Real-Time Continuous Optimization Platform for CPU-Based AI Applications

MAY NOT BE REPRODUCED WITHOUT PERMISSION

Executive Summary
Use Case #1 – Machine Learning and Predictive Modeling
Use Case #2 – Large-scale Data Processing and Analytics
Use Case #3 – Text Analytics and Basic NLP
Use Case #4 – Real-time Stream Processing
Optimizing AI with Intel® Granulate
Intel Software for Optimizing AI

Executive Summary

To say AI and generative AI have changed the way business is being conducted is an understatement. McKinsey research shows that a third of organizations are already using gen AI regularly. [1] Gartner estimates that 45% of companies are piloting programs and another 10% have gone live. [2]

Perhaps as telling is that nearly half of executives say they are also using gen AI tools outside of work. This familiarity and acceptance of AI tools will likely shape future investment. Nearly 40% of companies say they plan to increase their investment in AI. [1]

Talk about disruptive.

“The impact of gen AI alone could automate almost 10 percent of tasks in the US economy.”
Kweilin Ellingrud, Director, McKinsey Global Institute [3]“

Over the next 10 years, AI could increase productivity by 1.5% per year. And that could increase S&P profits by 30% or more over the next decade.”
Ben Snider, Senior Strategist, Goldman Sachs [4]

The global AI market is estimated at more than $305 billion in 2024 and is forecast to grow at a CAGR of more than 15% through 2030, reaching $738 billion. [5] Yet, most organizations are still in their infancy when it comes to deploying AI. There are hardware, workflow, accuracy, and financial challenges that must be overcome. Harnessing AI efficiently requires an optimized environment to maximize throughput and keep costs under control.

Running AI workloads is incredibly expensive. OpenAI spends up to $700k a day maintaining its infrastructure and server costs. [6] And AI is a power-hungry endeavor. One estimate shows that by 2027, AI alone could consume as much energy as the entire country of Sweden. [7]

GPUs are the dominant choice for most AI workloads, especially those involving complex deep learning. Parallel processing handles massive amounts of data much faster. But there are also concerns about GPUs. Intel research shows worries among users about the lack of availability and affordability of GPU chips and having one vendor (Nvidia) that is setting the direction for the industry. Industry leaders are also concerned about optimization across end-to-end AI pipelines amid spiraling budgets.

However, there are options. For example, CPUs can handle certain AI tasks effectively at a reduced cost for hardware, infrastructure, licensing, and power consumption. In this eBook, we will examine:

Four specific use cases and workloads for CPU-based AI
How to overcome the challenges in optimizing AI workflows and environments
An overview of how IntelGranulate provides real-time continuous optimization for CPU-based AI applications

Intel Granulate can reduce the costs of running AI workloads by up to 45% with no code changes.

Use Case #1 – Machine Learning and Predictive Modeling

CPUs can be highly effective for certain machine learning (ML) and predictive modeling tasks — enabling more cost-efficient AI processing. Predictive modeling predicts future outcomes while ML algorithms enable systems to learn and improve over time.

While GPUs are typically preferred for intensive deep learning tasks, CPUs can be a highly effective solution for ML algorithms and models, especially those involving branching logic, sequential operations, or smaller datasets. For example:

Decision Trees: A tree-like model used for classification and regression tasks, well-suited for CPUs due to their branching logic and sequential operations.
Random Forests: An ensemble learning method that combines multiple decision trees, benefiting from the CPU’s ability to handle branching logic and parallel processing of individual trees.
Linear Models: Models that assume a linear relationship between the input features and the target variable, such as linear regression and logistic regression, which can be efficiently executed on CPUs.

An example might be a financial institution that uses decision trees or random forests for credit risk assessment or fraud detection. CPUs can generally handle this type of workload and produce cost savings compared to running similar workloads on GPUs.

Challenges in Optimizing CPU Performance for ML Workloads

Despite the benefits of using CPUs for certain ML workloads, there are some real challenges to optimize performance, including:

Memory Management and Caching Issues: Inefficient memory management and caching can result in poor resource utilization, negatively impacting performance.
Inefficient CPU Resource Utilization: Factors such as underutilization, thread contention, and suboptimal task scheduling can lead to inefficient use of CPU resources.
Scalability Challenges for Distributed ML: Effectively distributing ML workloads across multiple CPUs can be complex, requiring efficient load balancing and resource allocation strategies.
Lack of Specialized Hardware Acceleration: Unlike GPUs, CPUs lack specialized hardware acceleration for certain ML operations, which can limit performance for specific tasks.

Intel Granulate’s real-time continuous optimization platform overcomes these challenges to realize optimal performance for ML and predictive modeling workloads.

Use Case #2 – Large-scale Data Processing and Analytics

AI is driving significant growth in Big Data analytics. Forecasts show the market growing from $160 billion in 2022 to $399 billion by 2030. [8] Providing the foundation for data insights, big data is characterized by its volume (massive amounts of data), velocity (high-speed data generation), and variety (diverse data formats and sources).

Handling big data requires efficient and scalable data processing for tasks such as data preparation, feature engineering, and model training. However, this presents several key challenges to manage and process these datasets in real-time — without impacting ensuring data quality or integrity.

CPUs can be an effective solution for several AI workflows and data processing needs, such as:

Data Cleaning: Identifying and correcting or removing corrupt, inaccurate, or irrelevant data records, which often involves sequential operations and data manipulation.
Feature Engineering: Selecting, transforming, and creating new features from raw data to improve model performance, which can involve data manipulation and sequential operations.
Extract, Transform, Load (ETL): Extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse or analytics platform, which can benefit from CPUs’ data manipulation capabilities.

CPUs offer significant benefits for these types of tasks, performing efficient sequential operations and data manipulation capabilities with significantly lower power demands compared to GPU processing.

Challenges in Optimizing CPU Performance for Large-Scale Data Processing

That’s not to say there aren’t challenges associated with using CPUs for large-scale data processing. It’s essential to optimize performance to overcome challenges including:

Load Balancing and Resource Contention: In distributed environments, ensuring efficient load balancing and avoiding resource contention can be complex, potentially leading to underutilization or bottlenecks.
Inefficient Memory Usage and Caching: Inefficient memory management and caching strategies can result in poor resource utilization and performance limitations, especially when dealing with large datasets.
Lack of Specialized Hardware Acceleration: CPUs lack specialized hardware acceleration for certain data processing operations, which can limit performance for specific tasks compared to GPU-accelerated solutions.
Scalability Limitations: Highly parallelized data processing workloads may face scalability limitations on CPUs, as GPUs can offer superior parallel processing capabilities for such workloads.

Intel Granulate’s Big Data solution addresses these challenges by optimizing Spark workloads on CPUs. Optimization enables efficient and cost-effective large-scale data processing for AI applications. With Intel Granlute, you get efficient resource allocation and task scheduling — enabling more efficient and accurate AI model development and deployment.

Use Case #3 – Text Analytics and Basic NLP

The latest studies report that 328 million terabytes of data are created every day. It’s estimated that 90% of the world’s data was generated just within the past two years and volume and continuing to grow. [9] With this massive increase in structured and unstructured data, text analytics and natural language processing (NLP) require increasing optimization.

NLP enables computers to understand, interpret, and generate human language. As the volume of text data continues to grow, however, the ability to extract information becomes increasingly challenging. Without AI tools, however, these mass data sets can be overwhelming

CPUs can be highly effective for certain text analytics and NLP tasks to streamline and simply processing, including:

Text Preprocessing: Tasks like tokenization, stemming, and stop-word removal, which are often the initial steps in NLP pipelines, can be efficiently performed on CPUs due to their sequential nature and data manipulation capabilities.
Basic Parsing and Tagging: Shallow parsing and part-of-speech tagging, which involve analyzing the grammatical structure of text, can be suitable for CPUs, as these tasks often involve sequential operations and branching logic.
Rule-based NLP: Rule-based NLP systems, which rely on predefined rules and patterns rather than machine learning models, can be executed efficiently on CPUs due to their branching logic and data manipulation requirements.

CPUs’ strengths in sequential operations, data manipulation, and branching logic make them a solid solution for these tasks.

Challenges in Optimizing CPU Performance for Text Analytics and NLP Workloads

Just as in other applications, however, optimizing CPU performance is essential. Challenges include:

Data Cleaning Bottlenecks: These tasks, while suitable for CPUs, can become bottlenecks in NLP pipelines if not optimized properly, hindering overall performance.
Inefficient Memory Usage and Caching: Handling large text corpora or working with complex linguistic models can strain memory resources if not managed efficiently, leading to performance degradation.
Scalability Limitations for Highly Parallelized NLP Tasks: While basic NLP tasks can be suitable for CPUs, highly parallelized tasks like language model training or neural machine translation may face scalability limitations.

Intel Granulate optimizes CPU utilization and performance for text analytics and basic NLP tasks. By leveraging Intel Granulate’s solutions, organizations can unlock the cost-effectiveness of CPU-based systems while achieving optimal performance for their workloads.

Use Case #4 – Real-time Stream Processing

Real-time stream processing is essential in scenarios where timely insights and decision-making are crucial, such as fraud or anomaly detection or analyzing data from the expanding number of Internet of Things (IoT) devices and Manufacturing 4.0 machinery.

In these applications, low latency and high throughput are essential. Data delays can result in costly equipment downtime, malfunctions, data breaches, and missed opportunities. CPUs are an efficient solution for certain real-time stream processing tasks, including:

Event Processing: Tasks like filtering, aggregating, or transforming event streams can be efficiently handled by CPUs due to their sequential processing capabilities and low latency.
Rule-based Filtering and Routing: Applying predefined rules or conditions to filter, route, or process data streams based on specific criteria can benefit from CPUs’ branching logic and data manipulation capabilities.
Low-latency Inference: For real-time inferencing tasks involving smaller models or rule-based systems, CPUs can provide low-latency performance, which is critical in real-time stream processing scenarios.

CPUs demonstrate strength in low latency and branching logic, making them an effective solution at reduced costs vs. GPUs. Optimizing operations can lower costs even further.

Challenges in Optimizing CPU Performance for Real-time Stream Processing

Optimizing CPU performance for real-time stream processing faces similar challenges. Two notable ones include:

Load Balancing and Resource Contention: In high-throughput stream processing scenarios, ensuring efficient load balancing and avoiding resource contention can be complex, potentially leading to bottlenecks or performance degradation.
Inefficient Memory Usage and Caching: Inefficient memory management and caching strategies can result in poor resource utilization and performance limitations, especially when dealing with stateful stream processing or large data streams.

Intel Granulate solves utilization and performance challenges in real-time stream processing, ensuring workloads are rightsized and running at peak efficiency.

Optimizing AI with Intel Granulate

Intel Granulate offers three main solutions to optimize AI workloads running on CPUs: Big Data, Databricks, and Runtime/JVM. These solutions are designed to address the unique challenges and requirements of different AI workloads, fostering optimal performance, efficiency, and significant cost savings.

Big Data

The Big data solution is specifically tailored to optimize Spark workloads on CPUs. Apache Spark is a widely used open-source distributed computing framework for big data processing. It plays a crucial role in many AI pipelines such as data preprocessing, feature engineering, and model training.

Intel Granulate’s Big Data solution employs advanced techniques to optimize CPU-based Spark workloads, including:

Resource Management: The solution ensures efficient allocation and utilization of CPU resources, minimizing underutilization, oversubscription, and resource contention in distributed environments.
Memory Optimization: Advanced memory management techniques are employed to optimize memory usage and caching, reducing overhead and improving overall performance, especially when dealing with large datasets.
Task Scheduling Optimization: Intelligent task scheduling algorithms are used to optimize the execution of Spark tasks on CPUs, leveraging their strengths and minimizing bottlenecks.

By optimizing Spark workloads, Intel Granulate’s Big Data solution helps you achieve demonstrable performance improvements at lower costs compared to non-optimized environments. You can fine-tune performance autonomously for:

Transformations: Mapping, filtering, and other transformation operations to improve CPU utilization and overall performance.
Actions: Reducing and collecting data, and minimizing data movement to improve efficiency.
Shuffles: Optimizing shuffle operations, which are often performance bottlenecks in Spark workloads to reduce overhead and improve throughput.

Intel Granulate works whether your systems are on-prem, cloud-based, or hybrid and supports all infrastructures including EMR, Dataproc, HDInsight, Cloudera, MapReduce, Spark, PySpark, and more.

https://granulate.io/solutions/big-data-databricks-optimization/

By continuously optimizing application runtime and resource allocation, you reduce the manual workload for data science, data engineering, and data analysis teams across:

Yarn Resource Allocation: Optimized to improve cluster density and remove waste from over-provisioning.
Spark Dynamic Allocation: Optimized dynamical allocation and removal of executors based on job patterns and predictive ideal heuristics.
Crypto and Compression Acceleration: Leverage crypto architecture, accelerators, and instruction sets.
Memory Arenas Optimization: Releasing memory space and object sizes to reduce allocation overhead.

Learn more about optimizing Big Data with our Comprehensive Guide to Big Data Optimization.

Databricks

Intel Granulate’s Databricks solution is designed to optimize Databricks deployments running on CPUs. Databricks is a popular, cloud-based platform that provides a unified analytics platform for big data processing, machine learning, and data engineering.

The Databricks solution leverages the benefits of Databricks’ managed services while optimizing the underlying workloads for CPU-based environments. Key optimizations include:

Spark Optimization: Similar to the Big Data solution, the Databricks solution optimizes Spark workloads running on CPUs within the Databricks environment, ensuring efficient resource utilization and performance.
JVM Runtime Optimization: Improve JVM performance for machine learning workloads executed on Databricks,
Memory Arenas Optimization: Manage and optimize memory arenas and object sizes in the Databricks environment.
Compression and Serialization Optimization: Leverage CPU capabilities for compression and serialization algorithms to improve performance for data transfer and storage.

By optimizing Databricks deployments on CPUs, organizations can leverage the power and scalability of the Databricks platform while benefiting from cost-effective and efficient execution of their AI workloads.

https://granulate.io/solutions/databricks/

By continuously monitoring and securely optimizing large-scale Databricks workloads, organizations can improve job completion time and cut cloud infrastructure costs with no code changes. Intel Granulate is a Databricks validated technology partner and supports all major CSPs.

Learn more about optimizing Databricks with our in-depth Guide to Databricks Optimization.

Runtime/JVM

The Runtime/JVM solution from Intel Granulate focuses on optimizing the performance of the Java Virtual Machine (JVM) for CPU-based AI applications. Many AI frameworks and libraries are written in Java or other JVM-based languages, making JVM performance a critical factor in overall application performance.

The Runtime/JVM solution employs various techniques to optimize JVM performance, including:

Lockless Networking: Enhanced lock-free network stacks designed to achieve parallelism and maximize throughput.
Thread Pool Sizing and Scheduling: Adaptive thread pool sizing and scheduling mechanisms are employed to ensure optimal utilization of CPU resources and minimize thread contention and overhead.
Inter-Process Communication: Leveraging contemporary protocols and shared memory to reduce overhead and improve throughput.

By optimizing JVM performance, Intel Granulate’s Runtime/JVM solution enables organizations to achieve significant resource utilization gains for their CPU-based AI applications, without the need for code changes or refactoring. For example, running Intel Granulate on second gen Intel Xeon scalable processors reduced throughput for Java Ops by 13,7% per second and throughput with SLA Java Ops by 12.3% per second.

https://granulate.io/runtime-optimization

Intel Granulate autonomously optimizes at the core, on the application level, to improve runtime efficiency. This provides lean and efficient runtimes for enhanced performance utilizing fewer resources and avoiding expensive overprovisioning. This creates faster response times and improves throughput while also reducing latency and stability even at peak times.

Testing Intel Granulate — 3 Simple Steps

Testing Intel Granulate is simple. You can get a free trial and install it in about 15 minutes on your applications with no code changes. Let Intel Granulate autonomously learn your data, applications, and workloads — this takes about a week. Once this process takes place, you’re ready to deploy. In about 15 minutes, you can activate Intel Granulate and immediately see performance improvement[PD6] s.

https://granulate.io/blog/ai-applications-can-benefit-from-autonomous-optimization/

Request a demo of Intel Granulate today and see for yourself.

Intel Software for Optimizing AI

Besides Intel Granulate, Intel offers a comprehensive software portfolio designed to optimize AI workloads across various environments and infrastructure. These solutions complement Intel Granulate’s capabilities and provide organizations with a holistic approach to optimal performance, and cost savings for AI

Machine Learning Environment Optimization

cnvrg.io enables organizations to manage and optimize AI workloads across diverse infrastructures, including on-premises, cloud, and hybrid environments. With cnvrg.io’s full-stack machine learning operating system, you can:

Run ML jobs faster and less expensively
Mix and match infrastructure to your end-to-end flow
Maximize workload performance and speed on any compute and storage
Connect your storage and compute to launch AI workloads on demand

From a single launch pad, you can unify code, projects, models, repositories, compute, and storage with control and visibility across all of your ML runs. This allows you to deliver faster AI applications and results while automating and monitoring your ML workflow from research to production.

Cloud-based AI Development Environment

The Intel Developer Cloud is designed for development with a suite of Intel software on the latest Intel Xeon processors and compute. This allows you to build, test, run, and optimize AI applications at lower cost and reduced overhead to:

Accelerate AI and HPC: Accelerate and scale AI with the latest hardware and software innovations, gaining more compute power to fine-tune your software and generative AI.
Optimize for the Edge: Evaluate, benchmark, and prototype AI and edge solutions on Intel hardware. You can also launch containerized workloads on Intel architectures using Kubernetes.
Build Multiarchitecture and FPGA Applications Program your oneAPI multiarchitecture applications using Intel oneAPI and AI tools or test your workloads across Intel field programming gate array (FPGA), CPU, and GPU environments.

By combining Intel Granulate’s real-time continuous optimization capabilities with the broader software portfolio from Intel, organizations can unlock the full potential of AI while seeing significant performance gains and reduced costs.

RESOURCES

IMAGE SOURCE: Bing Image Creator