Evaluating On-Node GPU Interconnects for Deep Learning Workloads
- BATTELLE (PACIFIC NW LAB)
- Brookhaven National Laboratory
Scaling deep learning workloads across multiple GPUs on a single node has become increasingly important in data analytics. A key question is how well a PCIe-based GPU interconnect can perform relative to a custom high-performance interconnect such as NVIDIA's NVLink. This paper evaluates two such on-node interconnects for eight NVIDIA Pascal P100 GPUs: (a) the NVIDIA DGX-1's NVLink 1.0 `hybrid cube mesh'; and (b) the Cirrascale GX8's two-level PCIe tree using dual SR3615 switch risers. To show the effects of a range of neural network workloads, we define a parameterized version of the popular ResNet. We define a workload intensity metric that characterizes the expected computation/communication ratio; we also locate AlexNet and GoogLeNet within that space. As expected, the DGX-1 typically has superior performance. However, when equalizing GPU SM frequencies, the GX8 is very competitive on all ResNet workloads. With 8 GPUs, the GX8 can outperform the DGX-1 on all-to-all reductions by 10% for medium-sized payloads; and in rare cases, the GX8 slightly outperforms on ResNet.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1525777
- Report Number(s):
- PNNL-SA-129849
- Country of Publication:
- Switzerland
- Language:
- English
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