: SC 2004: Proc. of the ACM/IEEE Conf. on Supercomputing, p. 59. IEEE Computer Society Press, Los Alamitos (2004)
Ratanaworabhan, P., Ke, J., Burtscher, M.: Fast Lossless Compression of Scientific Floating-Point Data. In: DCC 2006: Proceedings of the Data Compression Conference, pp. 133–142. IEEE Computer Society Press, Los Alamitos (2006)
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Hofmann, M., Rünger, G. (2008). MPI Reduction Operations for Sparse Floating-point Data. In: Lastovetsky, A., Kechadi, T., Dongarra, J. (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2008. Lecture Notes in Computer Science, vol 5205. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87475-1_17
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DOI: https://doi.org/10.1007/978-3-540-87475-1_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87474-4
Online ISBN: 978-3-540-87475-1
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