High-Performance GPU Computing for AI and Deep Learning

As Artificial Intelligence (AI) models grow in complexity and scale, high-performance GPU computing has become the backbone of modern AI infrastructure. Enterprises deploying deep learning systems require engineers and data scientists who can efficiently utilize GPU resources to accelerate training, optimize inference, and reduce operational costs. This 7-day corporate training program provides hands-on expertise in GPU programming, parallel computing, and deep learning optimization using NVIDIA CUDA, cuDNN, and TensorRT. Participants will gain the skills to design, optimize, and deploy AI workloads that achieve maximum performance and scalability across enterprise environments.

Objectives of the Training

Understand the architecture and parallel processing principles behind GPUs.
Learn how to accelerate deep learning model training and inference using CUDA and cuDNN.
Master optimization techniques for AI workloads with TensorRT and mixed precision training.
Gain experience with profiling, debugging, and performance tuning GPU-based applications.
Learn to integrate GPU acceleration into enterprise AI pipelines and distributed systems.

Prerequisites

Intermediate programming experience in Python or C++.
Basic understanding of deep learning frameworks (TensorFlow, PyTorch).
Familiarity with linear algebra, matrix operations, and neural network concepts.
Exposure to Linux and command-line environments.

What You Will Learn

GPU architecture and CUDA programming fundamentals.
Parallel computing concepts: threads, kernels, and memory hierarchies.
Accelerating deep learning training using cuDNN and mixed precision.
Model optimization and inference acceleration using TensorRT.
Profiling, benchmarking, and tuning GPU performance.
Integrating GPU-accelerated workflows into enterprise ML pipelines.

Target Audience

This training is designed for AI Engineers, ML Developers, Data Scientists, and System Architects who want to master GPU computing for optimizing AI workloads. It is also valuable for Infrastructure Engineers and Technical Leaders responsible for scaling and maintaining enterprise AI environments.

Detailed 7-Day Curriculum

Day 1 – Introduction to GPU Computing and CUDA Ecosystem (6 Hours)

Session 1: Evolution of GPU Computing – From Graphics to AI Acceleration.
Session 2: NVIDIA GPU Architecture – Streaming Multiprocessors, Cores, and Memory Hierarchies.
Session 3: CUDA Programming Fundamentals – Threads, Blocks, and Kernels.
Hands-on: Writing and Running Your First CUDA Program.

Day 2 – CUDA Programming and Parallelization Techniques (6 Hours)

Session 1: Deep Dive into CUDA Memory Management – Shared, Global, and Constant Memory.
Session 2: Thread Synchronization, Streams, and Asynchronous Execution.
Session 3: Debugging and Profiling CUDA Applications using Nsight Systems.
Hands-on: Parallel Matrix Multiplication and Reduction Operations.

Day 3 – Deep Learning Acceleration with cuDNN (6 Hours)

Session 1: Overview of NVIDIA cuDNN Library and Its Integration with Deep Learning Frameworks.
Session 2: Using cuDNN for Convolutional, Recurrent, and Transformer Models.
Session 3: Mixed Precision Training with Tensor Cores for Speed and Efficiency.
Case Study: Training Deep Neural Networks on GPU vs CPU – Performance Comparison.

Day 4 – TensorRT for Model Optimization and Inference Acceleration (6 Hours)

Session 1: Introduction to TensorRT – Concepts and Workflow.
Session 2: Model Conversion, Quantization, and Precision Optimization.
Session 3: Integrating TensorRT with TensorFlow and PyTorch Models.
Hands-on: Accelerating Inference for Image Classification and Object Detection Models.

Day 5 – Distributed GPU Computing and Multi-GPU Training (6 Hours)

Session 1: Multi-GPU Scaling – Data Parallelism and Model Parallelism Strategies.
Session 2: Using NCCL, Horovod, and DeepSpeed for Distributed Training.
Session 3: Checkpointing, Gradient Accumulation, and Memory Optimization Techniques.
Workshop: Training a Deep Learning Model on a Multi-GPU Cluster.

Day 6 – Profiling, Performance Tuning, and Cloud Integration (6 Hours)

Session 1: Performance Analysis with NVIDIA Nsight Compute and TensorBoard.
Session 2: Identifying Bottlenecks and Optimizing GPU Utilization.
Session 3: Deploying GPU-Accelerated Workloads on AWS, Azure, and GCP.
Hands-on: Benchmarking Inference Latency and Throughput.

Day 7 – Capstone Project & Future of GPU-Accelerated AI (6 Hours)

Session 1: Capstone Project Development – Designing a GPU-Optimized AI Solution.
Session 2: Group Presentations and Performance Analysis Discussion.
Session 3: Future Trends – Energy Efficiency, AI Accelerators, and Edge GPU Computing.
Panel Discussion: Building Sustainable AI Infrastructure for the Future.

Capstone Project

Participants will design and implement a GPU-accelerated deep learning system for a real-world application. Example projects include optimizing an image classification pipeline, accelerating NLP model inference, or training a large model across multiple GPUs. The project will focus on performance benchmarking, optimization, and deployment strategies in a cloud or on-premise GPU cluster.

Future Trends in High-Performance GPU Computing

The future of GPU computing lies in intelligent resource orchestration, energy-efficient architectures, and domain-specific accelerators. Enterprises are moving toward heterogeneous computing environments that blend GPUs, TPUs, and specialized AI chips for optimized performance. Emerging trends include edge AI acceleration, zero-copy memory transfers, and federated training across GPU clusters. Organizations that master GPU performance tuning will lead the AI revolution with scalable, cost-efficient, and environmentally responsible AI systems.