Yanwei Wang
ABSTRACT
GPU-aided servers are widely used to accelerate the increasing computing workload on edge network. However, massive computing requests can cause delays in transmission and processing at edge servers, leading to long response time and high system energy consumption. Moreover, in distributed scenarios, GPUs from different vendors can hardly communicate directly through the network. As a rare open-source GPU architecture, Vortex has been emerged to enable an FPGA accelerator to serve as a PCIe-based soft GPU. On this basis, Vortex GPUs in our edge acceleration architecture further integrate the low-latency RDMA function. Compared with the original Vortex GPU-aided servers cluster, our preliminary study shows that our Vortex GPU cluster is fast, scalable, and energy-efficient.
- Ching-Hsiang Chu, Xiaoyi Lu, Ammar A. Awan, Hari Subramoni, Bracy Elton, and Dhabaleswar K. Panda. 2019. Exploiting Hardware Multicast and GPUDirect RDMA for Efficient Broadcast. IEEE Transactions on Parallel and Distributed Systems 30, 3 (2019), 575--588. https://doi.org/10.1109/TPDS.2018.2867222Google Scholar
Cross Ref
- Blaise Tine, Krishna Praveen Yalamarthy, Fares Elsabbagh, and Kim Hyesoon. 2021. Vortex: Extending the RISC-V ISA for GPGPU and 3D-Graphics. In MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture (Virtual Event, Greece) (MICRO '21). Association for Computing Machinery, New York, NY, USA, 754--766. https://doi.org/10.1145/3466752.3480128Google ScholarDigital Library
- Hong Zhang, Yupeng Tang, Anurag Khandelwal, and Ion Stoica. 2023. SHEPHERD: Serving DNNs in the Wild. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23). USENIX Association, Boston, MA, 787--808. https://www.usenix.org/conference/nsdi23/presentation/zhang-hongGoogle Scholar
Index Terms
- Poster: A Fast, Scalable, and Energy-efficient Edge Acceleration Architecture based on GPU Cluster
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