The world changed decades ago when researchers discovered how to translate the matrix processing capabilities of Graphics Processing Units (GPUs) to workloads other than gaming graphics rendering.
The unparalleled growth in Artificial Intelligence/ Machine Learning/ Deep Learning (AI/ ML/ DL), Big Data analytics, real-time inference operations and Internet of Things applications (IoT) since then would never have been imaginable without the tremendous hardware acceleration delivered by modern GPUs.
However, in business ecosystems with cut-throat competition, the second most important thing for an enterprise after “data” is “time”. Traditional CPU-based data processing is clunky and slow at the best of times, and jars irretrievably when expected to handle terabytes worth of input data. Enterprises want powerful processors for compute-intensive workloads and have naturally gravitated away from CPU-based processing towards GPU-accelerated processing.
This article will discuss the nature of compute-intensive workloads and the need for GPU acceleration. Besides the benefits of GPU acceleration, we will also discuss some software frameworks and toolkits that enable enterprises to utilize GPU resources for compute-intensive data processing. Dive in!
Introduction to Compute-Intensive Applications
These are complex workloads which require extensive computation in the form of mathematical calculations, data modelling and visual simulations. Advanced understanding of algorithms, data structures, computer architecture and system design are a pre-requisite for developing such applications. The performance of compute-intensive applications can be substantially optimized by tweaks in the system design, including data flow characteristics and processor scheduling.
Examples of compute-intensive workloads include –
1. Artificial Intelligence (AI), Machine Learning/ Deep Learning (ML/ DL), and Artificial Neural Network applications (ANNs)
2. Blockchain development and Cryptography hashes – Financial applications as well as IT network security through crypto-encryption
3. Scientific and engineering simulations, including Computer-Aided Architecture Design (CAAD) and Digital Twin concept deployed in modelling and virtually studying physical systems
4. Immersive gaming, high-res 3D rendering and animations
5. Data analytics, including statistical modelling, prediction and inference applications, and market/ social media sentiment analysis
The Case for GPU-Accelerated Computing
Demanding applications like Artificial Intelligence/ Machine Learning training, cryptography hashes and engineering stimulations must execute quickly and efficiently for the project to be financially and operationally viable.
Accelerated computing has been growing by leaps and bounds as resource-intensive workloads become more commonplace across industries. Specialized hardware like Graphics Processing Unit (GPU), Application-Specific Integrated Circuit (ASIC) and High-Performance Computing clusters (HPC) are now being deployed on-prem or via Cloud access to dramatically speed up data processing.
The use of GPU for accelerating data processing, complex calculations, and machine learning has, in fact, become the new normal across numerous industries, ranging from weather forecasting to sales and marketing! The global GPU market is projected to touch USD 200 billion by 2029 and USD 400 billion by 2032!
GPU Market Size by 2029: Integrated + Discrete (Source)
This tremendous surge in GPU utilization across industries is predominantly fueled by data analytics’ potential for extraction of business-specific actionable insights. Enterprises use accelerated computing for compute-intensive workloads by streamlining them in instruction bundles. These bundles contain all the frequently occurring tasks that can be simultaneously executed through parallel processing. GPUs are, by design, manufactured for large-scale parallel processing, delivering unprecedented performance while also reducing the time-to-market for AI/ ML applications, highly immersive games, and Blockchain-based financial products, etc.
Key scenarios where rapid, uninterrupted data processing offers encouraging returns on investments are as follows –
a. Training data models:
AI/ ML projects, particularly Deep Learning projects for natural language processing, e-commerce or social media networks, incorporating Artificial Neural Networks (ANNs), feed on humongous amounts of data for the initial training and comprehension. Such projects must utilize on-prem or cloud-based GPU hardware for substantially easing the ML/ DL training process.
b. Immersive graphics projects:
GPU resources have now become mandatory for individuals/ enterprises that develop high-res graphics, including immersive games and detail-oriented animation projects. Nvidia’s latest Ampere-class GPUs come equipped with the extraordinarily powerful Ray Tracing feature (RT) that lets visual content creators play around with complex graphics rendering and numerous light-shade-color options.
c. Real-time data analytics:
GPU-accelerated computing demonstrates tremendous promise across the entire spectrum of value extraction from data – whether structured, semi-structured or unstructured. The future of production and marketing lies with enterprises deriving constructive, actionable, real-time insights from data streams collated from multiple sources.
d. Blockchain management:
Blockchain technology has found use across sectors as diverse as finance and healthcare! The compute power required to validate changes in the blockchain has been increasing dramatically, however, with each passing day. Processing such massive volume, variety and velocity of data is impossible with CPU-based systems, thereby necessitating the reliance on GPUs, specifically enterprise-class GPU servers.
Libraries for GPU Hardware utilization –
AI/ ML engineers and data scientists rely on a number of pre-designed libraries to efficiently utilize GPU hardware for compute-intensive workloads. The most popular among these are –
1. CUDA Toolkit: Extensively used for development and deployment of software applications, the CUDA toolkit comes pre-equipped with GPU-accelerated libraries, C/C++/Python tools, and debugging and optimization tools. Furthermore, it allows effortless scaling from single GPU workstation to cloud-based multi-GPU distributed environment.
2. PyTorch: Widely used, open-source ML framework, PyTorch supports NVIDIA’s CUDA-standard GPUs to accelerate compute-intensive workloads to their maximum potential. By using device arguments with PyTorch modules, developers can specify which device to utilize (CPU or GPU), which cores to leverage (CUDA or Tensor), as well as determine the precision and type of GPU-processing.
3. TensorFlow: A well-known ML library developed by Google, it allows developers to write code that can split incoming data into smaller fragments and distribute them across multiple GPUs for maximum hardware utilization and processing efficiency.
4. Keras: Python-based open-source software library used for developing and implementing Artificial Neural Networks in Deep Learning applications. It can be utilized across a variety of hardware configurations including single/multiple GPU and TPU.
Instead of investing in expensive GPU hardware, you can also access high-end GPU resources via Cloud services by subscribing to the pay-as-you-go model. This lets you use GPU machines for as long as you require and then turn the financial tap off as soon as your project (AI/ ML training, or financial modelling, or statistical analysis, etc.) is complete. You also need not undergo the headache of configuring the GPU hardware and network cables, nor do you need to invest time and capital in installing air-conditioners etc. to protect your GPUs from overheating.
