A multi-aspect online tuning framework for HPC applications

• Bajrovic, E., Mijakovic, R., Dokulil, J., Benkner, S., & Gerndt, M. (2016). Tuning OpenCL applications with the periscope tuning framework, Hawaii international conference on system sciences. IEEE.

  Google Scholar 

• Balaprakash, P., Tiwari, A., & Wild, S.M. (2013). Multi-objective optimization of hpc kernels for performance, power, and energy, 4th international workshop on performance modeling, benchmarking, and simulation of HPC systems (PMBS12), 11/2013.

  Google Scholar 

• Benedict, S., Petkov, V., & Gerndt, M. (2010). Periscope: An online-based distributed performance analysis tool. In Müller, M.S., Resch, M.M., Schulz, A., & Nagel, W.E. (Eds.), Tools for high performance computing 2009 (pp. 1–16). Berlin Heidelberg: Springer.

  Google Scholar 

• Bruel, P., Gonzalez, M., & Goldman, A. (2015). Autotuning gpu compiler parameters using opentuner. XXII Symposium of Systems of High Performance Computing.

• Buck, B., & Hollingsworth, J.K. (2000). An api for runtime code patching. International Journal of High Performance Computing Applications, 14(4), 317–329.

  Article  Google Scholar 

• CESAR. Proxy-apps. https://cesar.mcs.anl.gov/content/software.

• Chen, C., Chame, J., & Hall, M. (2008). Chill: A framework for composing high-level loop transformations. Technical report University of Southern California.

• Chung, I-H., & Hollingsworth, J.K. (2004). Using information from prior runs to improve automated tuning systems, Proceedings of the 2004 ACM/IEEE conference on supercomputing, SC ’04 (p. 30). Washington: IEEE Computer Society.

  Google Scholar 

• CORAL. benchmarks. https://asc.llnl.gov/coral-benchmarks.

• Costa, G., Jorba, J., Morajko, A., Margalef, T., & Luque, E. (2008). Performance models for dynamic tuning of parallel applications on computational grids, 2008 IEEE international conference on cluster computing (pp. 376–385).

  Chapter  Google Scholar 

• Costa, G., Sikora, A., Jorba, J., & Gmate, T.M. (2014). Dynamic tuning of parallel applications in grid environment. Journal of Grid Computing, 12(2), 371–398.

  Article  Google Scholar 

• Datta, K., Murphy, M., Volkov, V., Williams, S., Carter, J., Oliker, L., Patterson, D., Shalf, J., & Yelick, K. (2008). Stencil computation optimization and auto-tuning on state-of-the-art multicore architectures. In Proceedings of the 2008 ACM/IEEE conference on supercomputing, SC ’08 (pp. 4:1–4:12). Piscataway: IEEE Press.

• Demmel, J., Dongarra, J., Eijkhout, V., Fuentes, E., Petitet, A., Vuduc, R., Whaley, R. C., & Yelick, K. (2005). Self-adapting linear algebra algorithms and software. Proceedings of the IEEE, 93(2), 293–312.

  Article  Google Scholar 

• Frigo, M., & Johnson, S. G. (1998). Fftw: an adaptive software architecture for the fft. In Proceedings of the 1998 IEEE international conference on acoustics, speech and signal processing, 1998 (Vol. 3, pp. 1381–1384).

• Frigo, M., & Johnson, S. G. (2005). The design and implementation of fftw3. Proceedings of the IEEE, 93(2), 216–231.

  Article  Google Scholar 

• Fursin, G., Kashnikov, Y., Memon, A.W., Chamski, Z., Temam, O., Namolaru, M., Yom-Tov, E., Mendelson, B., Zaks, A., Courtois, E., Bodin, F., Barnard, P., Ashton, E., Bonilla, E., Thomson, J., Williams, C.K.I., & O’Boyle, M. (2011). Milepost gcc Machine learning enabled self-tuning compiler. International Journal of Parallel Programming, 39(3), 296–327.

  Article  Google Scholar 

• Gerndt, M., César, E., & Benkner, S. (eds.) (2015). Automatic tuning of HPC applications - the periscope tuning framework. Shaker Verlag.

• Haneda, M., Knijnenburg, P. M. W., & Wijshoff, H.A.G. (2005). Automatic selection of compiler options using non-parametric inferential statistics, 14th International conference on parallel architectures and compilation techniques, 2005. PACT 2005 (pp. 123–132).

  Google Scholar 

• Kukkonen, S., & Lampinen, J. (2005). Gde3: The third evolution step of generalized differential evolution. In The 2005 IEEE congress on evolutionary computation, 2005 (Vol. 1, pp. 443–450). IEEE.

• Leather, H., Bonilla, E., & O’Boyle, M. (2009). Automatic feature generation for machine learning based optimizing compilation, Proceedings of the 7th Annual IEEE/ACM international symposium on code generation and optimization, CGO ’09 (pp. 81–91). Washington: IEEE Computer Society.

  Chapter  Google Scholar 

• Morajko, A., Caymes-Scutari, P., Margalef, T., & Mate, E. Luque. (2007). Monitoring, analysis and tuning environment for parallel/distributed applications. Concurrency and Computation: Practice and Experience, 19(11), 1517–1531.

  Article  MATH  Google Scholar 

• Morajko, A., César, E., Caymes-Scutari, P., Margalef, T., Sorribes, J., & Luque, E. (2005). Automatic tuning of Master/Worker applications. In Proceedings of Euro-Par 2005 parallel processing: 11th international euro-par conference (pp. 95–103).

• Navarette, C., Guillen, C., Hesse, W., & Brehm, M. (2014). Autotuning the energy consumption. In Bader, M. et al. (Eds.) Parallel computing accelerating computational science and engineering. IOS Press.

• Nelson, Y. L., Bansal, B., Hall, M., Nakano, A., & Lerman, K. (2008). Model-guided performance tuning of parameter values A case study with molecular dynamics visualization, IEEE international symposium on parallel and distributed processing, 2008. IPDPS 2008 (pp. 1–8).

  Google Scholar 

• Oleynik, Y., Gerndt, M., Schuchart, J., Kjeldsberg, P.G., & Nagel, W.E. (2015). Run-time exploitation of application dynamism for energy-efficient exascale computing (READEX). In IEEE 18th international conference on computational science and engineering (CSE), 2015 (pp. 347–350). IEEE.

• OpenCL SDK User-guide. https://software.intel.com/sites/default/files/m/e/7/0/3/1/33857-Intel_28R_29_OpenCL_SDK_User_Guide.eps https://software.intel.com/sites/default/files/m/e/7/0/3/1/33857-Intel_28R_29_OpenCL_SDK_User_Guide.eps.

• Pan, Z., & Eigenmann, R. (2006). Fast and effective orchestration of compiler optimizations for automatic performance tuning, Proceedings of the international symposium on code generation and optimization, CGO ’06 (pp. 319–332). Washington: IEEE Computer Society.

  Google Scholar 

• Püschel, M., Moura, J.M. F., Singer, B., Xiong, J., Johnson, J., Padua, D., Veloso, M., & Johnson, R.W. (2004). Spiral: a generator for platform-adapted libraries of signal processing algorithms. International Journal of High Performance Computing Applications, 18(1), 21–45.

  Article  Google Scholar 

• Ravipati, G., Bernat, A.R., Miller, B.P., & Hollingsworth, J.K. (2007). Towards the deconstruction of dyninst. Technical report. University of Wisconsin.

  Google Scholar 

• Ribler, R.L., Simitci, H., & Reed, D.A. (2001). The autopilot performance-directed adaptive control system. Future Generation Computer Systems, 18(1), 175–187.

  Article  MATH  Google Scholar 

• Ribler, R. L., Vetter, J. S., Simitci, H., & Reed, D. A. (1998). Autopilot: adaptive control of distributed applications, Proceedings of the 7th international symposium on high performance distributed computing, 1998 (pp. 172–179).

  Google Scholar 

• Tang, Y., Chowdhury, R.A., Kuszmaul, B.C., Luk, C.-K., & Leiserson, C.E. (2011). The pochoir stencil compiler, Proceedings of the 23rd annual ACM symposium on parallelism in algorithms and architectures, SPAA ’11 (pp. 117–128). New York: ACM.

  Google Scholar 

• Tiwari, A., Chen, C., Chame, J., Hall, M., & Hollingsworth, J.K. (2009). A scalable auto-tuning framework for compiler optimization, IEEE International symposium on parallel distributed processing, 2009. IPDPS 2009 (pp. 1–12).

  Google Scholar 

• Tiwari, A., & Hollingsworth, J. K. (2011). Online adaptive code generation and tuning. In 2011 IEEE international parallel distributed processing symposium (IPDPS) (pp. 879–892).

• Ţăpuş, C., Chung, I-H., & Hollingsworth, J.K. (2002). Active harmony: Towards automated performance tuning, Proceedings of the 2002 ACM/IEEE conference on supercomputing, SC ’02 (pp. 1–11). Los Alamitos: IEEE Computer Society Press.

  Google Scholar 

• The LLVM Compiler Infrastructure. http://llvm.org/.

• Triantafyllis, S., Vachharajani, M., Vachharajani, N., & August, D.I. (2003). Compiler optimization-space exploration, Proceedings of the international symposium on code generation and optimization: feedback-directed and runtime optimization, CGO ’03 (pp. 204–215). Washington: IEEE Computer Society.

  Google Scholar 

• Vuduc, R., Demmel, J.W., & Yelick, K.A. (2005). Oski: A library of automatically tuned sparse matrix kernels. Journal of Physics: Conference Series, 16(1), 521.

  Google Scholar 

• Whaley, R.C., Petitet, A., & Dongarra, J.J. (2001). Automated empirical optimizations of software and the ATLAS project. Parallel Computing, 27(12), 3–35. New Trends in High Performance Computing.

  Article  MATH  Google Scholar 

• Williams, S., Waterman, A., & Patterson, D. (2009). Roofline: An insightful visual performance model for multicore architectures. Communications of the ACM, 52 (4), 65–76.

  Article  Google Scholar 

• X-TUNE. Autotuning for exascale: self-tuning software to manage heterogeneity. http://ctop.cs.utah.edu/x-tune/.

• Xiujuan, L., & Zhongke, S. (2004). Overview of multi-objective optimization methods. Journal of Systems Engineering and Electronics, 15(2), 142–146.

  Google Scholar 

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