In part three of our ongoing series on optimizing AI and the applications that support them, we will discuss text analytics and basic natural language processing (NLP). If you haven’t yet, take some time to catch up on our previous discussions on Machine Learning and Large-Scale Data Processing and Analytics.
Much like the other technologies previously discussed in this series, text analytics and NLP have been increasing in popularity at a consistent rate over the past few years. And that trend is not slowing down.
This rise is evident with the market expected to reach $9 Billion by 2030, driven by the increasing use of business intelligence for prompt decision-making, where text analytics plays a crucial role in processing large volumes of unstructured text to derive actionable insights and patterns.he demand for these technologies is propelled by the digital interactions that generate substantial unstructured text data, which, when analyzed, can enhance profitability, customer experience, and security across various sectors.
Advancements in NLP are particularly noteworthy. With the success of sentiment analysis and conversational AI applications like ChatGPT by OpenAI and the prevalence of AI-powered chatbots in the corporate world, NLP applications are now requiring unprecedented data resources, both in volume and complexity.
Benefits of Optimizing Text Analytics and Basic NLP
Here are some of the benefits that IT teams responsible for text analytics and basic NLP might take advantage of with the use of autonomous optimization:
Automated optimization of CPU usage in big data applications for text analytics and NLP can significantly speed up data processing, enabling faster analysis and insights derivation from large datasets.
By optimizing CPU utilization, organizations can reduce operational costs associated with data processing, including lower cloud computing expenses and on-premises infrastructure requirements.
Automated optimization allows for more efficient use of resources, enabling big data applications to scale more effectively and handle increasing volumes of data without a proportional increase in hardware resources.
Increased Application Performance
Optimizing CPU usage ensures that big data applications run at peak performance, minimizing delays and enhancing the user experience, particularly for resource-intensive tasks like sentiment analysis and language generation.
These benefits collectively contribute to a more robust and cost-effective infrastructure for handling the complex demands of text analytics and NLP applications, ensuring businesses can leverage the full potential of their data.
How CPU-Based Workloads Support GPU Applications
In text analytics and NLP applications, GPUs and CPUs serve distinct yet complementary roles due to their inherent architectural differences.
GPUs excel in handling parallel tasks simultaneously, making them ideal for the complex matrix operations and deep learning models commonly used in NLP and text analytics. Their architecture allows for efficient processing of large volumes of data, significantly speeding up tasks like sentiment analysis, language translation, and text generation. This parallel processing capability is crucial for training and deploying large-scale deep learning models, where the ability to handle massive datasets and perform computations quickly is a key advantage.
CPUs, on the other hand, are more versatile and efficient at handling a wide range of tasks, including the sequential and general-purpose computing tasks that form the backbone of many applications. In the context of text analytics and NLP, CPUs are often used for data preprocessing, feature extraction, and other tasks that do not require the heavy parallel processing capabilities of GPUs. They are particularly useful for tasks that involve complex logic or where low latency for individual operations is crucial.
In practice, GPUs and CPUs are often used together in text analytics and NLP applications to leverage their respective strengths. While GPUs handle the heavy lifting of model training and inference, CPUs manage the overarching tasks, data handling, and preprocessing steps. This complementary use ensures efficient processing, from raw data preparation to deep learning, allowing for optimized performance across the entire pipeline.
Intel Granulate for CPU-Based Text Analytics and Basic NLP
Big Data Optimization
Intel Granulate’s Big Data optimization capabilities can significantly enhance the performance of text analytics and basic NLP applications by ensuring that complex workloads are efficiently managed across various execution engines and platforms.
This optimization leads to precise resource allocation, minimizing CPU and memory waste, which is crucial for processing large volumes of unstructured text data inherent in text analytics. The dynamic scaling feature adapts to fluctuating data characteristics, ensuring that resources are optimally used, thus speeding up data processing times and reducing operational costs.
With Big Data Optimization, data engineering teams can complete more jobs in less time to increase pipeline and meet their SLAs with autonomously optimized EMR, Cloudera, Databricks, Dataproc, and HDInsight workloads.
For text analytics and NLP applications running on Databricks, Intel Granulate’s optimization can lead to increased processing density and reduced costs. By optimizing Spark executor scheduling and dynamic scaling, the solution ensures that data processing tasks specific to text analytics, such as sentiment analysis or entity recognition, are executed more efficiently. This not only improves the performance of these applications but also enhances the managed scaling capabilities of Databricks, ensuring seamless and cost-effective operations.
Runtime Optimization is particularly beneficial for these types of AI applications that rely on JVM for data processing. By fine-tuning JVM settings, Intel Granulate ensures that these applications run at peak performance, which is critical for tasks requiring intensive data parsing and processing. This level of optimization can significantly reduce latency and increase throughput in text analytics workflows, leading to faster insights and improved application responsiveness.
When combined with the Big Data and Databricks optimizations, this improvement on the runtime level drives even more value.