Course Introduction
GPU-Accelerated Data Processing And Analytics Training Course provides a comprehensive foundation for understanding how Graphics Processing Units can be used to accelerate data-intensive computing, analytics, artificial intelligence, machine learning, simulation, and large-scale data processing workloads. The course examines the principles behind GPU computing and explains how parallel processing architectures differ from traditional CPU-based approaches, enabling participants to identify workloads where GPU acceleration can deliver significant performance improvements.
The course explores the complete lifecycle of GPU-accelerated analytics, from data ingestion and memory management to parallel computation, optimization, visualization, and deployment. Participants learn how GPU architectures, memory hierarchies, kernels, threads, blocks, and parallel execution models influence analytical performance. Practical concepts are connected to real-world data engineering and analytics scenarios so that learners can understand not only how GPU technologies work, but also when and why they should be applied.
Participants are introduced to modern GPU computing ecosystems and programming approaches used for high-performance data analytics. The course covers technologies such as CUDA-based computing, GPU-enabled Python libraries, distributed GPU processing, accelerated dataframe operations, GPU machine learning, and GPU-aware data pipelines. Emphasis is placed on practical techniques for moving data efficiently between storage, CPU memory, and GPU memory while minimizing bottlenecks that can reduce the benefits of acceleration.
A major component of the course focuses on performance engineering and optimization. Learners examine profiling, benchmarking, memory utilization, kernel execution, workload parallelization, data transfer overhead, concurrency, and resource utilization. They also explore how to diagnose performance problems and determine whether a workload should remain CPU-based, be partially accelerated, or be redesigned specifically for GPU execution. This enables participants to make technically informed decisions about performance optimization and infrastructure investment.
The course also addresses the use of GPUs across modern analytics environments, including data science platforms, cloud infrastructure, distributed computing systems, machine learning pipelines, real-time analytics, and high-performance computing environments. Security, governance, cost management, sustainability, infrastructure planning, and operational monitoring are integrated throughout the training so that participants can consider GPU acceleration from both technical and organizational perspectives.
Emerging developments such as GPU-accelerated AI, generative AI infrastructure, multi-GPU computing, GPU virtualization, cloud GPUs, distributed GPU clusters, accelerated databases, edge GPU analytics, and heterogeneous computing are also examined. By the end of the course, participants will have a structured understanding of how GPU acceleration can transform data processing and analytics workloads while developing the knowledge required to evaluate, design, optimize, and manage GPU-enabled analytical solutions.
Duration
10 days
Who Should Attend
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Data analysts seeking to understand how GPU acceleration can improve analytical workload performance.
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Data scientists working with large datasets, machine learning models, and computationally intensive analytical workloads.
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Data engineers responsible for designing high-performance data processing and analytics pipelines.
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Machine learning engineers developing GPU-enabled training and inference workflows.
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Python developers interested in accelerated numerical computing and data analytics.
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Database professionals working with analytical systems that support GPU acceleration.
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Business intelligence professionals handling increasingly large and computationally demanding datasets.
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IT professionals responsible for evaluating and managing GPU-enabled infrastructure.
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Cloud engineers deploying GPU-enabled analytics and machine learning workloads.
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Software developers interested in parallel computing and heterogeneous processing architectures.
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Artificial intelligence practitioners working with computationally intensive models and datasets.
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High-performance computing professionals seeking to extend GPU technologies into analytics workloads.
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Research and academic professionals processing large scientific or numerical datasets.
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Technology architects designing scalable data and analytics platforms.
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DevOps, DataOps, and MLOps practitioners supporting accelerated analytical environments.
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Project managers responsible for data, analytics, AI, or high-performance computing initiatives.
Course Objectives
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Explain GPU architecture, parallel processing principles, and the fundamental differences between CPU-based and GPU-based data processing approaches.
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Identify analytical workloads that can benefit from GPU acceleration and evaluate situations where CPU processing may remain more appropriate.
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Understand GPU memory hierarchies, data movement, memory bandwidth, and transfer overheads that influence analytical application performance.
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Apply parallel computing concepts to data transformation, numerical computation, machine learning, and other computationally intensive analytical workloads.
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Develop an understanding of CUDA programming concepts including kernels, threads, blocks, grids, synchronization, and device execution.
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Use GPU-enabled Python and analytics libraries to perform accelerated dataframe, numerical, scientific, and machine learning operations.
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Design data processing pipelines that efficiently move information between storage systems, CPU memory, and GPU memory while minimizing bottlenecks.
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Apply profiling, benchmarking, and performance analysis techniques to identify inefficient GPU workloads and optimization opportunities.
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Understand multi-GPU, distributed GPU, and cloud GPU architectures for scaling demanding analytics and machine learning workloads.
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Evaluate GPU infrastructure based on performance, memory requirements, workload characteristics, scalability, cost, energy consumption, and operational requirements.
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Implement appropriate security, governance, monitoring, and resource-management practices for GPU-enabled data processing environments.
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Assess emerging GPU computing technologies and determine how accelerated analytics can support future data engineering, AI, and high-performance computing strategies.
Comprehensive Course Outline
Module 1: Foundations Of GPU-Accelerated Data Processing
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Principles of parallel computing and the role of GPUs in modern data processing and analytical workloads.
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Differences between CPU and GPU architectures, execution models, workloads, and computational performance characteristics.
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Characteristics of data-parallel, compute-intensive, memory-intensive, and highly parallel analytical workloads.
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Business and technical drivers for adopting GPU acceleration across data, analytics, artificial intelligence, and scientific computing.
Module 2: GPU Architecture And Computing Models
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GPU architecture components including streaming multiprocessors, processing cores, registers, caches, and memory systems.
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Threads, thread blocks, grids, warps, scheduling, and execution models used in GPU parallel computation.
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GPU memory hierarchy, global memory, shared memory, constant memory, registers, and their performance implications.
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Heterogeneous computing models that combine CPUs and GPUs for coordinated analytical and computational workloads.
Module 3: GPU Memory Management And Data Movement
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GPU memory allocation, deallocation, transfer mechanisms, and efficient management of analytical datasets.
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Host-to-device and device-to-host data transfers and their effects on end-to-end application performance.
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Memory bandwidth, coalesced access, caching, data locality, and techniques for reducing memory-related bottlenecks.
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Unified memory, pinned memory, asynchronous transfers, and advanced strategies for optimizing CPU-GPU data movement.
Module 4: CUDA Programming And Parallel Execution
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CUDA programming fundamentals including kernels, device functions, threads, blocks, grids, and execution configuration.
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Designing parallel algorithms and decomposing analytical computations into GPU-compatible execution patterns.
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Thread synchronization, race conditions, atomic operations, and techniques for maintaining computational correctness.
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CUDA execution optimization through occupancy analysis, thread organization, workload balancing, and efficient kernel design.
Module 5: GPU-Accelerated Python And Numerical Computing
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GPU-enabled Python environments and approaches for accelerating numerical and scientific computing workloads.
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Accelerated array operations, vectorization, mathematical functions, and numerical transformations on GPUs.
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GPU-compatible dataframe processing and techniques for accelerating large-scale data preparation workflows.
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Integration of GPU computing into existing Python analytics applications and computational notebooks.
Module 6: GPU-Accelerated Data Engineering And ETL
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Designing GPU-accelerated extraction, transformation, and loading pipelines for large-scale analytical datasets.
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Parallel data cleaning, filtering, joins, aggregation, sorting, encoding, and feature preparation techniques.
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GPU-aware file processing and efficient interaction with columnar data formats and high-throughput storage systems.
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Identifying ETL bottlenecks and redesigning data engineering workflows to exploit parallel GPU computation effectively.
Module 7: GPU-Accelerated Databases And Query Processing
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Principles of GPU acceleration within analytical databases, query engines, and large-scale data processing platforms.
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Parallel execution of filtering, aggregation, joins, sorting, and other database operations using GPU resources.
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Columnar data processing, vectorized execution, and techniques for improving analytical query throughput.
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Evaluating GPU-enabled database architectures for business intelligence, data warehousing, and advanced analytics.
Module 8: Real-Time And Streaming GPU Analytics
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Principles of GPU acceleration for high-throughput and low-latency streaming data processing environments.
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Parallel event processing, stream transformations, anomaly detection, and real-time analytical computations.
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GPU utilization strategies for sensor data, financial streams, telecommunications, industrial monitoring, and operational analytics.
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Designing pipelines that balance ingestion speed, GPU processing capacity, memory utilization, and real-time response requirements.
Module 9: GPU-Accelerated Machine Learning And AI
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GPU acceleration principles for model training, numerical optimization, deep learning, and large-scale machine learning workloads.
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Accelerating data preparation, feature engineering, model training, inference, and hyperparameter experimentation.
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GPU resource requirements for neural networks, transformers, computer vision, natural language processing, and generative AI.
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Performance considerations for single-GPU, multi-GPU, and distributed machine learning environments.
Module 10: GPU Performance Engineering And Optimization
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Benchmarking GPU workloads using throughput, latency, utilization, memory bandwidth, and end-to-end processing metrics.
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Profiling GPU applications to identify inefficient kernels, synchronization overhead, memory bottlenecks, and CPU-GPU transfer delays.
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Kernel optimization techniques including occupancy improvement, workload balancing, memory optimization, and execution concurrency.
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Establishing repeatable performance-testing methodologies for GPU-enabled data processing and analytics systems.
Module 11: Multi-GPU And Distributed GPU Computing
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Multi-GPU architectures and strategies for distributing computational workloads across multiple accelerator devices.
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GPU communication, synchronization, collective operations, and data partitioning in distributed computing environments.
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Distributed GPU frameworks for large-scale data processing, machine learning, simulation, and analytics workloads.
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Scaling GPU applications across servers and clusters while managing communication overhead and resource utilization.
Module 12: Cloud GPU Infrastructure And Accelerated Analytics Platforms
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Cloud GPU architectures, instance selection, accelerator types, storage integration, networking, and workload deployment models.
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Designing scalable GPU analytics environments using elastic cloud resources and managed data processing platforms.
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GPU cost optimization through workload scheduling, utilization monitoring, resource right-sizing, and demand management.
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Comparing on-premises, cloud, hybrid, and multi-cloud strategies for GPU-intensive analytical workloads.
Module 13: GPU Security, Governance And Operational Management
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Security considerations for GPU-enabled infrastructure, applications, data pipelines, containers, and shared computing environments.
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Access control, isolation, encryption, workload separation, and secure management of GPU-enabled analytical platforms.
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Governance of GPU resources including allocation policies, usage monitoring, compliance, accountability, and operational controls.
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Managing data privacy and protection requirements when processing sensitive information through accelerated analytics systems.
Module 14: GPU Containers, APIs And Production Deployment
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Containerizing GPU-enabled analytics applications and configuring appropriate accelerator access within deployment environments.
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GPU-compatible APIs, libraries, drivers, runtime dependencies, and environment-management considerations for production systems.
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Deploying GPU workloads through orchestration platforms and managing scheduling, scaling, availability, and resource allocation.
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Production readiness practices covering testing, observability, dependency management, rollback strategies, and lifecycle maintenance.
Module 15: GPU Infrastructure Optimization And Sustainability
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GPU capacity planning based on workload demand, memory requirements, concurrency, utilization patterns, and performance targets.
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Total cost of ownership analysis covering hardware, cloud resources, licensing, power, cooling, maintenance, and operational staffing.
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Energy-efficient GPU computing, workload scheduling, utilization optimization, and sustainable high-performance analytics practices.
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Developing business and technical cases for GPU investment based on measurable performance and operational requirements.
Module 16: Emerging Trends And Future Of GPU-Accelerated Analytics
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Generative AI infrastructure, large language models, foundation models, and evolving GPU requirements for advanced analytics.
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Next-generation accelerators, heterogeneous computing, specialized AI processors, and emerging alternatives to conventional GPU architectures.
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Edge GPU analytics, GPU-enabled digital twins, autonomous systems, and distributed intelligence across connected environments.
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Future developments in GPU cloud platforms, confidential computing, accelerated databases, federated workloads, and intelligent resource orchestration.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
