Abstract
Accelerator architectures specialize in executing SIMD (single instruction, multiple data) in lockstep. Because the majority of CUDA applications are parallelized loops, control flow information can provide an in-depth characterization of a kernel. CUDAflow is a tool that statically separates CUDA binaries into basic block regions and dynamically measures instruction and basic block frequencies. CUDAflow captures this information in a control flow graph (CFG) and performs subgraph matching across various kernel’s CFGs to gain insights into an application’s resource requirements, based on the shape and traversal of the graph, instruction operations executed and registers allocated, among other information. The utility of CUDAflow is demonstrated with SHOC and Rodinia application case studies on a variety of GPU architectures, revealing novel control flow characteristics that facilitate end users, autotuners, and compilers in generating high performing code.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Adhianto, L., et al.: HPCToolkit: tools for performance analysis of optimized parallel programs. Concurr. Comput. Pract. Exp. 22(6), 685–701 (2010)
Ammons, G., Ball, T., Larus, J.R.: Exploiting hardware performance counters with flow and context sensitive profiling. ACM Sigplan Not. 32(5), 85–96 (1997)
Ball, T., Larus, J.R.: Optimally profiling and tracing programs. ACM Trans. Program. Lang. Syst. (TOPLAS) 16(4), 1319–1360 (1994)
Böhm, C., Jacopini, G.: Flow diagrams, turing machines and languages with only two formation rules. Commun. ACM 9(5), 366–371 (1966)
Borgelt, C., Berthold, M.R.: Mining molecular fragments: finding relevant substructures of molecules. In: Proceedings of the IEEE International Conference on Data Mining, pp. 51–58. IEEE (2002)
Che, S., et al.: Rodinia: a benchmark suite for heterogeneous computing. In: IEEE International Symposium on Workload Characterization, IISWC 2009, pp. 44–54. IEEE (2009)
Collective Knowledge (CK). http://cknowledge.org
Csardi, G., Nepusz, T.: The iGraph software package for complex network research
Danalis, A., et al.: The scalable heterogeneous computing (SHOC) benchmark suite. In: Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, pp. 63–74. ACM (2010)
Allinea DDT. http://www.allinea.com/products/ddt
Diamos, G., Ashbaugh, B., Maiyuran, S., Kerr, A., Wu, H., Yalamanchili, S.: SIMD re-convergence at thread frontiers. In: Proceedings of the 44th Annual IEEE/ACM International Symposium on Microarchitecture, pp. 477–488. ACM (2011)
Farooqui, N., Kerr, A., Eisenhauer, G., Schwan, K., Yalamanchili, S.: Lynx: a dynamic instrumentation system for data-parallel applications on GPGPU architectures. In: International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 58–67. IEEE (2012)
Gonzales, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1993)
Huan, J., Wang, W., Prins, J.: Efficient mining of frequent subgraphs in the presence of isomorphism. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 549–552. IEEE (2003)
Koutra, D., Vogelstein, J.T., Faloutsos, C.: DeltaCon: a principled massive-graph similarity function. SIAM
Lim, R., Carrillo-Cisneros, D., Alkowaileet, W., Scherson, I.: Computationally efficient multiplexing of events on hardware counters. In: Linux Symposium (2014)
Lim, R., Malony, A., Norris, B., Chaimov, N.: Identifying optimization opportunities within kernel execution in GPU codes. In: Hunold, S., et al. (eds.) Euro-Par 2015. LNCS, vol. 9523, pp. 185–196. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27308-2_16
Lim, R., Norris, B., Malony, A.: Autotuning GPU kernels via static and predictive analysis. In: 2017 46th International Conference on Parallel Processing (ICPP), pp. 523–532. IEEE (2017)
Marin, G., Dongarra, J., Terpstra, D.: MIAMI: A framework for application performance diagnosis. In: 2014 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pp. 158–168. IEEE (2014)
Miller, B.P., et al.: The paradyn parallel performance measurement tool. Computer 28(11), 37–46 (1995)
Nvidia Visual Profiler. https://developer.nvidia.com/nvidia-visual-profiler
Sabne, A., Sakdhnagool, P., Eigenmann, R.: Formalizing structured control flow graphs. In: Ding, C., Criswell, J., Wu, P. (eds.) LCPC 2016. LNCS, vol. 10136, pp. 153–168. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52709-3_13
Sarkar, V.: Determining average program execution times and their variance. In: ACM SIGPLAN Notices, vol. 24, pp. 298–312. ACM (1989)
Shende, S.S., Malony, A.D.: The TAU parallel performance system. Int. J. High Perform. Comput. Appl. 20(2), 287–311 (2006)
Singh, R., Xu, J., Berger, B.: Pairwise global alignment of protein interaction networks by matching neighborhood topology. In: Speed, T., Huang, H. (eds.) RECOMB 2007. LNCS, vol. 4453, pp. 16–31. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71681-5_2
Sreepathi, S., et al.: Application characterization using Oxbow toolkit and PADS infrastructure. In: Proceedings of the 1st International Workshop on Hardware-Software Co-Design for High Performance Computing, pp. 55–63. IEEE Press (2014)
Williams, M.H., Ossher, H.: Conversion of unstructured flow diagrams to structured form. Comput. J. 21(2), 161–167 (1978)
Wu, H., Diamos, G., Li, S., Yalamanchili, S.: Characterization and transformation of unstructured control flow in GPU applications. In: 1st International Workshop on Characterizing Applications for Heterogeneous Exascale Systems (2011)
Yan, X., Han, J.: gSpan: graph-based substructure pattern mining. In: Proceedings of 2002 IEEE International Conference on Data Mining, ICDM 2003, pp. 721–724. IEEE (2002)
Zhang, F., D’Hollander, E.H.: Using hammock graphs to structure programs. IEEE Trans. Softw. Eng. 30(4), 231–245 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lim, R., Norris, B., Malony, A. (2019). A Similarity Measure for GPU Kernel Subgraph Matching. In: Hall, M., Sundar, H. (eds) Languages and Compilers for Parallel Computing. LCPC 2018. Lecture Notes in Computer Science(), vol 11882. Springer, Cham. https://doi.org/10.1007/978-3-030-34627-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-34627-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34626-3
Online ISBN: 978-3-030-34627-0
eBook Packages: Computer ScienceComputer Science (R0)