Summary of Efficient Arbitrary Precision Acceleration For Large Language Models on Gpu Tensor Cores, by Shaobo Ma et al.
Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores
by Shaobo Ma, Chao Fang, Haikuo Shao, Zhongfeng Wang
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel comprehensive acceleration scheme for large language models (LLMs) is proposed to address the challenges in efficient inference. The scheme introduces a bipolar-INT data format that facilitates parallel computing and supports symmetric quantization, reducing data redundancy. An arbitrary precision matrix multiplication scheme is implemented, decomposing and recovering matrices at the bit level, enabling flexible precision while maximizing GPU Tensor Core utilization. Additionally, an efficient matrix preprocessing method optimizes data layout for subsequent computations, and a data recovery-oriented memory management system minimizes memory access latency. Experimental results demonstrate up to 2.4speedup in matrix multiplication compared to NVIDIA’s CUTLASS, achieving up to 6.7inference acceleration when integrated into LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are working on making large language models (LLMs) faster and more efficient. They propose a new way to make LLMs work better on computers. This approach uses a special kind of data storage that makes calculations faster and more accurate. It also improves how the computer stores and retrieves information, which helps speed up the processing time. The results show that this method can make LLMs run up to 2.4 times faster than before, making them more useful for many applications. |
Keywords
» Artificial intelligence » Inference » Precision » Quantization
