Summary of Misam: Using Ml in Dataflow Selection Of Sparse-sparse Matrix Multiplication, by Sanjali Yadav et al.
Misam: Using ML in Dataflow Selection of Sparse-Sparse Matrix Multiplication
by Sanjali Yadav, Bahar Asgari
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 In this paper, researchers tackle the challenge of optimizing sparse matrix-matrix multiplication (SpGEMM) on hardware accelerators. SpGEMM is crucial in various fields like scientific computing, graph analytics, and deep learning, where exploiting sparsity reduces storage and computational demands. However, traditional hardware accelerators are designed for specific sparsity patterns, leading to suboptimal performance when the actual sparsity deviates from these patterns. To address this issue, the authors propose a machine learning-based approach that adaptively selects the most suitable dataflow scheme for SpGEMM tasks with diverse sparsity patterns. This involves employing decision trees and deep reinforcement learning to identify optimal dataflow schemes, outperforming heuristic-based methods in some cases. The authors evaluate their models by comparing their performance with that of a heuristic, highlighting the strengths and weaknesses of each approach. Their findings show that using machine learning for dynamic dataflow selection can provide significant gains (up to 28 times), making this approach attractive for various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about finding the best way to perform a type of math operation called SpGEMM on special computer chips. This operation is important in many areas like science, graph analysis, and artificial intelligence. The problem is that these computer chips are designed to work with specific types of sparsity (how much empty space there is in the data), but real-world data often has different patterns of sparsity. To solve this issue, the researchers use machine learning techniques to automatically choose the best way to perform SpGEMM for different types of data. They tested their approach and found that it can be up to 28 times faster than previous methods. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Reinforcement learning
