ATC/OSDI 2023 BoF Roller
Reproducing Roller paper from OSDI'22. https://www.usenix.org/conference/osdi22/presentation/zhu
Hongyu Zhu, Ruofan Wu, Yijia Diao, Shanbin Ke, Haoyu Li, Chen Zhang, Jilong Xue, Lingxiao Ma, Yuqing Xia, Wei Cui, Fan Yang, Mao Yang, Lidong Zhou, Asaf Cidon and Gennady Pekhimenko (2022). ROLLER: Fast and Efficient Tensor Compilation for Deep Learning. In 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22) (pp. 233-248).
It's used as an example in ATC/OSDI 2023 BoF.
The artifact reproduces Figure 16 of the paper.
Reproducibility status:
Reproduced for demoing purpose during ATC/OSDI 2023 BOF. YouTube video available: https://youtu.be/5hZDU1IFNXY
Support:
Best effort, ruidanli@uchicago.edu
Reproducibility condition:
Observe a graph similar to Figure 16 of the Roller paper.
Requirements
This experiment requires a Chameleon account with an active project allocation.
Estimated Time
Around 2 hours
Launching this artifact will open it within Chameleon’s shared Jupyter experiment environment, which is accessible to all Chameleon users with an active allocation.
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