The SYCL Standard as a Tool for Improving Computer Science Teaching
DOI:
https://doi.org/10.24160/1993-6982-2026-3-180-190Keywords:
information technologies, parallel programming, CPU, GPU, SIMT paradigm, SYCL standardAbstract
Matters concerned with improving the teaching of information technology (IT), first of all, for training IT specialists, are considered. The article analyzes topical issues of improving the teaching of IT related to the rapid development of modern computer technology (CCT): an increase in the number of processor cores (CPU), radical expansion of graphic processor (GPU) application areas, and integration of GPU and CPU into single chips. The article gives a number of examples related to IT teaching at universities of the Russian Federation and, to a lesser extent, at schools, which demonstrate the current advances of the Russian Federation in this area. Some features of studying computer science at foreign schools are briefly considered. Based on the analysis performed, topical potential areas for improving the teaching of IT in the Russian Federation related to the incomplete use of modern CCT capabilities are proposed. The necessary improvements in teaching IT related to the basic paradigm of modern computer architectures – SIMT (single instruction, multiple threads), which is used for GPUs of all leading manufacturers, are described and substantiated. The expediency of teaching the students of IT directions the SIMT architecture and programming for various heterogeneous systems using the open SYCL standard is substantiated. The advantages of such an approach are formulated.
References
1. Chowdhary K.R. Software-hardware Evolution and Birth of Multicore Processors [Электрон. ресурс] https://arxiv.org/abs/2112.06436 (дата обращения 01.11.2025).
2. Dally W.J., Keckler S.W., Kirk D.B. Evolution of the Graphics Processing Unit (GPU) // IEEE Micro. 2021. V. 41(6). Pp. 42—51.
3. Hardware Evolution is Driving Software Innovation [Электрон. ресурс] https://community.sap.com/t5/additional-blogs-by-sap/hardware-evolution-is-driving-software-innovation/ba-p/129784280 (дата обращения 01.11.2025).
4. Кузьминский М.Б. Новое поколение GPGPU и сопутствующего оборудования: микроархитектура и производительность вычислительных систем от серверов до суперкомпьютеров // Программные системы: теория и приложения. 2024. Т. 15. № 2(61). С. 139—473.
5. Босова Л.Л. Современные тенденции развития школьной информатики в России и за рубежом // Информатика и образование. 2019. № 1(300). С. 22—32.
6. Raspberry Pi for Home [Офиц. сайт] https://www.raspberrypi.com/for-home/ (дата обращения 01.11.2025).
7. Альт Образование 10 [Электрон. ресурс] https://www.altlinux.org/Альт_Образование_10 (дата обращения 01.11.2025).
8. АО «Байкал Электроникс» [Офиц. сайт] https://www.baikalelectronics.ru/products/ (дата обращения 01.11.2025).
9. Шенк Т., Барбер Д., Тернер Э. Red Hat Linux для системных администраторов. Энциклопедия пользователя. Киев: Изд-во «ДиаСофт», 2001.
10. Босова Л.Л. и др. Информатика. 10—11 классы. М.: БИНОМ. Лаборатория знаний, 2020.
11. Босова Л.Л., Босова А.Ю. Информатика. 9 класс. М.: БИНОМ. Лаборатория знаний, 2017.
12. Абрамов Н.С., Абрамов С.М. Ноябрь 2022: состояние и перспективы развития суперкомпьютерной отрасли в мире и в России // Программные системы: теория и приложения. 2023. Т. 14. № 2(57). С. 49—93.
13. Duan Xiaohui e. a. Enabling Real World Scale Structural Superlubricity All-atom Simulation on the Next-generation Sunway Supercomputer // Proc. SC23: Intern. Conf. High Performance Computing, Networking, Storage and Analysis. Denver, 2023. Pp. 1—14.
14. DeepSeek-V2: a Strong, Economical, and Efficient Mixture-of-experts Language Model [Электрон. ресурс] https://arxiv.org/abs/2405.04434 (дата обращения 01.11.2025).
15. DeepSeek-V3 Techn. Rep. [Электрон. ресурс] https://arxiv.org/abs/2412.19437 (дата обращения 01.11.2025).
16. Кузьминский М.Б. Современные серверные ARM-процессоры для суперЭВM: A64FX и другие. Начальные данные тестов производительности // Программные системы: теория и приложения. 2022. Т. 13. № 1(52). C. 63—129.
17. Зотова С.В. Практико-ориентированное преподавание информатики и информационных технологий в вузе // Известия Тульского гос. ун-та. Серия «Педагогика». 2018. № 3. С. 43—47.
18. Кутлияров А.Н., Кутлияров Д.Н. Использование современных информационных технологий в вузах // Вестник Учебно-методического объединения по образованию в области природообустройства и водопользования. 2015. № 7. С. 60—62.
19. Косицкая Ф.Л. Основные тренды в современном российском высшем образовании (по материалам зимней школы преподавателей — 2020) // Научно-педагогическое обозрение. 2020. № 3(31). С. 101—109.
20. Очков В.Ф. и др. Информационные технологии в инженерных расчетах: SMath и Python. СПб.: Лань, 2023.
21. Ochkov V., Stevens A., Tikhonov A. STEM Problems with Mathcad and Python. Boca Raton: CRC Press/Chapman & Hall, 2022.
22. Российский Квантовый Центр [Офиц. сайт] https://www.rqc.ru/education (дата обращения 01.11.2025).
23. Панков К.Н., Миронов Ю.Б. Применение квантовых методов в задачах защиты информации. М.: Науч.-техн. изд-во «Горячая линия-Телеком», 2022.
24. Андрущенко А.С. и др. Прикладные квантовые технологии для защиты информации. М.: Науч.-техн. изд-во «Горячая линия-Телеком», 2024.
25. Matsuoka S. e. a. Myths and Legends in High-performance Computing // Intern. J. High Performance Computing Appl. 2023. V. 37(3—4). Pp. 245—259.
26. JTC1/SC22/WG5. The Home of Fortran Standards [Офиц. сайт] https://wg5-fortran.org/ (дата обращения 01.11.2025).
27. Fortran Compilers [Офиц. сайт] https://fortran-lang.org/compilers/ (дата обращения 01.11.2025).
28. ООО «Базальт СПО» [Офиц. сайт] https://www.basealt.ru/ (дата обращения 01.11.2025).
29. Astra Linux [Офиц. сайт] https://astralinux.ru/ (дата обращения 01.11.2025).
30. Ред ОС [Офиц. сайт] https://redos.red-soft.ru/ (дата обращения 01.11.2025).
31. Postgres Pro [Офиц. сайт] https://postgrespro.ru/ (дата обращения 01.11.2025).
32. Eclipse IDE [Офиц. сайт] https://eclipseide.org/ (дата обращения 01.11.2025).
33. Code: Blocks [Офиц. сайт] https://www.codeblocks.org/ (дата обращения 01.11.2025).
34. SYCL [Офиц. сайт] https://www.khronos.org/sycl/ (дата обращения 01.11.2025).
35. Cheng K.-T., Wang Y.-C. Using Mobile GPU for General-purpose Computing — a Case Study of Face Recognition on Smartphones // Proc. Intern. Symp. VLSI Design, Automation and Test. Hsinchu, 2011. Pp. 1—4.
36. AMD INSTINCT™ MI300A APU [Электрон. ресурс] https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/data-sheets/amd-instinct-mi300a-data-sheet.pdf (дата обращения 01.11.2025).
37. NVIDIA [Офиц. сайт] https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (дата обращения 01.11.2025).
38. Rahman T.N., Khan N., Zaman Z.I. Redefining Computing: Rise of ARM from Consumer to Cloud for Energy Efficiency // World J. Adv. Research Rev. 2024. V. 21(1). Pp. 817—835.
39. AMD EPYC™ 9965 [Электрон. ресурс] https://www.amd.com/en/products/processors/server/epyc/9005-series/amd-epyc-9965.html#product-specs (дата обращения 01.11.2025).
40. Intel Corp. [Офиц. сайт] https://ark.intel.com/content/www/us/en/ark/products/series/240357/intel-xeon-6.html (дата обращения 01.11.2025).
41. Intel Corp. [Офиц. сайт] https://www.intel.com/content/dam/www/central-libraries/us/en/documents/2024-05/xeon6-e-cores-network-and-edge-brief.pdf (дата обращения 01.11.2025).
42. Abram H., Papadopoulou N., Pericàs M. Exploring SYCL as a Portability Layer for High-performance Computing on CPUs // Proc. 40th Intern. Conf. ISC High Performance. Hamburg: Prometeus GmbH, 2025. Pp. 1—12.
43. Da Silva H.C., Pisani F., Borin E. A Comparative Study of SYCL, OpenCL, and OpenMP // Proc. Intern. Symp. Computer Architecture and High Performance Computing Workshops. Los Angeles, 2016. Pp. 61—66.
44. Breyer M., Van Craen A., Pflüger D. A Comparison of SYCL, OpenCL, CUDA, and OpenMP for Massively Parallel Support Vector Machine Classification on Multi-vendor Hardware // Proc. 10th Intern. Workshop on OpenCL. 2022. V. 2. Pp. 1—12.
45. OpenCL [Офиц. сайт] https://opencl.org/ (дата обращения 01.11.2025).
46. Антонюк В.А. OpenCL. Открытый язык для параллельных программ. M: МГУ им. М.В. Ломоносова, 2017.
47. Shilpage W.R., Wright S.A. An Investigation into the Performance and Portability of SYCL Compiler Implementations // Proc. Intern. Conf. High Performance Computing. Cham: Springer, 2023. Pp. 605—619.
48. NVIDIA CUDA [Электрон. ресурс] https://developer.nvidia.com/cuda?ref=dataphoenix.info (дата обращения 01.11.2025).
49. HIP [Электрон. ресурс] https://github.com/ROCm/HIP (дата обращения 01.11.2025).
50. Torres L.A., Barrios H.C.J., Denneulin Y. Evaluation of Computational and Energy Performance in Matrix Multiplication Algorithms on CPU and GPU Using MKL, cuBLAS and SYCL [Электрон. ресурс] https://arxiv.org/abs/2405.17322 (дата обращения 01.11.2025).
51. Reddy Kuncham G.K., Vaidya R., Barve M. Performance Study of GPU Applications Using SYCL and CUDA on Tesla V100 GPU // Proc. IEEE High Performance Extreme Computing Conf. Waltham, 2021. Pp. 1—7.
52. Breyer M., Van Craen A., Pflüger D. Evaluation of SYCL’s Different Data Parallel Kernels // Proc. 12th Intern. Workshop on OpenCL and SYCL. 2024. V. 10. Pp. 1—4.
53. Campos C., Asenjo R., Hormigo J., Navarro A. Leveraging SYCL for Heterogeneous cDTW Computation on CPU, GPU, and FPGA // Concurrency and Computation: Practice and Experience. 2025. V. 37(15—17). P. e70142.
54. Reguly I.Z. e. a. Under the Hood of SYCL — an Initial Performance Analysis with an Unstructured-mesh CFD Application // High Performance Computing. ISC High Performance. Cham.: Springer, 2021. Pp. 391—410.
55. Massive Image Dataset Blending Using SYCL with Intel® Max Series GPU [Электрон. ресурс] https://community.intel.com/t5/Blogs/Tech-Innovation/Tools/Massive-Image-Dataset-Blending-Using-SYCL-with-Intel-Max-Series/post/1538125 (дата обращения 01.11.2025).
56. Nguyen A. e. a. Efficient, Out-of-memory Sparse MTTKRP on Massively Parallel Architectures [Электрон. ресурс] https://arxiv.org/pdf/2201.12523 (дата обращения 01.11.2025).
57. Apanasevich L., Kale Y., Sharma H., Sokovic A.M. A Comparison of the Performance of the Molecular Dynamics Simulation Package GROMACS Implemented in the SYCL and CUDA Programming Models [Электрон. ресурс] https://arxiv.org/abs/2406.10362 (дата обращения 01.11.2025).
58. Alekseenko A., Páll S., Lindahl E. GROMACS on AMD GPU-based HPC Platforms: Using SYCL for Performance and Portability [Электрон. ресурс] https://arxiv.org/abs/2405.01420 (дата обращения 01.11.2025).
59. Skoblin V., Höfling F., Christgau S. Gaining Cross-platform Parallelism for HAL's Molecular Dynamics Package Using SYCL [Электрон. ресурс] https://arxiv.org/abs/2406.04210 (дата обращения 01.11.2025).
60. State-of-the-art and Trends for Computing and Interconnect Network Solutions for HPC and AI. [Электрон. ресурс] https://dps.uibk.ac.at/~philipp/publication/tekin-2021-state/tekin-2021-state.pdf (дата обращения 01.11.2025).
61. Bauinger C., Genovese L. Introducing SYCL to Accelerate a Fock Operator Calculation Library of the BigDFT Electronic Structure Code // High Performance Computing. ISC High Performance. Cham: Springer, 2024. Pp. 79—101.
62. Davis J.H. e. a. Taking GPU Programming Models to Task for Performance Portability [Электрон. ресурс] https://arxiv.org/pdf/2402.08950 (дата обращения 01.11.2025).
63. Crisci L. e. a. SYCL-bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs [Электрон. ресурс] https://www.cosenza.eu/papers/CrisciIWOCL24.pdf (дата обращения 01.11.2025).
64. Intel Corp. [Офиц. сайт] https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compiler.html (дата обращения 01.11.2025).
65. Intel Corp. [Офиц. сайт] [Электрон. ресурс] https://www.intel.com/content/www/us/en/developer/tools/oneapi/toolkits.html (дата обращения 01.11.2025).
66. OneAPI [Офиц. сайт] https://www.oneapi.io/ (дата обращения 01.11.2025).
67. Intel Corp. [Офиц. сайт] [Электрон. ресурс] https://www.intel.com/content/www/us/en/developer/articles/technical/sycl-2020-features-dpc-language-oneapi-c.html (дата обращения 01.11.2025).
68. UXL [Офиц. сайт] https://uxlfoundation.org/ (дата обращения 01.11.2025).
69. Reinders J. e. a. Data Parallel C++: Programming Accelerated Systems Using C++ and SYCL. Berkeley: Springer, 2023.
70. Burns R. e. a. Tutorial: Application Development with SYCL // Proc. 10th Intern. Workshop on OpenCL. 2022. Pp. 1—1.
71. SYCL Academy [Электрон. ресурс] https://github.com/codeplaysoftware/syclacademy (дата обращения 01.11.2025).
72. Teaching SYCL at Durham University, Department of Computer Science! [Электрон. ресурс] https://sycl.tech/news/2024/02/06/teaching-sycl-at-durham-university-department-of-computer-science (дата обращения 01.11.2025).
73. Explore SYCL with Samples from Intel v.2025.0 [Электрон. ресурс] https://www.intel.com/content/www/us/en/docs/oneapi/code-samples-dpcpp/2025-0/overview.html (дата обращения 01.11.2025).
74. Fuentes J., López D., González S. Teaching Heterogeneous Computing Using DPC++ // Proc. IEEE Intern. Parallel and Distributed Proc. Symp. Workshops. Lyon, 2022. Pp. 354—360.
75. Сысоев А.В. и др. Учебный курс «Программирование с использованием модели OneAPI» // Вестник Южно-Уральского гоc. ун-та. Серия «Вычислительная математика и информатика». 2022. Т. 11. № 3. С. 45—58.
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Для цитирования: Кузьминский М.Б., Чернецов А.М., Шамаева О.Ю. Стандарт SYCL как инструмент совершенствования преподавания информатики // Вестник МЭИ. 2026. № 3. С. 180—190. DOI: 10.24160/1993-6982-2026-3-180-190
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Конфликт интересов: авторы заявляют об отсутствии конфликта интересов
#
1. Chowdhary K.R. Software-hardware Evolution and Birth of Multicore Processors [Elektron. Resurs] https://arxiv.org/abs/2112.06436 (Data Obrashcheniya 01.11.2025).
2. Dally W.J., Keckler S.W., Kirk D.B. Evolution of the Graphics Processing Unit (GPU). IEEE Micro. 2021;41(6):42—51.
3. Hardware Evolution is Driving Software Innovation [Elektron. Resurs] https://community.sap.com/t5/additional-blogs-by-sap/hardware-evolution-is-driving-software-innovation/ba-p/129784280 (Data Obrashcheniya 01.11.2025).
4. Kuz'minskiy M.B. Novoe Pokolenie GPGPU i Soputstvuyushchego Oborudovaniya: Mikroarhitektura i Proizvoditel'nost' Vychislitel'nyh Sistem ot Serverov do Superkomp'yuterov. Programmnye Sistemy: Teoriya i Prilozheniya. 2024;15;2(61):139—473. (in Russian).
5. Bosova L.L. Sovremennye Tendentsii Razvitiya Shkol'noy Informatiki v Rossii i za Rubezhom. Informatika i Obrazovanie. 2019;1(300):22—32. (in Russian).
6. Raspberry Pi for Home [Ofits. Sayt] https://www.raspberrypi.com/for-home/ (Data Obrashcheniya 01.11.2025).
7. Al't Obrazovanie 10 [Elektron. Resurs] https://www.altlinux.org/Al't_Obrazovanie_10 (Data Obrashcheniya 01.11.2025). (in Russian).
8. AO «Baykal Elektroniks» [Ofits. Sayt] https://www.baikalelectronics.ru/products/ (Data Obrashcheniya 01.11.2025). (in Russian).
9. Shenk T., Barber D., Terner E. Red Hat Linux dlya Sistemnyh Administratorov. Entsiklopediya Pol'zovatelya. Kiev: Izd-vo «DiaSoft», 2001. (in Russian).
10. Bosova L.L. i dr. Informatika. 10—11 Klassy. M.: BINOM. Laboratoriya Znaniy, 2020. (in Russian).
11. Bosova L.L., Bosova A.Yu. Informatika. 9 Klass. M.: BINOM. Laboratoriya Znaniy, 2017. (in Russian).
12. Abramov N.S., Abramov S.M. Noyabr' 2022: Sostoyanie i Perspektivy Razvitiya Superkomp'yuternoy Otrasli v Mire i v Rossii. Programmnye Sistemy: Teoriya i Prilozheniya. 2023;14;2(57):49—93. (in Russian).
13. Duan Xiaohui e. a. Enabling Real World Scale Structural Superlubricity All-atom Simulation on the Next-generation Sunway Supercomputer. Proc. SC23: Intern. Conf. High Performance Computing, Networking, Storage and Analysis. Denver, 2023:1—14.
14. DeepSeek-V2: a Strong, Economical, and Efficient Mixture-of-experts Language Model [Elektron. Resurs] https://arxiv.org/abs/2405.04434 (Data Obrashcheniya 01.11.2025).
15. DeepSeek-V3 Techn. Rep. [Elektron. Resurs] https://arxiv.org/abs/2412.19437 (Data Obrashcheniya 01.11.2025).
16. Kuz'minskiy M.B. Sovremennye Servernye ARM-protsessory dlya SuperEVM: A64FX i Drugie. Nachal'nye Dannye Testov Proizvoditel'nosti. Programmnye Sistemy: Teoriya i Prilozheniya. 2022;13;1(52):63—129. (in Russian).
17. Zotova S.V. Praktiko-orientirovannoe Prepodavanie Informatiki i Informatsionnyh Tekhnologiy v Vuze. Izvestiya Tul'skogo Gos. Un-ta. Seriya «Pedagogika». 2018;3:43—47. (in Russian).
18. Kutliyarov A.N., Kutliyarov D.N. Ispol'zovanie Sovremennyh Informatsionnyh Tekhnologiy v Vuzah. Vestnik Uchebno-metodicheskogo Ob'edineniya po Obrazovaniyu v Oblasti Prirodoobustroystva i Vodopol'zovaniya. 2015;7:60—62. (in Russian).
19. Kositskaya F.L. Osnovnye Trendy v Sovremennom Rossiyskom Vysshem Obrazovanii (po Materialam Zimney Shkoly Prepodavateley — 2020). Nauchno-pedagogicheskoe Obozrenie. 2020;3(31):101—109. (in Russian).
20. Ochkov V.F. i dr. Informatsionnye Tekhnologii v Inzhenernyh Raschetah: SMath i Python. SPb.: Lan', 2023. (in Russian).
21. Ochkov V., Stevens A., Tikhonov A. STEM Problems with Mathcad and Python. Boca Raton: CRC Press/Chapman & Hall, 2022.
22. Rossiyskiy Kvantovyy Tsentr [Ofits. Sayt] https://www.rqc.ru/education (Data Obrashcheniya 01.11.2025). (in Russian).
23. Pankov K.N., Mironov Yu.B. Primenenie Kvantovyh Metodov v Zadachah Zashchity Informatsii. M.: Nauch.-tekhn. Izd-vo «Goryachaya Liniya-Telekom», 2022. (in Russian).
24. Andrushchenko A.S. i dr. Prikladnye Kvantovye Tekhnologii dlya Zashchity Informatsii. M.: Nauch.-tekhn. Izd-vo «Goryachaya Liniya-Telekom», 2024. (in Russian).
25. Matsuoka S. e. a. Myths and Legends in High-performance Computing. Intern. J. High Performance Computing Appl. 2023;37(3—4):245—259.
26. JTC1/SC22/WG5. The Home of Fortran Standards [Ofits. Sayt] https://wg5-fortran.org/ (Data Obrashcheniya 01.11.2025).
27. Fortran Compilers [Ofits. Sayt] https://fortran-lang.org/compilers/ (Data Obrashcheniya 01.11.2025).
28. OOO «Bazal't SPO» [Ofits. Sayt] https://www.basealt.ru/ (Data Obrashcheniya 01.11.2025). (in Russian).
29. Astra Linux [Ofits. Sayt] https://astralinux.ru/ (Data Obrashcheniya 01.11.2025).
30. Red OS [Ofits. Sayt] https://redos.red-soft.ru/ (Data Obrashcheniya 01.11.2025). (in Russian).
31. Postgres Pro [Ofits. Sayt] https://postgrespro.ru/ (Data Obrashcheniya 01.11.2025).
32. Eclipse IDE [Ofits. Sayt] https://eclipseide.org/ (Data Obrashcheniya 01.11.2025).
33. Code: Blocks [Ofits. Sayt] https://www.codeblocks.org/ (Data Obrashcheniya 01.11.2025).
34. SYCL [Ofits. Sayt] https://www.khronos.org/sycl/ (Data Obrashcheniya 01.11.2025).
35. Cheng K.-T., Wang Y.-C. Using Mobile GPU for General-purpose Computing — a Case Study of Face Recognition on Smartphones. Proc. Intern. Symp. VLSI Design, Automation and Test. Hsinchu, 2011:1—4.
36. AMD INSTINCT™ MI300A APU [Elektron. Resurs] https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/data-sheets/amd-instinct-mi300a-data-sheet.pdf (Data Obrashcheniya 01.11.2025).
37. NVIDIA [Ofits. Sayt] https://resources.nvidia.com/en-us-grace-cpu/nvidia-grace-hopper (Data Obrashcheniya 01.11.2025).
38. Rahman T.N., Khan N., Zaman Z.I. Redefining Computing: Rise of ARM from Consumer to Cloud for Energy Efficiency. World J. Adv. Research Rev. 2024;21(1):817—835.
39. AMD EPYC™ 9965 [Elektron. Resurs] https://www.amd.com/en/products/processors/server/epyc/9005-series/amd-epyc-9965.html#product-specs (Data Obrashcheniya 01.11.2025).
40. Intel Corp. [Ofits. Sayt] https://ark.intel.com/content/www/us/en/ark/products/series/240357/intel-xeon-6.html (Data Obrashcheniya 01.11.2025).
41. Intel Corp. [Офиц. сайт] [Elektron. Resurs] https://www.intel.com/content/dam/www/central-libraries/us/en/documents/2024-05/xeon6-e-cores-network-and-edge-brief.pdf (Data Obrashcheniya 01.11.2025).
42. Abram H., Papadopoulou N., Pericàs M. Exploring SYCL as a Portability Layer for High-performance Computing on CPUs. Proc. 40th Intern. Conf. ISC High Performance. Hamburg: Prometeus GmbH, 2025:1—12.
43. Da Silva H.C., Pisani F., Borin E. A Comparative Study of SYCL, OpenCL, and OpenMP. Proc. Intern. Symp. Computer Architecture and High Performance Computing Workshops. Los Angeles, 2016:61—66.
44. Breyer M., Van Craen A., Pflüger D. A Comparison of SYCL, OpenCL, CUDA, and OpenMP for Massively Parallel Support Vector Machine Classification on Multi-vendor Hardware. Proc. 10th Intern. Workshop on OpenCL. 2022;2:1—12.
45. OpenCL [Ofits. Sayt] https://opencl.org/ (Data Obrashcheniya 01.11.2025).
46. Antonyuk V.A. OpenCL. Otkrytyy Yazyk dlya Parallel'nyh Programm. M: MGU im. M.V. Lomonosova, 2017. (in Russian).
47. Shilpage W.R., Wright S.A. An Investigation into the Performance and Portability of SYCL Compiler Implementations. Proc. Intern. Conf. High Performance Computing. Cham: Springer, 2023:605—619.
48. NVIDIA CUDA [Elektron. Resurs] https://developer.nvidia.com/cuda?ref=dataphoenix.info (Data Obrashcheniya 01.11.2025).
49. HIP [Elektron. Resurs] https://github.com/ROCm/HIP (Data Obrashcheniya 01.11.2025).
50. Torres L.A., Barrios H.C.J., Denneulin Y. Evaluation of Computational and Energy Performance in Matrix Multiplication Algorithms on CPU and GPU Using MKL, cuBLAS and SYCL [Elektron. Resurs] https://arxiv.org/abs/2405.17322 (Data Obrashcheniya 01.11.2025).
51. Reddy Kuncham G.K., Vaidya R., Barve M. Performance Study of GPU Applications Using SYCL and CUDA on Tesla V100 GPU. Proc. IEEE High Performance Extreme Computing Conf. Waltham, 2021:1—7.
52. Breyer M., Van Craen A., Pflüger D. Evaluation of SYCL’s Different Data Parallel Kernels. Proc. 12th Intern. Workshop on OpenCL and SYCL. 2024;10:1—4.
53. Campos C., Asenjo R., Hormigo J., Navarro A. Leveraging SYCL for Heterogeneous cDTW Computation on CPU, GPU, and FPGA. Concurrency and Computation: Practice and Experience. 2025;37(15—17):e70142.
54. Reguly I.Z. e. a. Under the Hood of SYCL — an Initial Performance Analysis with an Unstructured-mesh CFD Application. High Performance Computing. ISC High Performance. Cham.: Springer, 2021:391—410.
55. Massive Image Dataset Blending Using SYCL with Intel® Max Series GPU [Elektron. Resurs] https://community.intel.com/t5/Blogs/Tech-Innovation/Tools/Massive-Image-Dataset-Blending-Using-SYCL-with-Intel-Max-Series/post/1538125 (Data Obrashcheniya 01.11.2025).
56. Nguyen A. e. a. Efficient, Out-of-memory Sparse MTTKRP on Massively Parallel Architectures [Elektron. Resurs] https://arxiv.org/pdf/2201.12523 (Data Obrashcheniya 01.11.2025).
57. Apanasevich L., Kale Y., Sharma H., Sokovic A.M. A Comparison of the Performance of the Molecular Dynamics Simulation Package GROMACS Implemented in the SYCL and CUDA Programming Models [Elektron. Resurs] https://arxiv.org/abs/2406.10362 (Data Obrashcheniya 01.11.2025).
58. Alekseenko A., Páll S., Lindahl E. GROMACS on AMD GPU-based HPC Platforms: Using SYCL for Performance and Portability [Elektron. Resurs] https://arxiv.org/abs/2405.01420 (Data Obrashcheniya 01.11.2025).
59. Skoblin V., Höfling F., Christgau S. Gaining Cross-platform Parallelism for HAL's Molecular Dynamics Package Using SYCL [Elektron. Resurs] https://arxiv.org/abs/2406.04210 (Data Obrashcheniya 01.11.2025).
60. State-of-the-art and Trends for Computing and Interconnect Network Solutions for HPC and AI. [Elektron. Resurs] https://dps.uibk.ac.at/~philipp/publication/tekin-2021-state/tekin-2021-state.pdf (Data Obrashcheniya 01.11.2025).
61. Bauinger C., Genovese L. Introducing SYCL to Accelerate a Fock Operator Calculation Library of the BigDFT Electronic Structure Code. High Performance Computing. ISC High Performance. Cham: Springer, 2024:79—101.
62. Davis J.H. e. a. Taking GPU Programming Models to Task for Performance Portability [Elektron. Resurs] https://arxiv.org/pdf/2402.08950 (Data Obrashcheniya 01.11.2025).
63. Crisci L. e. a. SYCL-bench 2020: Benchmarking SYCL 2020 on AMD, Intel, and NVIDIA GPUs [Elektron. Resurs] https://www.cosenza.eu/papers/CrisciIWOCL24.pdf (Data Obrashcheniya 01.11.2025).
64. Intel Corp. [Ofits. Sayt] https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compiler.html (Data Obrashcheniya 01.11.2025).
65. Intel Corp. [Офиц. сайт] [Elektron. Resurs] https://www.intel.com/content/www/us/en/developer/tools/oneapi/toolkits.html (Data Obrashcheniya 01.11.2025).
66. OneAPI [Ofits. Sayt] https://www.oneapi.io/ (Data Obrashcheniya 01.11.2025).
67. Intel Corp. [Офиц. сайт] [Elektron. Resurs] https://www.intel.com/content/www/us/en/developer/articles/technical/sycl-2020-features-dpc-language-oneapi-c.html (Data Obrashcheniya 01.11.2025).
68. UXL [Ofits. Sayt] https://uxlfoundation.org/ (Data Obrashcheniya 01.11.2025).
69. Reinders J. e. a. Data Parallel C++: Programming Accelerated Systems Using C++ and SYCL. Berkeley: Springer, 2023.
70. Burns R. e. a. Tutorial: Application Development with SYCL. Proc. 10th Intern. Workshop on OpenCL. 2022:1—1.
71. SYCL Academy [Elektron. Resurs] https://github.com/codeplaysoftware/syclacademy (Data Obrashcheniya 01.11.2025).
72. Teaching SYCL at Durham University, Department of Computer Science! [Elektron. Resurs] https://sycl.tech/news/2024/02/06/teaching-sycl-at-durham-university-department-of-computer-science (Data Obrashcheniya 01.11.2025).
73. Explore SYCL with Samples from Intel v.2025.0 [Elektron. Resurs] https://www.intel.com/content/www/us/en/docs/oneapi/code-samples-dpcpp/2025-0/overview.html (Data Obrashcheniya 01.11.2025).
74. Fuentes J., López D., González S. Teaching Heterogeneous Computing Using DPC++. Proc. IEEE Intern. Parallel and Distributed Proc. Symp. Workshops. Lyon, 2022:354—360.
75. Sysoev A.V. i dr. Uchebnyy Kurs «Programmirovanie s Ispol'zovaniem Modeli OneAPI». Vestnik Yuzhno-Ural'skogo Goc. Un-ta. Seriya «Vychislitel'naya Matematika i Informatika». 2022;11(3):45—58. (in Russian)
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For citation: Kuzminsky M.B., Chernetsov A.M., Shamayeva O.Yu. The SYCL Standard as a Tool for Improving Computer Science Teaching. Bulletin of MPEI. 2026;3:180—190. (in Russian). DOI: 10.24160/1993-6982-2026-3-180-190
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Conflict of interests: the authors declare no conflict of interest

