ML and DL Libraries Performance Optimization

CASE STUDY

ML and DL Libraries Performance Optimization

big data   ·    C++   ·    deep learning   ·    Linux   ·    machine learning   ·    neural networks   ·    Python

Scope

A US-based semiconductor company asked Auriga to analyze and optimize Machine Learning and Deep Learning libraries’ performance on new processors.

Projects Highlights

Research and optimization of the ML/DL libraries’ performance: TensorFlow, Caffe, MXNET, scikit-learn, etc.

Low-level analysis of bottlenecks in basic mathematical algorithms (vector/matrix multiplication).

Parallel calculations features.

Comparison with state-of-the-art benchmarks of the leading hardware manufacturers.

Achieved Benefits

Neural networks libraries’ bottlenecks revealed in the course of detailed performance analysis.

Performance maximized due to optimal hardware/software configurations found.

Developed benchmarks demonstrate 20-30% higher performance for deep neural networks training on new processors compared to competing platforms.

Technologies

Linux  ·  Python  ·  C++  ·  DLBench

DeepBench  ·  Statistical analysis

Most Relevant Cases

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