Low-Intrusion Profiling and Scalability Analysis in Distributed-Memory Environments using PaScal Suite
Resumo
The continuous expansion of computational power in High-Performance Computing (HPC) systems has not been accompanied by a proportional increase in the scalability of general parallel applications. Traditional profiling tools provide detailed diagnostics for individual executions but often generate large trace volumes and require substantial manual effort when scalability must be evaluated across multiple configurations. To address this gap, this work extends the Parallel Scalability Suite to natively support distributed-memory applications. The proposed architecture adopts the Sidecar pattern to decouple metric management from the main execution flow, using Unix Domain Sockets for low-latency local communication and deferring transmission to a central Orchestrator until application completion. The solution was evaluated using dense matrix multiplication and the miniFE finite element miniapplication under multiple scalability configurations. Results show low measurement intrusion while preserving the scalability trends observed through conventional analysis.
Referências
Adhianto, L., Anderson, J., Barnett, R. M., Grbic, D., Indic, V., Krentel, M., Liu, Y., Milakovic, S., Phan, W., and Mellor-Crummey, J. (2024). Refining hpctoolkit for application performance analysis at exascale. INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS, 38(6, SI):612–632.
Adhianto, L., Banerjee, S., Fagan, M., Krentel, M., Marin, G., Mellor-Crummey, J., and Tallent, N. R. (2010). Hpctoolkit: tools for performance analysis of optimized parallel programs. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 22(6):685–701.
Bergel, A., Bhatele, A., Boehme, D., Gralka, P., Griffin, K., Hermanns, M.-A., Okanović, D., Pearce, O., and Vierjahn, T. (2019). Visual analytics challenges in analyzing calling context trees. In Bhatele, A., Boehme, D., Levine, J. A., Malony, A. D., and Schulz, M., editors, Programming and Performance Visualization Tools, pages 233–249, Cham. Springer International Publishing.
Boehme, D., Gamblin, T., Beckingsale, D., Bremer, P.-T., Gimenez, A., LeGendre, M., Pearce, O., and Schulz, M. (2016). Caliper: Performance introspection for hpc software stacks. In SC ‘16: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, pages 550–560. IEEE Comp Soc; ACM; SIGPHC. International Conference on High Performance Computing, Networking, Storage and Analysis (SC), Salt Lake City, UT, NOV 13-18, 2016.
Burns, B. and Oppenheimer, D. (2016). Design patterns for container-based distributed systems. In 8th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 16), Denver, CO. USENIX Association.
Calotoiu, A., Hoefler, T., Poke, M., and Wolf, F. (2013). Using automated performance modeling to find scalability bugs in complex codes. In 2013 INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS (SC), International Conference for High Performance Computing Networking Storage and Analysis. Assoc Comp Machinery; IEEE Comp Soc. International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Denver, CO, NOV 17-22, 2013.
da Silva, A. B. N., Cunha, D. A. M., Silva, V. R. G., Furtunato, A. F. d. A., and Xavier-de Souza, S. (2019). Pascal viewer: A tool for the visualization of parallel scalability trends. In Bhatele, A., Boehme, D., Levine, J., Malony, A., and Schulz, M., editors, PROGRAMMING AND PERFORMANCE VISUALIZATION TOOLS, volume 11027 of Lecture Notes in Computer Science, pages 250–264. 5th International Workshop on Visual Performance Analysis (VPA), Dallas, TX, NOV 11, 2018.
da Silva, V. R. G., da Silva, A. B. N., Valderrama, C., Manneback, P., and Xavier-de Souza, S. (2022). A minimally intrusive approach for automatic assessment of parallel performance scalability of shared-memory hpc applications. ELECTRONICS, 11(5).
Fan, K., Kesavan, S., Petruzza, S., and Kumar, S. (2024). Tinyprof: Towards continuous performance introspection through scalable parallel i/o. In ISC High Performance 2024 Research Paper Proceedings (39th International Conference). Cited by: 2.
Heroux, M. A., Doerfler, D. W., Crozier, P. S., Willenbring, J. M., Edwards, H. C., Williams, A., Rajan, M., Keiter, E. R., Thornquist, H. K., and Numrich, R. W. (2009). Improving Performance via Mini-applications. Technical Report SAND2009-5574, Sandia National Laboratories.
Huck, K. A., Shende, S., Malony, A. D., Coti, C., Spear, W., Alcaraz, J., Yokelson, D., Ramesh, S., Haque, M. M. A., Wood, C., Chaimov, N., Durbin, C., Johnson, A., Lambert, J., and Beekman, I. (2025). Preparing the tau performance system for exascale and beyond. The International Journal of High Performance Computing Applications, 39(4):532–552.
Huf, A. and Siqueira, F. A. (2019). Composition of heterogeneous web services: A systematic review. Journal of Network and Computer Applications. PII: S108480451930205X; SGR: 85067853499; Cited by: 22.
Islam, T. Z., Ayala, A., Jensen, Q., and Ibrahim, K. Z. (2019). Toward a programmable analysis and visualization framework for interactive performance analytics. 2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), pages 70–77.
Jin, Y., Wang, H., Tang, X., Guo, Z., Zhao, Y., Hoefler, T., Liu, T., Liu, X., and Zhai, J. (2025). Leveraging graph analysis to pinpoint root causes of scalability issues for parallel applications. IEEE Transactions on Parallel and Distributed Systems, 36(2):308–325.
Jost, G., Labarta, J., and Gimenez, J. (2004). Paramedir: A tool for programmable performance analysis. In Bubak, M., van Albada, G. D., Sloot, P. M. A., and Dongarra, J., editors, Computational Science - ICCS 2004, pages 466–469, Berlin, Heidelberg. Springer Berlin Heidelberg.
Knüpfer, A., Rössel, C., Mey, D. a., Biersdorff, S., Diethelm, K., Eschweiler, D., Geimer, M., Gerndt, M., Lorenz, D., Malony, A., Nagel, W. E., Oleynik, Y., Philippen, P., Saviankou, P., Schmidl, D., Shende, S., Tschüter, R., Wagner, M., Wesarg, B., and Wolf, F. (2012). Score-p: A joint performance measurement run-time infrastructure for periscope,scalasca, tau, and vampir. In Brunst, H., Müller, M. S., Nagel, W. E., and Resch, M. M., editors, Tools for High Performance Computing 2011, pages 79–91, Berlin, Heidelberg. Springer Berlin Heidelberg.
Muelder, C., Gygi, F., and Ma, K.-L. (2009). Visual analysis of inter-process communication for large-scale parallel computing. IEEE Transactions on Visualization and Computer Graphics, 15(6):1129–1136.
Nagel, W., Arnold, A., Weber, M., Hoppe, H.-C., and Solchenbach, K. (1996). Vampir: Visualization and analysis of mpi resources. Supercomputer, 12.
Pacheco, P. (2021). An Introduction to Parallel Programming. Morgan Kaufmann, 2nd edition.
Shende, S. S. and Malony, A. D. (2006). The tau parallel performance system. INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS, 20(2):287–311.
Silva, A. B. N., Silva, V. R. G., Valderrama, C., and Xavier-de Souza, S. (2025). Pascal suite: A toolset for evaluating and profiling parallel scalability in shared-memory programs. IEEE ACCESS, 13:204708–204725.
Thärigen, I., Protze, J., Orland, F., and Hermanns, M.-A. (2021). Differential performance analysis workflow for algorithmic changes. In 2021 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), pages 7–16.
Touati, S.-A.-A., Worms, J., and Briais, S. (2013). The speedup-test: a statistical methodology for programme speedup analysis and computation. Concurrency and Computation: Practice and Experience, 25(10):1410–1426.
Weber, M., Ziegenbalg, J., and Wesarg, B. (2019). Online performance analysis with the vampir tool set. In Niethammer, C., Resch, M. M., Nagel, W. E., Brunst, H., and Mix, H., editors, Tools for High Performance Computing 2017, pages 129–143, Cham. Springer International Publishing.
Xuan, Z., You, X., Feng, T., Liu, Y., and Qian, D. (2025). Simtrace: Exploiting Spatial and Temporal Sampling for Large-Scale Performance Analysis. ACM Transactions on Architecture and Code Optimization. SGR: 105010723962.
Zhukov, I. and Wylie, B. J. N. (2014). Assessing measurement and analysis performance and scalability of scalasca 2.0. In an Mey, D., Alexander, M., Bientinesi, P., Cannataro, M., Clauss, C., Costan, A., Kecskemeti, G., Morin, C., Ricci, L., Sahuquillo, J., Schulz, M., Scarano, V., Scott, S. L., and Weidendorfer, J., editors, Euro-Par 2013: Parallel Processing Workshops, pages 627–636, Berlin, Heidelberg. Springer Berlin Heidelberg.
Adhianto, L., Banerjee, S., Fagan, M., Krentel, M., Marin, G., Mellor-Crummey, J., and Tallent, N. R. (2010). Hpctoolkit: tools for performance analysis of optimized parallel programs. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 22(6):685–701.
Bergel, A., Bhatele, A., Boehme, D., Gralka, P., Griffin, K., Hermanns, M.-A., Okanović, D., Pearce, O., and Vierjahn, T. (2019). Visual analytics challenges in analyzing calling context trees. In Bhatele, A., Boehme, D., Levine, J. A., Malony, A. D., and Schulz, M., editors, Programming and Performance Visualization Tools, pages 233–249, Cham. Springer International Publishing.
Boehme, D., Gamblin, T., Beckingsale, D., Bremer, P.-T., Gimenez, A., LeGendre, M., Pearce, O., and Schulz, M. (2016). Caliper: Performance introspection for hpc software stacks. In SC ‘16: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS, pages 550–560. IEEE Comp Soc; ACM; SIGPHC. International Conference on High Performance Computing, Networking, Storage and Analysis (SC), Salt Lake City, UT, NOV 13-18, 2016.
Burns, B. and Oppenheimer, D. (2016). Design patterns for container-based distributed systems. In 8th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 16), Denver, CO. USENIX Association.
Calotoiu, A., Hoefler, T., Poke, M., and Wolf, F. (2013). Using automated performance modeling to find scalability bugs in complex codes. In 2013 INTERNATIONAL CONFERENCE FOR HIGH PERFORMANCE COMPUTING, NETWORKING, STORAGE AND ANALYSIS (SC), International Conference for High Performance Computing Networking Storage and Analysis. Assoc Comp Machinery; IEEE Comp Soc. International Conference for High Performance Computing, Networking, Storage and Analysis (SC), Denver, CO, NOV 17-22, 2013.
da Silva, A. B. N., Cunha, D. A. M., Silva, V. R. G., Furtunato, A. F. d. A., and Xavier-de Souza, S. (2019). Pascal viewer: A tool for the visualization of parallel scalability trends. In Bhatele, A., Boehme, D., Levine, J., Malony, A., and Schulz, M., editors, PROGRAMMING AND PERFORMANCE VISUALIZATION TOOLS, volume 11027 of Lecture Notes in Computer Science, pages 250–264. 5th International Workshop on Visual Performance Analysis (VPA), Dallas, TX, NOV 11, 2018.
da Silva, V. R. G., da Silva, A. B. N., Valderrama, C., Manneback, P., and Xavier-de Souza, S. (2022). A minimally intrusive approach for automatic assessment of parallel performance scalability of shared-memory hpc applications. ELECTRONICS, 11(5).
Fan, K., Kesavan, S., Petruzza, S., and Kumar, S. (2024). Tinyprof: Towards continuous performance introspection through scalable parallel i/o. In ISC High Performance 2024 Research Paper Proceedings (39th International Conference). Cited by: 2.
Heroux, M. A., Doerfler, D. W., Crozier, P. S., Willenbring, J. M., Edwards, H. C., Williams, A., Rajan, M., Keiter, E. R., Thornquist, H. K., and Numrich, R. W. (2009). Improving Performance via Mini-applications. Technical Report SAND2009-5574, Sandia National Laboratories.
Huck, K. A., Shende, S., Malony, A. D., Coti, C., Spear, W., Alcaraz, J., Yokelson, D., Ramesh, S., Haque, M. M. A., Wood, C., Chaimov, N., Durbin, C., Johnson, A., Lambert, J., and Beekman, I. (2025). Preparing the tau performance system for exascale and beyond. The International Journal of High Performance Computing Applications, 39(4):532–552.
Huf, A. and Siqueira, F. A. (2019). Composition of heterogeneous web services: A systematic review. Journal of Network and Computer Applications. PII: S108480451930205X; SGR: 85067853499; Cited by: 22.
Islam, T. Z., Ayala, A., Jensen, Q., and Ibrahim, K. Z. (2019). Toward a programmable analysis and visualization framework for interactive performance analytics. 2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), pages 70–77.
Jin, Y., Wang, H., Tang, X., Guo, Z., Zhao, Y., Hoefler, T., Liu, T., Liu, X., and Zhai, J. (2025). Leveraging graph analysis to pinpoint root causes of scalability issues for parallel applications. IEEE Transactions on Parallel and Distributed Systems, 36(2):308–325.
Jost, G., Labarta, J., and Gimenez, J. (2004). Paramedir: A tool for programmable performance analysis. In Bubak, M., van Albada, G. D., Sloot, P. M. A., and Dongarra, J., editors, Computational Science - ICCS 2004, pages 466–469, Berlin, Heidelberg. Springer Berlin Heidelberg.
Knüpfer, A., Rössel, C., Mey, D. a., Biersdorff, S., Diethelm, K., Eschweiler, D., Geimer, M., Gerndt, M., Lorenz, D., Malony, A., Nagel, W. E., Oleynik, Y., Philippen, P., Saviankou, P., Schmidl, D., Shende, S., Tschüter, R., Wagner, M., Wesarg, B., and Wolf, F. (2012). Score-p: A joint performance measurement run-time infrastructure for periscope,scalasca, tau, and vampir. In Brunst, H., Müller, M. S., Nagel, W. E., and Resch, M. M., editors, Tools for High Performance Computing 2011, pages 79–91, Berlin, Heidelberg. Springer Berlin Heidelberg.
Muelder, C., Gygi, F., and Ma, K.-L. (2009). Visual analysis of inter-process communication for large-scale parallel computing. IEEE Transactions on Visualization and Computer Graphics, 15(6):1129–1136.
Nagel, W., Arnold, A., Weber, M., Hoppe, H.-C., and Solchenbach, K. (1996). Vampir: Visualization and analysis of mpi resources. Supercomputer, 12.
Pacheco, P. (2021). An Introduction to Parallel Programming. Morgan Kaufmann, 2nd edition.
Shende, S. S. and Malony, A. D. (2006). The tau parallel performance system. INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS, 20(2):287–311.
Silva, A. B. N., Silva, V. R. G., Valderrama, C., and Xavier-de Souza, S. (2025). Pascal suite: A toolset for evaluating and profiling parallel scalability in shared-memory programs. IEEE ACCESS, 13:204708–204725.
Thärigen, I., Protze, J., Orland, F., and Hermanns, M.-A. (2021). Differential performance analysis workflow for algorithmic changes. In 2021 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), pages 7–16.
Touati, S.-A.-A., Worms, J., and Briais, S. (2013). The speedup-test: a statistical methodology for programme speedup analysis and computation. Concurrency and Computation: Practice and Experience, 25(10):1410–1426.
Weber, M., Ziegenbalg, J., and Wesarg, B. (2019). Online performance analysis with the vampir tool set. In Niethammer, C., Resch, M. M., Nagel, W. E., Brunst, H., and Mix, H., editors, Tools for High Performance Computing 2017, pages 129–143, Cham. Springer International Publishing.
Xuan, Z., You, X., Feng, T., Liu, Y., and Qian, D. (2025). Simtrace: Exploiting Spatial and Temporal Sampling for Large-Scale Performance Analysis. ACM Transactions on Architecture and Code Optimization. SGR: 105010723962.
Zhukov, I. and Wylie, B. J. N. (2014). Assessing measurement and analysis performance and scalability of scalasca 2.0. In an Mey, D., Alexander, M., Bientinesi, P., Cannataro, M., Clauss, C., Costan, A., Kecskemeti, G., Morin, C., Ricci, L., Sahuquillo, J., Schulz, M., Scarano, V., Scott, S. L., and Weidendorfer, J., editors, Euro-Par 2013: Parallel Processing Workshops, pages 627–636, Berlin, Heidelberg. Springer Berlin Heidelberg.
Publicado
03/11/2026
Como Citar
SANTOS-DA-SILVA, Felipe H.; ASSIS, Ítalo A. S.; FERNANDES, João B.; CORREIA, Igor S. F.; XAVIER-DE-SOUZA, Samuel.
Low-Intrusion Profiling and Scalability Analysis in Distributed-Memory Environments using PaScal Suite. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 27. , 2026, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 580-594.
DOI: https://doi.org/10.5753/sscad.2026.34987.
