**A use case on Logistic regression training**

Over the last few years there are several efforts for more powerful computing platforms to face the challenges imposed by emerging applications like machine learning. General purpose CPUs have been developed specialized ML modules, GPUs and FPGAs with specialized engines are around the corner. Several startups develop novel ASICs specialized for ML applications and Deep Neural networks.

In this article we perform a comparison of 3 different platforms available on the cloud (general purpose CPUs, GPUs and FPGAs). We evaluate the performance in terms of total execution time, accuracy and cost.

For this benchmark we have selected logistic regression as it is one of the most widely used algorithm for classification. Logistic Regression is used for building predictive models for many complex pattern-matching and classification problems. It is used widely in such diverse areas as bioinformatics, finance and data analytics. Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function.

Logistic Regression was chosen because it’s arguably the most popular algorithm for building predictive analytics use cases and its iterative procedure when fitting the model, which allows us to extract better results on this comparison.

In this case we evaluate the training of the MNIST dataset with 10 classes and 1 million dataset.

The purpose of these notebooks is to compare and evaluate the performance of a Logistic Regression model using three implementations, Python’s Sklearn package (alongside Intel’s Math Kernel Library), Rapids cuml library and InAccel Sklearn-like package. The first one is the widely used Data Science library, Intel’s MKL is a cpu math processing accelerated framework, while the others are newer solutions built on top of GPU and FPGA accelerators respectively.

We compared the training in 4 different platforms:

**Reference**: Intel Xeon Skylake SP (r5.2xlarge with original code)**MKL**: Intel Xeon Skylake SP (r5.2x large using MKL libraries)**GPU**: NVIDIA® V100 Tensor Core (p3.2x large RAPIDS library)**FPGA**: FPGA (f1.2x using InAccel ML suite)

In the case of general purpose CPUs we use both the original code (without optimized libraries) and the Intel MKL library for optimization of ML kernels. In the case of GPUs, we use the RAPIDS framework and in the case of FPGAs we use our own ML suite for logistic regression available from InAccel.

The following figure depicts the performance of each platform (total execution time). As you can see in terms of performance, GPU achieves the best performance compared to the rest of platforms. However, the accuracy in this case is only 73% while the rest of the platforms can achieve up to 88% accuracy. So in terms of accuracy, FPGAs using the InAccel ML suite can achieve the optimum performance and very high accuracy.

Total execution time for ML training of logistic regression (MNIST). In parenthesis, the accuracy achieved for each platform/algorithm.

However, cost is also very important for enterprises and data scientists. In this case we compare the performance vs cost trade off using these 4 platforms. The cost of each platform is shown below:

- r5.2xlarge: $0.504/h
- p3.2xlarge: $3.06/h
- f1.2xlarge: $1.65/h

In the following figure we show the performance (total execution time) and total cost for the ML training for these 4 platforms.

Performance vs Cost for the training of Logistic regression using MNIST (In the parenthesis you can see the accuracy each model achieved)

As you can see in this figure, FPGAs on the cloud (f1.2xlarge on this case with InAccel ML suite) achieves the best combination in terms of performance-accuracy and cost. Optimized libraries for GPP (MKL) achieve the most cost-efficient solution but the performance is not as high as using accelerators. GPUs can achieve better performance but the cost is much higher and in this case the accuracy is not acceptable in many applications.

© 2020 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Upcoming DSC Webinar**

- Optimization and The NFL’s Toughest Scheduling Problem - June 23

At first glance, the NFL’s scheduling problem seems simple: 5 people have 12 weeks to schedule 256 games over the course of a 17-week season. The scenarios are potentially well into the quadrillions. In this latest Data Science Central webinar, you will learn how the NFL began using Gurobi’s mathematical optimization solver to tackle this complex scheduling problem. Register today.

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Upcoming DSC Webinar**

- Optimization and The NFL’s Toughest Scheduling Problem - June 23

At first glance, the NFL’s scheduling problem seems simple: 5 people have 12 weeks to schedule 256 games over the course of a 17-week season. The scenarios are potentially well into the quadrillions. In this latest Data Science Central webinar, you will learn how the NFL began using Gurobi’s mathematical optimization solver to tackle this complex scheduling problem. Register today.

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central