Assessing Operational Carbon Footprint for Task-based Applications using Simulation
Resumo
O consumo de energia em HPC gera emissões de carbono que variam com o local e o horário, mas aferi-las com execuções reais é custoso. Propomos um método baseado em simulação para prever a pegada de carbono operacional de aplicações baseadas em tarefas. Um modelo de energia por tarefa para CPU e GPU, construído com base em medições de hardware, é aplicado a rastros StarPU-SimGrid, e a energia prevista é convertida para a pegada de carbono utilizando dados regionais. Nós utilizamos a fatoração de Cholesky em quatro partições no Brasil e na França, com as simulações prevendo a energia simulada com erros máximos de 6% (CPU) a 28% (GPU), e uma mesma carga de trabalho emitindo até ×3,7 mais carbono em função da partição e região.
Referências
Agullo, E., Augonnet, C., Dongarra, J., Ltaief, H., Namyst, R., Thibault, S., and Tomov, S. (2010). Faster, Cheaper, Better – a Hybridization Methodology to Develop Linear Algebra Software for GPUs. In GPU Computing Gems, volume 2. Morgan Kaufmann.
Augonnet, C., Aumage, O., Furmento, N., Namyst, R., and Thibault, S. (2012). Starpu-mpi: Task programming over clusters of machines enhanced with accelerators. In European MPI Users’ Group Meeting, pages 298–299. Springer.
Bosilca, G., Bouteiller, A., Danalis, A., Faverge, M., Hérault, T., and Dongarra, J. J. (2013). Parsec: Exploiting heterogeneity to enhance scalability. Computing in Science & Engineering, 15(6):36–45.
Casanova, H., Giersch, A., Legrand, A., Quinson, M., and Suter, F. (2025). Lowering entry barriers to developing custom simulators of distributed applications and platforms with SimGrid. Parallel Computing, 123:103–125.
Dongarra, J. and Keyes, D. (2024). The co-evolution of computational physics and high-performance computing. Nature Reviews Physics, 6(10):621–627.
Duran, A., Ayguadé, E., Badia, R. M., Labarta, J., Martinell, L., Martorell, X., and Planas, J. (2011). Ompss: a proposal for programming heterogeneous multi-core architectures. Parallel processing letters, 21.02.
Electricity Maps (2026). Reduce carbon emissions with actionable electricity data.
Gnibga, W. E., Blavette, A., and Orgerie, A.-C. (2023). Latency, energy and carbon aware collaborative resource allocation with consolidation and qos degradation strategies in edge computing. In 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), pages 2630–2639. IEEE.
Heinrich, F. (2019). Modeling, prediction and optimization of energy consumption of MPI applications using SimGrid. PhD thesis, Université Grenoble Alpes.
Janssen, C. L., Adalsteinsson, H., Cranford, S., Kenny, J. P., Pinar, A., Evensky, D. A., and Mayo, J. (2010). A simulator for large-scale parallel computer architectures. International Journal of Distributed Systems and Technologies (IJDST), 1(2):57–73.
Kamatar, A., Gonthier, M., Hayot-Sasson, V., Bauer, A., Copik, M., Castro Fernandez, R., Hoefler, T., Chard, K., and Foster, I. (2025). Core hours and carbon credits: Incentivizing sustainability in hpc. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 870–887.
Li, B., Basu Roy, R., Wang, D., Samsi, S., Gadepally, V., and Tiwari, D. (2023). Toward sustainable hpc: Carbon footprint estimation and environmental implications of hpc systems. In Proceedings of the international conference for high performance computing, networking, storage and analysis, pages 1–15.
Mubarak, M., Carothers, C. D., Ross, R. B., and Carns, P. (2016). Enabling parallel simulation of large-scale hpc network systems. IEEE Transactions on Parallel and Distributed Systems, 28(1):87–100.
Nguyen, T., MacLean, C., Siracusa, M., Doerfler, D., Wright, N. J., and Williams, S. (2022). Fpga-based hpc accelerators: An evaluation on performance and energy efficiency. Concurrency and Computation: Practice and Experience, 34(20):e6570.
Patterson, D., Gonzalez, J., Hölzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D. R., Texier, M., and Dean, J. (2022). The carbon footprint of machine learning training will plateau, then shrink. Computer, 55(7):18–28.
Portegies Zwart, S. (2020). The ecological impact of high-performance computing in astrophysics. Nature Astronomy, 4(9):819–822.
Risse, J., Guermouche, A., and Trahay, F. (2025). Fine-grain energy consumption modeling of hpc task-based programs. In 2025 IEEE International Conference on Cluster Computing (CLUSTER), pages 1–12. IEEE.
Saraiva, G., Vasconcelos, M., Bruschi, S., Carastan-Santos, D., and Cordeiro, D. (2025). Estimating co2 emissions of distributed applications and platforms with simgrid_batsim. In Anais do XXVI Simpósio em Sistemas Computacionais de Alto Desempenho, pages 109–120, Porto Alegre, RS, Brasil. SBC.
Schulz, M., Ruefenacht, M., Kranzlmüller, D., and Schulz, L. B. (2023). Accelerating hpc with quantum computing: It is a software challenge too. Computing in Science & Engineering, 24(4):60–64.
Stanisic, L., Agullo, E., Buttari, A., Guermouche, A., Legrand, A., Lopez, F., and Videau, B. (2015). Fast and accurate simulation of multithreaded sparse linear algebra solvers. In 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS), pages 481–490. IEEE.
Stanisic, L., Thibault, S., Legrand, A., Videau, B., and Méhaut, J.-F. (2014). Modeling and simulation of a dynamic task-based runtime system for heterogeneous multi-core architectures. In European Conference on Parallel Processing, pages 50–62. Springer.
Strohmaier, E., Meuer, H. W., Dongarra, J., and Simon, H. D. Green500. [link]. accessed November 10,2025.
Strohmaier, E., Meuer, H. W., Dongarra, J., and Simon, H. D. Top500. [link]. accessed November 10,2025.
Suarez, E., Amaya, J., Frank, M., Freyermuth, O., Girone, M., Kostrzewa, B., and Pfalzner, S. (2025). Energy efficiency trends in hpc: what high-energy and astrophysicists need to know. Frontiers in Physics, 13:1542474.
Xia, Y., Jiang, P., Agrawal, G., and Ramnath, R. (2023). End-to-end lu factorization of large matrices on gpus. In Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP ’23, page 288–300, New York, NY, USA. Association for Computing Machinery.
Zheng, G., Kakulapati, G., and Kalé, L. V. (2004). Bigsim: A parallel simulator for performance prediction of extremely large parallel machines. In 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings., page 78. IEEE.
Augonnet, C., Aumage, O., Furmento, N., Namyst, R., and Thibault, S. (2012). Starpu-mpi: Task programming over clusters of machines enhanced with accelerators. In European MPI Users’ Group Meeting, pages 298–299. Springer.
Bosilca, G., Bouteiller, A., Danalis, A., Faverge, M., Hérault, T., and Dongarra, J. J. (2013). Parsec: Exploiting heterogeneity to enhance scalability. Computing in Science & Engineering, 15(6):36–45.
Casanova, H., Giersch, A., Legrand, A., Quinson, M., and Suter, F. (2025). Lowering entry barriers to developing custom simulators of distributed applications and platforms with SimGrid. Parallel Computing, 123:103–125.
Dongarra, J. and Keyes, D. (2024). The co-evolution of computational physics and high-performance computing. Nature Reviews Physics, 6(10):621–627.
Duran, A., Ayguadé, E., Badia, R. M., Labarta, J., Martinell, L., Martorell, X., and Planas, J. (2011). Ompss: a proposal for programming heterogeneous multi-core architectures. Parallel processing letters, 21.02.
Electricity Maps (2026). Reduce carbon emissions with actionable electricity data.
Gnibga, W. E., Blavette, A., and Orgerie, A.-C. (2023). Latency, energy and carbon aware collaborative resource allocation with consolidation and qos degradation strategies in edge computing. In 2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS), pages 2630–2639. IEEE.
Heinrich, F. (2019). Modeling, prediction and optimization of energy consumption of MPI applications using SimGrid. PhD thesis, Université Grenoble Alpes.
Janssen, C. L., Adalsteinsson, H., Cranford, S., Kenny, J. P., Pinar, A., Evensky, D. A., and Mayo, J. (2010). A simulator for large-scale parallel computer architectures. International Journal of Distributed Systems and Technologies (IJDST), 1(2):57–73.
Kamatar, A., Gonthier, M., Hayot-Sasson, V., Bauer, A., Copik, M., Castro Fernandez, R., Hoefler, T., Chard, K., and Foster, I. (2025). Core hours and carbon credits: Incentivizing sustainability in hpc. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 870–887.
Li, B., Basu Roy, R., Wang, D., Samsi, S., Gadepally, V., and Tiwari, D. (2023). Toward sustainable hpc: Carbon footprint estimation and environmental implications of hpc systems. In Proceedings of the international conference for high performance computing, networking, storage and analysis, pages 1–15.
Mubarak, M., Carothers, C. D., Ross, R. B., and Carns, P. (2016). Enabling parallel simulation of large-scale hpc network systems. IEEE Transactions on Parallel and Distributed Systems, 28(1):87–100.
Nguyen, T., MacLean, C., Siracusa, M., Doerfler, D., Wright, N. J., and Williams, S. (2022). Fpga-based hpc accelerators: An evaluation on performance and energy efficiency. Concurrency and Computation: Practice and Experience, 34(20):e6570.
Patterson, D., Gonzalez, J., Hölzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D. R., Texier, M., and Dean, J. (2022). The carbon footprint of machine learning training will plateau, then shrink. Computer, 55(7):18–28.
Portegies Zwart, S. (2020). The ecological impact of high-performance computing in astrophysics. Nature Astronomy, 4(9):819–822.
Risse, J., Guermouche, A., and Trahay, F. (2025). Fine-grain energy consumption modeling of hpc task-based programs. In 2025 IEEE International Conference on Cluster Computing (CLUSTER), pages 1–12. IEEE.
Saraiva, G., Vasconcelos, M., Bruschi, S., Carastan-Santos, D., and Cordeiro, D. (2025). Estimating co2 emissions of distributed applications and platforms with simgrid_batsim. In Anais do XXVI Simpósio em Sistemas Computacionais de Alto Desempenho, pages 109–120, Porto Alegre, RS, Brasil. SBC.
Schulz, M., Ruefenacht, M., Kranzlmüller, D., and Schulz, L. B. (2023). Accelerating hpc with quantum computing: It is a software challenge too. Computing in Science & Engineering, 24(4):60–64.
Stanisic, L., Agullo, E., Buttari, A., Guermouche, A., Legrand, A., Lopez, F., and Videau, B. (2015). Fast and accurate simulation of multithreaded sparse linear algebra solvers. In 2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS), pages 481–490. IEEE.
Stanisic, L., Thibault, S., Legrand, A., Videau, B., and Méhaut, J.-F. (2014). Modeling and simulation of a dynamic task-based runtime system for heterogeneous multi-core architectures. In European Conference on Parallel Processing, pages 50–62. Springer.
Strohmaier, E., Meuer, H. W., Dongarra, J., and Simon, H. D. Green500. [link]. accessed November 10,2025.
Strohmaier, E., Meuer, H. W., Dongarra, J., and Simon, H. D. Top500. [link]. accessed November 10,2025.
Suarez, E., Amaya, J., Frank, M., Freyermuth, O., Girone, M., Kostrzewa, B., and Pfalzner, S. (2025). Energy efficiency trends in hpc: what high-energy and astrophysicists need to know. Frontiers in Physics, 13:1542474.
Xia, Y., Jiang, P., Agrawal, G., and Ramnath, R. (2023). End-to-end lu factorization of large matrices on gpus. In Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP ’23, page 288–300, New York, NY, USA. Association for Computing Machinery.
Zheng, G., Kakulapati, G., and Kalé, L. V. (2004). Bigsim: A parallel simulator for performance prediction of extremely large parallel machines. In 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings., page 78. IEEE.
Publicado
03/11/2026
Como Citar
MARCONDES, Otho José Sirtoli; RADDATZ, Rayan; SCHNORR, Lucas Mello.
Assessing Operational Carbon Footprint for Task-based Applications using Simulation. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 27. , 2026, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 343-356.
DOI: https://doi.org/10.5753/sscad.2026.34952.
