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How did the GPU Accelerated System Transform Data Science?

Data has become the most important resource for an organization. This has empowered the role of data science in transforming businesses into AI-fuelled enterprises. Data analytics and machine learning have become essential to business success. They are instrumental in helping businesses make more informed decisions and hence improve efficiency daily. Despite the growing use of these technologies, the desired speed of data processing was not achieved primarily because they were made to run on legacy systems not traditionally meant to support them. This has been the major reason why the workflows of data science could not be speedy. Everything was based on the capacity of the CPU which was behind all the data models. There was an increased need felt by the customers to be able to convert data into actionable insights. Hence came the GPU Computing.

What is GPU Computing?

Graphics Processing Unit or a GPU was initially created to render graphics, but due to its great performance and cost advantage, it soon took to the realm of image processing. GPU computing refers to combining the capabilities of both the GPU and the CPU for the acceleration of various applications. Nvidia and AMD  are the major players in the GPU market.

Major Challenges Faced by Legacy Data Systems     

Data scientists were constantly burdened due to repeated downtime resulting from the inefficient workflows. They were faced with regular wait times because of the delays caused by tools based on CPUs for data preparation, training of various models, and even evaluation of results. In case when the Data scientists had to give shape to various ML models they had to spend long times on preparation of data, designing models based on the same and even months were spent on evaluation of their efficiency and hence a selection of models. To add to the woes, this process had to be undertaken on a continuous basis.

How Did Things change with NVIDIA GPU?

NVIDIA came as a game-changer with its GPU-backed platform which drastically overtook the CPU architecture in terms of speed and performance. Modern GPU was able to execute the complete ML workflow in high-speed memory of the system and parallel running data loading and data manipulation. This was made possible by the launch of the Real-time Acceleration Platform for Integrated Data Science (RAPIDS), which was designed to deliver end-to-end data science infrastructure.

RAPIDS presented a wholesome platform to the businesses who wanted to accelerate their ML and data science workflows

Advantages of NVIDIA GPU-based platforms

  • Faster Data Analysis: The NVIDIA GPU-accelerated platforms helps the users in streaming, processing, querying, and even analyzing the datasets in a matter of milliseconds down from a time running in hours. They are comfortably able to meet increased data demand and linear scalability. Even the analytical processing times are significantly reduced for billions of data set rows by more than 100X.
  • More Data Visualization: These platforms are 10-100 times faster than all the existing systems and allow the users to perform complex and multidimensional visual rendering in real-time. It allows an easy correlation analysis. Users are now able to interact with over a million edges and get insights from 100X more data.
  • More Computing Power: The platform is completely focused on the synergy created by Artificial Intelligence, visual processing, and even high-performance computing. The GPU-accelerated algorithms can read highly complex and large patterns which are not possible for the software which was coded manually.
  • Turn Data into Knowledge: This is done by revealing patterns in huge data sets for bringing to light new knowledge and insights in a matter of hours and minutes and not in days or weeks.
  • Crossing the Competition: It also helps in delivering highly fast solutions for various deep learning training and AI-accelerated analytics workloads.
  • Maximization of the Investment: It helps in improving Return on Investment by an apparent increase in productivity with a compute power around 800 CPUs put together with no hidden costs of traditional systems.

RAPIDS

RAPIDS is basically a GPU-based open-source suite of various software libraries and APIs designed specially to enable users to implement end-to-end data science and analytics pipelines which completely rest on GPUs. It enables much faster data preparation, model training, and ultimately graph analytics. Businesses can immensely use the same for achieving new milestones in the accuracy of models. It is directed towards most commonly run tasks of data preparation for both analytics and data science. Support for multi-GPU and multi-nodes is also included thereby enabling highly accelerated processing and training on huge datasets of large sizes.

It makes use of NVIDIA CUDA primitives for the low-level compute optimization and even reveals the GPU parallelism via its Python interfaces.

Libraries in Brief

  • cuDF: It is a dataframe manipulation library which allows for parallel data loading and manipulation along with using the high-bandwidth memory which is found in various NVIDIA GPUs. It is a great replacement based on Python to the Pandas toolset.
  • cuML: It is a collection of various ML libraries which give GPU versions of algorithms
  • CuGRAPH: It is a graphing API like network-X

RAPIDS gives native array_interface support due to its Apache Arrows roots to enable data to be pushed to those frameworks of deep learning which accept the array_interface like PyTorch, Chainer, etc. It will soon capture the market based on its faster iteration and much more frequent deployment which leads to enhanced model accuracy. Also, due to its Python focus, it can play well with most data science visualization libraries.

Conclusion

The true power of RAPIDS is in the fact that it has freed the users from the computing constraints of the legacy systems. It has empowered people to reimagine and test new ideas and also pursue new goals. It has perfectly balanced both the speed of writing code and the speed of executing it. Data scientists can now make most of the benefits offered by it like enhanced productivity, a faster iteration of models, improved accuracy of prediction, model accuracy, and also bringing down the total cost of ownership. NVIDIA has thus successfully plugged the gaps in the traditional ML pipelines by RAPIDS. Also, being an Open Source Software is another big advantage as it can be easily customized and extended without any hassles. It has the support of some of the notable names in the industry like Anaconda, Databricks, IBM, Uber, etc. RAPIDS truly allows the data scientists to constantly shift many tasks to a platform which is based on GPUs.


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