Abstract
State-of-the-art machine learning frameworks support a wide variety of design features to enable a flexible machine learning programming interface and to ease the programmability burden on machine learning developers. Identifying and using a performance-optimal setting in feature-rich frameworks, however, involves a non-trivial amount of performance profiling efforts and often relies on domain-specific knowledge. This article takes a deep dive into analyzing the performance impact of key design features in a machine learning framework and quantifies the role of parallelism. The observations and insights distill into a simple set of guidelines that one can use to achieve much higher training and inference speedup. Across a diverse set of real-world deep learning models, the evaluation results show that the proposed performance tuning guidelines outperform the Intel and TensorFlow recommended settings by 1.30× and 1.38×, respectively.
Original language | English (US) |
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Article number | 9 |
Journal | ACM Transactions on Architecture and Code Optimization |
Volume | 18 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2021 |
Externally published | Yes |
Keywords
- Machine learning frameworks
- parallel computing
- performance analysis
ASJC Scopus subject areas
- Software
- Information Systems
- Hardware and Architecture