High-Performance Computing with CUDA & Tensor Cores

This course provides an in-depth exploration of high-performance computing (HPC) using CUDA and Tensor Cores, focusing on the principles and practices of parallel programming on NVIDIA GPUs. Participants will gain a comprehensive understanding of GPU architecture, CUDA programming model, and techniques for optimizing code for maximum performance. The course emphasizes hands-on experience through practical exercises and real-world case studies, enabling students to develop efficient and scalable solutions for computationally intensive problems.

Course Overview

This course offers a detailed examination of CUDA programming and the utilization of Tensor Cores for accelerating deep learning and other compute-intensive applications. It covers topics ranging from fundamental CUDA concepts to advanced optimization strategies. The curriculum is designed to equip participants with the knowledge and skills necessary to leverage the power of GPUs for solving complex scientific, engineering, and data science challenges.

Key Learning Objectives

• Understand the architecture of NVIDIA GPUs and their role in high-performance computing.
• Master the CUDA programming model and its core concepts, including kernels, threads, blocks, and grids.
• Write, compile, and execute CUDA programs for various computational tasks.
• Optimize CUDA code for maximum performance using techniques such as memory optimization, thread synchronization, and kernel fusion.
• Explore the capabilities of Tensor Cores for accelerating matrix multiplication and deep learning operations.
• Apply CUDA and Tensor Cores to solve real-world problems in scientific computing, data analysis, and machine learning.

Course Content

Introduction to High-Performance Computing

• Overview of high-performance computing concepts and architectures.
• Introduction to parallel computing and its benefits.
• The role of GPUs in accelerating scientific and engineering applications.
• History and evolution of GPU computing.

GPU Architecture

• Detailed exploration of NVIDIA GPU architecture.
• Understanding Streaming Multiprocessors (SMs) and their components.
• Memory hierarchy of GPUs: global memory, shared memory, registers, and constant memory.
• GPU execution model and its implications for performance.

CUDA Programming Model

• Introduction to the CUDA programming model.
• CUDA kernels, threads, blocks, and grids.
• Writing and launching CUDA kernels.
• Memory management in CUDA.
• Error handling and debugging in CUDA programs.

CUDA Memory Management

• Understanding different types of memory in CUDA: global, shared, constant, and texture memory.
• Techniques for optimizing memory access patterns.
• Using shared memory for inter-thread communication and data reuse.
• Strategies for minimizing memory transfers between host and device.

Thread Synchronization

• Importance of thread synchronization in parallel programming.
• Using synchronization primitives in CUDA: __syncthreads(), atomic operations.
• Avoiding race conditions and data hazards.
• Implementing efficient synchronization patterns.

Performance Optimization Techniques

• Identifying performance bottlenecks in CUDA code.
• Techniques for optimizing kernel launch parameters.
• Loop unrolling, loop tiling, and other loop optimization strategies.
• Instruction-level parallelism and vectorization.
• Using profilers and performance analysis tools to identify areas for improvement.

CUDA Libraries

• Introduction to commonly used CUDA libraries, such as cuBLAS, cuFFT, and cuSPARSE.
• Using CUDA libraries for linear algebra, signal processing, and sparse matrix computations.
• Integrating CUDA libraries into custom CUDA programs.

Introduction to Tensor Cores

• Understanding the architecture and capabilities of Tensor Cores.
• Using Tensor Cores for accelerating matrix multiplication and deep learning operations.
• Programming with Tensor Cores using NVIDIA's libraries.
• Performance considerations when using Tensor Cores.

Advanced CUDA Topics

• Multi-GPU programming and scaling.
• Asynchronous operations and data transfers.
• CUDA streams and events.
• Interoperability with other programming languages and libraries.

Real-World Applications

• Case studies of using CUDA and Tensor Cores in scientific computing, data analysis, and machine learning.
• Examples of solving real-world problems using GPU acceleration.
• Best practices for developing high-performance GPU-accelerated applications.

Benefits of Taking This Course

• Gain a competitive edge in the rapidly growing field of high-performance computing.
• Develop in-demand skills in CUDA programming and GPU acceleration.
• Enhance your ability to solve complex computational problems using parallel computing techniques.
• Become proficient in using NVIDIA GPUs and Tensor Cores for various applications.
• Improve your career prospects in industries such as scientific research, engineering, data science, and machine learning.

Target Audience

This course is designed for students, researchers, and professionals who have a basic understanding of programming and are interested in learning how to leverage the power of GPUs for high-performance computing. The course is suitable for individuals with backgrounds in computer science, engineering, mathematics, physics, and related fields.

Prerequisites

Participants should have a basic understanding of programming concepts, such as variables, data types, control flow, and functions. Familiarity with C or C++ is recommended, but not required. No prior experience with CUDA or GPU programming is necessary.

Course Values

• Emphasis on practical application and hands-on experience.
• Focus on real-world problem-solving and case studies.
• Comprehensive coverage of CUDA programming and Tensor Core utilization.
• Up-to-date content reflecting the latest advancements in GPU technology.
• Preparation for a career in high-performance computing and related fields.