Summary of Input-dependent Power Usage in Gpus, by Theo Gregersen et al.
Input-Dependent Power Usage in GPUs
by Theo Gregersen, Pratyush Patel, Esha Choukse
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 approach is presented to optimize GPU power consumption in data centers by modifying input data for general matrix-matrix multiplications (GEMMs). The study shows that altering value distribution, bit similarity, placement, and sparsity of input data can significantly change power consumption, with a variation of almost 40% observed. This phenomenon is attributed to changes in the number of bit flips in GPUs. To manage power and reduce energy consumption, compiler and scheduler optimizations are proposed to leverage this property. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GPUs need lots of power because they’re used for artificial intelligence tasks. These tasks often involve big calculations that use most of the GPU’s resources. Researchers found a way to make these calculations use less power by changing how the data is arranged. They tried four different ways: making the numbers in the data more or less similar, moving the data around, and leaving some parts empty. By doing this, they were able to reduce the power used by the GPU by almost 40%. This discovery could help make data centers more energy-efficient. |
