Abstract
The digital signal processing (DSP) applications are one of the biggest consumers of computing. They process a big data volume which is represented with a high accuracy. They use complex algorithms, and must satisfy a time constraints in most of cases. In the other hand, it’s necessary today to use parallel and heterogeneous architectures in order to speedup the processing, where the best examples are the supercomputers ”Tianhe-2” and ”Titan” from the top500 ranking. These architectures could contain several connected nodes, where each node includes a number of generalist processor (multi-core) and a number of accelerators (many-core) to finally allows several levels of parallelism. However, for DSP programmers, it’s still complicated to exploit all these parallelism levels to reach good performance for their applications. They have to design their implementation to take advantage of all heterogeneous computing units, taking into account the architecture specificities of each of them: communication model, memory management, data management, jobs scheduling and synchronization ... etc. In the present work, we characterize DSP applications, and based on their distinctiveness, we propose a high level visual programming model and an execution model in order to drop down their implementations and in the same time make desirable performances.
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Augonnet, C., Thibault, S., Namyst, R.: StarPU: a Runtime System for Scheduling Tasks over Accelerator-Based Multicore Machines. Research Report RR-7240. INRIA (2010)
Bhattacharya, B., Bhattacharyya, S.: Parameterized dataflow modeling for dsp systems. Trans. Sig. Proc. 49(10), 2408–2421 (2001), http://dx.doi.org/10.1109/78.950795
Bueno, J., Martinell, L., Duran, A., Farreras, M., Martorell, X., Badia, R.M., Ayguade, E., Labarta, J.: Productive cluster programming with ompss. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011, Part I. LNCS, vol. 6852, pp. 555–566. Springer, Heidelberg (2011), http://dl.acm.org/citation.cfm?id=2033345.2033405
Chandra, R., Dagum, L., Kohr, D., Maydan, D., McDonald, J., Menon, R.: Parallel Programming in OpenMP. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Chen, W.Y.: Optimizing Partitioned Global Address Space Programs for Cluster Architectures. Ph.D. thesis, EECS Department, University of California, Berkeley (December 2007), http://www.eecs.berkeley.edu/Pubs/TechRpts/2007/EECS-2007-140.html
Flynn, M.: Some computer organizations and their effectiveness. IEEE Transactions on Computers C-21(9), 948–960 (1972)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. Pattern Anal. Mach. Intell. 20(11), 1254–1259 (1998), http://dx.doi.org/10.1109/34.730558
Kirk, D.B., Hwu, W.M.W.: Programming Massively Parallel Processors: A Hands-on Approach, 1st edn. Morgan Kaufmann Publishers Inc., San Francisco (2010)
Lee, E., Messerschmitt, D.: Static scheduling of synchronous data flow programs for digital signal processing. IEEE Transactions on Computers C-36(1), 24–35 (1987)
Munshi, A., Gaster, B., Mattson, T., Ginsburg, D.: OpenCL Programming Guide. OpenGL, Pearson Education (2011), http://books.google.fr/books?id=M-Sve_KItQwC
Pacheco, P.S.: Parallel Programming with MPI. Morgan Kaufmann Publishers Inc., San Francisco (1996)
Parhi, K., Messerschmitt, D.: Static rate-optimal scheduling of iterative data-flow programs via optimum unfolding. IEEE Transactions on Computers 40(2), 178–195 (1991)
Reinders, J.: Intel threading building blocks - outfitting C++ for multi-core processor parallelism. O’Reilly (2007)
Sanders, J., Kandrot, E.: CUDA by Example: An Introduction to General-Purpose GPU Programming, 1st edn. Addison-Wesley Professional (2010)
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Mansouri, F., Huet, S., Houzet, D. (2014). A Visual Programming Model to Implement Coarse-Grained DSP Applications on Parallel and Heterogeneous Clusters. In: Lopes, L., et al. Euro-Par 2014: Parallel Processing Workshops. Euro-Par 2014. Lecture Notes in Computer Science, vol 8805. Springer, Cham. https://doi.org/10.1007/978-3-319-14325-5_13
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DOI: https://doi.org/10.1007/978-3-319-14325-5_13
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