Architecture design, key technologies, and application research of GPU heterogeneous resource pool platform based on virtualization
The current challenges facing the field of artificial intelligence include high prices and market supply disruptions. The traditional single-card, single-use model results in low resource utilization and efficiency. Furthermore, existing technological research methods make it difficult to support th...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2024-09-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024216/ |
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| Summary: | The current challenges facing the field of artificial intelligence include high prices and market supply disruptions. The traditional single-card, single-use model results in low resource utilization and efficiency. Furthermore, existing technological research methods make it difficult to support the efficient management and scheduling of diverse heterogeneous GPU resources. Based on this, a virtualization-based GPU heterogeneous resource pool platform was proposed. Firstly, the overall architecture, logical architecture, and functional architecture of the platform were planned and designed. Secondly, key technologies were studied, and a virtualization heterogeneous GPU resource pool framework and a scheduling model based on time slicing + load balancing were proposed. Finally, based on the methods described, various innovative application models were proposed, including multiservice single-card stacking, cross-pull, cross-machine integration, hybrid deployment, and time division multiplexing. The research method proposed provides enterprise-level AI applications with GPU computing resources that are compatible with multiple GPU manufacturers, support remote access, flexible partitioning and aggregation, and flexible scheduling. Following the completion of calculations and an in-depth analysis, it has been demonstrated that a reduction of up to 60% in the number of GPU cards can be achieved while simultaneously enhancing operational efficiency by a factor of four. |
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| ISSN: | 1000-0801 |