dpBento: Benchmarking DPUs for Data Processing
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912314346700800 |
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| author | Hu, Jiasheng Cui, Chihan Li, Anna Vora, Raahil Chen, Yuanfan Bernstein, Philip A. Li, Jialin Zhang, Qizhen |
| author_facet | Hu, Jiasheng Cui, Chihan Li, Anna Vora, Raahil Chen, Yuanfan Bernstein, Philip A. Li, Jialin Zhang, Qizhen |
| contents | Data processing units (DPUs, SoC-based SmartNICs) are emerging data center hardware that provide opportunities to address cloud data processing challenges. Their onboard compute, memory, network, and auxiliary storage can be leveraged to offload a variety of data processing tasks. Although recent work shows promising benefits of DPU offloading for specific operations, a comprehensive view of the implications of DPUs for data processing is missing. Benchmarking can help, but existing benchmark tools lack the focus on data processing and are limited to specific DPUs. In this paper, we present dpBento, a benchmark suite that aims to uncover the performance characteristics of different DPU resources and different DPUs, and the performance implications of offloading a wide range of data processing operations and systems to DPUs. It provides an abstraction for automated performance testing and reporting and is easily extensible. We use dpBento to measure recent DPUs, present our benchmarking results, and highlight insights into the potential benefits of DPU offloading for data processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05536 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | dpBento: Benchmarking DPUs for Data Processing Hu, Jiasheng Cui, Chihan Li, Anna Vora, Raahil Chen, Yuanfan Bernstein, Philip A. Li, Jialin Zhang, Qizhen Distributed, Parallel, and Cluster Computing Databases H.2.4; C.2.4 Data processing units (DPUs, SoC-based SmartNICs) are emerging data center hardware that provide opportunities to address cloud data processing challenges. Their onboard compute, memory, network, and auxiliary storage can be leveraged to offload a variety of data processing tasks. Although recent work shows promising benefits of DPU offloading for specific operations, a comprehensive view of the implications of DPUs for data processing is missing. Benchmarking can help, but existing benchmark tools lack the focus on data processing and are limited to specific DPUs. In this paper, we present dpBento, a benchmark suite that aims to uncover the performance characteristics of different DPU resources and different DPUs, and the performance implications of offloading a wide range of data processing operations and systems to DPUs. It provides an abstraction for automated performance testing and reporting and is easily extensible. We use dpBento to measure recent DPUs, present our benchmarking results, and highlight insights into the potential benefits of DPU offloading for data processing. |
| title | dpBento: Benchmarking DPUs for Data Processing |
| topic | Distributed, Parallel, and Cluster Computing Databases H.2.4; C.2.4 |
| url | https://arxiv.org/abs/2504.05536 |