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Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity.

H. Xia, Z. Zheng, Y. Li, D. Zhuang, Z. Zhou, X. Qiu, Y. Li, W. Lin, and S. Song. CoRR, (2023)

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Jinliang Zhuang

Guangchao Zhuang

Yan Zhuang

Zhifeng Zhuang

Weilun Zhuang

Other publications of authors with the same name

Randomness In Neural Network Training: Characterizing The Impact of Tooling.D. Zhuang, X. Zhang, S. Song, and S. Hooker. CoRR, (2021)An Efficient End-to-End Deep Learning Training Framework via Fine-Grained Pattern-Based Pruning.C. Zhang, G. Yuan, W. Niu, J. Tian, S. Jin, D. Zhuang, Z. Jiang, Y. Wang, B. Ren, S. Song and 1 other author(s). CoRR, (2020)Randomness in Neural Network Training: Characterizing the Impact of Tooling.D. Zhuang, X. Zhang, S. Song, and S. Hooker. MLSys, mlsys.org, (2022)MonoNN: Enabling a New Monolithic Optimization Space for Neural Network Inference Tasks on Modern GPU-Centric Architectures.D. Zhuang, Z. Zheng, H. Xia, X. Qiu, J. Bai, W. Lin, and S. Song. OSDI, page 989-1005. USENIX Association, (2024)ClickTrain: efficient and accurate end-to-end deep learning training via fine-grained architecture-preserving pruning.C. Zhang, G. Yuan, W. Niu, J. Tian, S. Jin, D. Zhuang, Z. Jiang, Y. Wang, B. Ren, S. Song and 1 other author(s). ICS, page 266-278. ACM, (2021)Quant-LLM: Accelerating the Serving of Large Language Models via FP6-Centric Algorithm-System Co-Design on Modern GPUs.H. Xia, Z. Zheng, X. Wu, S. Chen, Z. Yao, S. Youn, A. Bakhtiari, M. Wyatt, D. Zhuang, Z. Zhou and 3 other author(s). USENIX ATC, page 699-713. USENIX Association, (2024)Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity.H. Xia, Z. Zheng, Y. Li, D. Zhuang, Z. Zhou, X. Qiu, Y. Li, W. Lin, and S. Song. CoRR, (2023)Bring orders into uncertainty: enabling efficient uncertain graph processing via novel path sampling on multi-accelerator systems.H. Zhang, L. Li, H. Liu, D. Zhuang, R. Liu, C. Huan, S. Song, D. Tao, Y. Liu, C. He and 2 other author(s). ICS, page 11:1-11:14. ACM, (2022)An efficient uncertain graph processing framework for heterogeneous architectures.H. Zhang, L. Li, D. Zhuang, R. Liu, S. Song, D. Tao, Y. Wu, and S. Song. PPoPP, page 477-479. ACM, (2021)η-LSTM: Co-Designing Highly-Efficient Large LSTM Training via Exploiting Memory-Saving and Architectural Design Opportunities.X. Zhang, H. Xia, D. Zhuang, H. Sun, X. Fu, M. Taylor, and S. Song. ISCA, page 567-580. IEEE, (2021)

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Disambiguation of "Zhuang, Donglin"

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Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity.

Please choose a person to relate this publication to

Jinliang Zhuang

Guangchao Zhuang

Yan Zhuang

Zhifeng Zhuang

Weilun Zhuang

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Zhuang, Donglin"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity.

Please choose a person to relate this publication to

Jinliang Zhuang

Guangchao Zhuang

Yan Zhuang

Zhifeng Zhuang

Weilun Zhuang

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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MSOffice XML

Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity.