Anonymized Network Sensing using C++26 std::execution on GPUs
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914096100671488 |
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| author | Mandulak, Michael Ghosh, Sayan Ferdous, S M Halappanavar, Mahantesh Slota, George |
| author_facet | Mandulak, Michael Ghosh, Sayan Ferdous, S M Halappanavar, Mahantesh Slota, George |
| contents | Large-scale network sensing plays a vital role in network traffic analysis and characterization. As network packet data grows increasingly large, parallel methods have become mainstream for network analytics. While effective, GPU-based implementations still face start-up challenges in host-device memory management and porting complex workloads on devices, among others. To mitigate these challenges, composable frameworks have emerged using modern C++ programming language, for efficiently deploying analytics tasks on GPUs. Specifically, the recent C++26 Senders model of asynchronous data operation chaining provides a simple interface for bulk pushing tasks to varied device execution contexts.
Considering the prominence of contemporary dense-GPU platforms and vendor-leveraged software libraries, such a programming model consider GPUs as first-class execution resources (compared to traditional host-centric programming models), allowing convenient development of multi-GPU application workloads via expressive and standardized asynchronous semantics. In this paper, we discuss practical aspects of developing the Anonymized Network Sensing Graph Challenge on dense-GPU systems using the recently proposed C++26 Senders model. Adopting a generic and productive programming model does not necessarily impact the critical-path performance (as compared to low-level proprietary vendor-based programming models): our commodity library-based implementation achieves up to 55x performance improvements on 8x NVIDIA A100 GPUs as compared to the reference serial GraphBLAS baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14050 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anonymized Network Sensing using C++26 std::execution on GPUs Mandulak, Michael Ghosh, Sayan Ferdous, S M Halappanavar, Mahantesh Slota, George Distributed, Parallel, and Cluster Computing Large-scale network sensing plays a vital role in network traffic analysis and characterization. As network packet data grows increasingly large, parallel methods have become mainstream for network analytics. While effective, GPU-based implementations still face start-up challenges in host-device memory management and porting complex workloads on devices, among others. To mitigate these challenges, composable frameworks have emerged using modern C++ programming language, for efficiently deploying analytics tasks on GPUs. Specifically, the recent C++26 Senders model of asynchronous data operation chaining provides a simple interface for bulk pushing tasks to varied device execution contexts. Considering the prominence of contemporary dense-GPU platforms and vendor-leveraged software libraries, such a programming model consider GPUs as first-class execution resources (compared to traditional host-centric programming models), allowing convenient development of multi-GPU application workloads via expressive and standardized asynchronous semantics. In this paper, we discuss practical aspects of developing the Anonymized Network Sensing Graph Challenge on dense-GPU systems using the recently proposed C++26 Senders model. Adopting a generic and productive programming model does not necessarily impact the critical-path performance (as compared to low-level proprietary vendor-based programming models): our commodity library-based implementation achieves up to 55x performance improvements on 8x NVIDIA A100 GPUs as compared to the reference serial GraphBLAS baseline. |
| title | Anonymized Network Sensing using C++26 std::execution on GPUs |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2510.14050 |