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[ 中文 ]

Graphcore® C600

The Graphcore® C600 IPU-Processor PCIe Card is a high-performance acceleration server card targeted for machine learning inference and training. Powered by the Graphcore Mk2 IPU Processor with FP8 support, the C600 is a dual-slot, full height PCI Express Gen4 card designed for mounting in industry standard server chassis to accelerate machine intelligence workloads.

Up to eight C600 IPU-Processor PCIe Cards can be networked together using IPU-Link™ high-bandwidth interconnect cables, delivering enhanced IPU compute capability.

Product Specs

Name Description
IPU Processor Graphcore Mk2 IPU Processor with FP8 support
IPU-Cores™ 1,472 IPU-Cores, each one a high-performance processor capable of multi-thread, independent code execution
In-Processor Memory™ Each IPU-Core is paired with fast, local, tightly-coupled In-Processor Memory. The C600 accelerator includes 900MB of In-Processor Memory
Compute Up to 560 teraFLOPS of FP8 compute
Up to 280 teraFLOPS of FP16 compute
Up to 70 teraFLOPS of FP32 compute
System Interface Dual PCIe Gen4 8-lane interfaces
Thermal Solution Passive
Form Factor PCIe full-height/length; double-slot
System Dimensions Length: 267mm (10.50”); Height: 111mm (4.37”); Width: 27.6mm (1.09”); Mass: 1.27kg (2.8lbs)
IPU-Link™ Support 32 lanes, 128 GB/s bandwidth (64 GB/s in each direction) IPU-Links
TDP 185W
Auxiliary Power Supply 8-pin
Quality Level Server grade

For more information of the Graphcore® C600, please refer to C600 cards.

PopRT

PopRT is a high-performance inference framework specifically for Graphcore IPUs. It is responsible for deeply optimizing the trained models, generating executable programs that can run on the Graphcore IPUs, and performing low-latency, high-throughput inference.

You can get PopRT and related documents from graphcore/PopRT.

Docker images are provided at graphcorecn/poprt.

Models supported

Model name Precision QPS Dataset Metric name Metric value report
albert-torch-fp32 FP16 3,280 Open Squad 1.1 F1 Score 87.69675 report
bert-torch-fp32 FP8 4,464 Open Squad 1.1 F1 Score 85.71465 report
bert-torch-fp32 FP16 3,134 Open Squad 1.1 F1 Score 85.85797 report
clip-onnx-fp32 FP16 7,305 Fake Dataset Mean Diff 0.00426 report
conformer-encoder-onnx-fp32 FP16 9,341 Fake Dataset Mean Diff 0.00161 report
deberta-torch-fp32 FP16 1,702 Open Squad 1.1 F1 Score 81.24629 report
resnet50-torch-fp32 FP8 18,851 Open Imagenet Top-1 0.76824 report
resnet50-torch-fp32 FP16 13,499 Open Imagenet Top-1 0.76963 report
roberta-torch-fp32 FP16 3,088 Open Squad 1.1 F1 Score 83.1606 report
roformer-tf-fp32 FP16 2,520 OPEN_CAIL2019 Top-1 0.64323 report
swin-large-torch-fp32 FP8 480 Open Imagenet Top-1 0.8552 report
swin-large-torch-fp32 FP16 315 Open Imagenet Top-1 0.8536 report
videobert-onnx-fp32 FP16 3,691 OPEN_CIFAR Top-1 0.6169 report
widedeep-tf-fp32 FP16 31,446,195 Open Criteo Kaggle Top-1 0.77392 report

How to run

Download and enable Poplar SDK

wget -O 'poplar_sdk-ubuntu_20_04-3.3.0-208993bbb7.tar.gz' 'https://downloads.graphcore.ai/direct?package=poplar-poplar_sdk_ubuntu_20_04_3.3.0_208993bbb7-3.3.0&file=poplar_sdk-ubuntu_20_04-3.3.0-208993bbb7.tar.gz'

tar xzf poplar_sdk-ubuntu_20_04-3.3.0-208993bbb7.tar.gz

source poplar_sdk-ubuntu_20_04-3.3.0+1403-208993bbb7/enable

Start PopRT docker container

docker pull graphcorecn/poprt:1.4.0

gc-docker -- -it \
              -v `pwd -P`:/workspace \
              -w /workspace \
              --entrypoint /bin/bash \
              graphcorecn/poprt:1.4.0

Install dependencies in docker container

apt-get update && \
apt-get install wget libglib2.0-0 -y

Run byte-mlperf task

For example,

python3 launch.py --task widedeep-tf-fp32 --hardware IPU

For more information of the command to run the task, please refer to ByteMLPerf.