Open Access
Issue
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
Article Number 01001
Number of page(s) 8
DOI https://doi.org/10.1051/epjconf/202533701001
Published online 07 October 2025
  1. CERN, Facts and figures about the lhc, https://home.web.cern.ch/resources/faqs/facts-and-figures-about-lhc, access: 2025-02-23 [Google Scholar]
  2. CERN, High-luminosity lhc, https://home.cern/resources/faqs/high-luminosity-lhc, access: 2025-02-23 [Google Scholar]
  3. Patched “perf” repository on cern gitlab, https://gitlab.cern.ch/adaptyst/linux, access: 2025-02-27 [Google Scholar]
  4. perf: Linux profiling with performance counters, https://perfwiki.github.io/main, access: 2025-02-23 [Google Scholar]
  5. R. Brun, F. Rademakers, Root—an object oriented data analysis framework, Nuclear instruments and methods in physics research section A: accelerators, spectrometers, detectors and associated equipment 389, 81 (1997). [Google Scholar]
  6. B. Gregg, Flame graphs, https://brendangregg.com/flamegraphs.html, access: 2025-0223 [Google Scholar]
  7. J. Morgado, L. Sousa, A. Ilic, CARM Tool: Cache-Aware Roofline Model Automatic Benchmarking and Application Analysis, in 2024 IEEE International Symposium on Workload Characterization (IISWC) (IEEE, 2024), pp. 68–81 [Google Scholar]
  8. C. Lattner, V. Adve, LLVM: A compilation framework for lifelong program analysis & transformation, in International symposium on code generation and optimization, 2004. CGO 2004. (IEEE, 2004), pp. 75–86 [Google Scholar]
  9. J. Auerbach, D.F. Bacon, I. Burcea, P. Cheng, S.J. Fink, R. Rabbah, S. Shukla, A compiler and runtime for heterogeneous computing, in Proceedings of the 49th Annual Design Automation Conference (2012), pp. 271–276 [Google Scholar]
  10. C.J. Rossbach, Y. Yu, J. Currey, J.P. Martin, D. Fetterly, Dandelion: a compiler and runtime for heterogeneous systems, in Proceedings of the Twenty-Fourth ACM Symposium on Operating Systems Principles (2013), pp. 49–68 [Google Scholar]
  11. P. Banerjee, N. Shenoy, A. Choudhary, S. Hauck, C. Bachmann, M. Haldar, P. Joisha, A. Jones, A. Kanhare, A. Nayak et al., A MATLAB compiler for distributed, heterogeneous, reconfigurable computing systems, in Proceedings 2000 IEEE Symposium on Field-Programmable Custom Computing Machines (Cat. No. PR00871) (IEEE, 2000), pp. 39–48 [Google Scholar]
  12. T. Grosser, T. Hoefler, Polly-ACC transparent compilation to heterogeneous hardware, in Proceedings of the 2016 International Conference on Supercomputing (2016), pp. 1–13 [Google Scholar]
  13. F. Franchetti, T.M. Low, D.T. Popovici, R.M. Veras, D.G. Spampinato, J.R. Johnson, M. Püschel, J.C. Hoe, J.M. Moura, Spiral: Extreme performance portability, Proceedings of the IEEE 106, 1935 (2018). [Google Scholar]
  14. H. Riebler, G. Vaz, T. Kenter, C. Plessl, Transparent acceleration for heterogeneous platforms with compilation to opencl, ACM Transactions on Architecture and Code Optimization (TACO) 16, 1 (2019). [Google Scholar]
  15. B.M. Gruber, G. Amadio, J. Blomer, A. Matthes, R. Widera, M. Bussmann, Llama: The low-level abstraction for memory access, Software: Practice and Experience 53, 115 (2023). [Google Scholar]
  16. Khronos Group, Sycl overview, https://www.khronos.org/sycl, access: 2025-02-23 [Google Scholar]
  17. C.R. Trott, D. Lebrun-Grandié, D. Arndt, J. Ciesko, V. Dang, N. Ellingwood, R. Gayatri, E. Harvey, D.S. Hollman, D. Ibanez et al., Kokkos 3: Programming model extensions for the exascale era, IEEE Transactions on Parallel and Distributed Systems 33, 805 (2021). [Google Scholar]
  18. E. Zenker, B. Worpitz, R. Widera, A. Huebl, G. Juckeland, A. Knüpfer, W.E. Nagel, M. Bussmann, Alpaka–an abstraction library for parallel kernel acceleration, in 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (IEEE, 2016), pp. 631–640 [Google Scholar]
  19. Openacc, https://www.openacc.org, access: 2025-02-23 [Google Scholar]
  20. AMD, Vitis hls, https://www.amd.com/en/products/software/adaptive-socs-and-fpgas/vitis/vitis-hls.html, access: 2025-02-23 [Google Scholar]
  21. Siemens, Catapult high-level synthesis & verification, https://eda.sw.siemens.com/en-US/ic/catapult-high-level-synthesis, access: 2025-02-23 [Google Scholar]
  22. L. Josipovic, A. Guerrieri, P. Ienne, Synthesizing general-purpose code into dynamically scheduled circuits, IEEE Circuits and Systems Magazine 21, 97 (2021). [Google Scholar]
  23. J. Duarte, S. Han, P. Harris, S. Jindariani, E. Kreinar, B. Kreis, J. Ngadiuba, M. Pierini, R. Rivera, N. Tran et al., Fast inference of deep neural networks in fpgas for particle physics, Journal of instrumentation 13, P07027 (2018). [Google Scholar]
  24. C. Lattner, M. Amini, U. Bondhugula, A. Cohen, A. Davis, J. Pienaar, R. Riddle, T. Shpeisman, N. Vasilache, O. Zinenko, MLIR: Scaling compiler infrastructure for domain specific computation, in 2021 IEEE/ACM International Symposium on Code Generation and Optimization (CGO) (IEEE, 2021), pp. 2–14 [Google Scholar]
  25. T. Ben-Nun, J. de Fine Licht, A.N. Ziogas, T. Schneider, T. Hoefler, Stateful dataflow multigraphs: A data-centric model for performance portability on heterogeneous architectures, in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (2019), pp. 1–14 [Google Scholar]
  26. Intel, Fix performance bottlenecks with intel vtune profiler, https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html, access: 2025-02-23 [Google Scholar]
  27. AMD, Amd µprof, https://www.amd.com/en/developer/uprof.html, access: 2025-02-23 [Google Scholar]
  28. Valgrind home, https://valgrind.org, access: 2025-02-23 [Google Scholar]
  29. Gnu gprof, https://ftp.gnu.org/old-gnu/Manuals/gprof-2.9.1/html_mono/gprof.html, access: 2025-02-23 [Google Scholar]
  30. gperftools repository, https://github.com/gperftools/gperftools, access: 2025-02-23 [Google Scholar]
  31. NVIDIA, Nvidia nsight developer tools - nvidia docs, https://docs.nvidia.com/nsight-developer-tools/index.html, access: 2025-02-23 [Google Scholar]
  32. S.S. Shende, A.D. Malony, The tau parallel performance system, The International Journal of High Performance Computing Applications 20, 287 (2006). [Google Scholar]
  33. L. Adhianto, S. Banerjee, M. Fagan, M. Krentel, G. Marin, J. Mellor-Crummey, N.R. Tallent, Hpctoolkit: Tools for performance analysis of optimized parallel programs, Concurrency and Computation: Practice and Experience 22, 685 (2010). [Google Scholar]

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