Training : Programming for GPUs

Optimize Applications for AMD GPUs

Programming for GPUs

Optimize Applications for AMD GPUs

Empowering Developers to Optimize HPC Applications on AMD Instinct™ GPUs

As High-Performance Computing (HPC) continues to drive breakthroughs in fields like scientific research, engineering, and AI, optimizing applications for advanced hardware is critical to unlocking maximum performance. AMD Instinct™ GPUs are specifically designed to accelerate HPC workloads, offering cutting-edge performance, scalability, and efficiency for a wide range of computational tasks.

At Qvelo, we provide comprehensive training programs to help developers, researchers, and engineers optimize their HPC applications on AMD Instinct™ GPUs. Our training delivers the knowledge and practical skills needed to harness the full power of AMD’s GPU architecture, enabling faster simulations, more efficient data processing, and smoother scalability across multiple GPUs.

Why Optimize for AMD Instinct™ GPUs?

AMD Instinct™ GPUs are engineered to deliver exceptional performance for the most demanding HPC and AI workloads. These GPUs leverage high-bandwidth memory (HBM), ROCm (Radeon Open Compute), and multi-GPU scalability to enhance computational efficiency.

By optimizing applications for AMD Instinct™, developers can achieve:

 

  • Breakthrough Performance: Take advantage of AMD Instinct™ GPUs’ massive parallelism, high memory bandwidth, and advanced architectures to accelerate large-scale simulations and data processing.
  • Scalability: Scale your applications seamlessly across single and multiple GPUs, maximizing resource utilization and computational throughput.
  • Open Software Ecosystem: AMD’s open-source ROCm platform allows developers to build, optimize, and deploy HPC applications with greater flexibility and control over system architecture.

Key Topics Covered

1. AMD Instinct™ GPU Architecture Overview

Understanding the architecture of AMD Instinct™ GPUs is critical to optimizing HPC applications for this platform. We begin by covering:

  • Compute Units and Stream Processors: Learn about the core compute units, stream processors, and how AMD Instinct™ GPUs handle parallelism at scale.
  • High-Bandwidth Memory (HBM): Discover how HBM on AMD Instinct™ GPUs reduces memory latency and boosts data throughput for large workloads.
  • Infinity Fabric™ Link: Understanding inter-GPU communication using Infinity Fabric™ technology, which allows for efficient scaling across multiple GPUs.
2. Introduction to the ROCm Software Ecosystem

The ROCm (Radeon Open Compute) platform is the foundation for developing, optimizing, and deploying HPC and AI applications on AMD hardware. In this section, we cover:

  • ROCm Toolchain: A detailed overview of the ROCm toolchain, including libraries, compilers, and runtime tools designed to optimize applications for AMD Instinct™ GPUs.
  • HIP (Heterogeneous-Compute Interface for Portability): Learn how to use HIP, AMD’s portable C++ language, to write GPU-accelerated code that runs on both AMD and NVIDIA hardware with minimal code changes.
  • OpenMP and OpenCL: How to leverage these parallel programming models for efficient multi-GPU execution on AMD hardware.
3. Workload Optimization Strategies

Optimizing applications for AMD Instinct™ GPUs requires an understanding of workload distribution, memory management, and parallelism. This module covers:

  • Data Parallelism and Task Parallelism: Techniques for distributing work efficiently across GPU compute units to ensure balanced workloads.
  • Memory Management Best Practices: Optimizing memory access patterns to minimize data movement between host and device and fully utilize HBM’s high bandwidth.
  • Latency Reduction: Strategies to reduce execution latency by overlapping computation and communication, maximizing throughput across multiple GPUs.
4. Multi-GPU Programming and Scaling

AMD Instinct™ GPUs are designed to scale across multiple devices, making them ideal for large-scale HPC deployments. In this section, we explore:

  • Scaling Applications Across Multiple GPUs: Techniques for splitting large workloads across multiple AMD Instinct™ GPUs, ensuring efficient task scheduling and load balancing.
  • Inter-GPU Communication with Infinity Fabric™: How to utilize Infinity Fabric™ for low-latency communication between GPUs, ensuring minimal overhead during large-scale computations.
  • Distributed Computing Techniques: Using ROCm and HIP to enable distributed computing across clusters of AMD Instinct™ GPUs, allowing for scalable, high-performance execution in HPC environments.
5. Using AMD's Optimized Libraries

AMD provides a suite of optimized libraries specifically designed for HPC and AI workloads on Instinct™ GPUs. In this module, we cover:

  • rocBLAS and rocFFT: How to accelerate linear algebra and Fourier Transform operations using AMD’s highly optimized rocBLAS and rocFFT libraries.
  • rocSPARSE and rocSOLVER: Using these libraries to optimize sparse matrix computations and numerical solvers for scientific computing applications.
  • MIOpen for AI Workloads: Leveraging the MIOpen library for machine learning and AI workloads, enabling optimized deep learning training and inference on AMD Instinct™ GPUs.
6. Performance Tuning and Profiling

Optimizing performance on AMD Instinct™ GPUs requires fine-tuning and identifying bottlenecks. This section focuses on:

  • AMD’s ROCm Profiler and Metrics: How to use ROCm tools to profile your application, analyze performance bottlenecks, and optimize kernel execution.
  • Memory and Kernel Optimization: Techniques for optimizing kernel launches and memory transfers to minimize overhead and maximize GPU utilization.
  • Performance Metrics and Debugging: Understanding key performance metrics and how to debug common issues related to memory access, kernel inefficiency, and inter-GPU communication.
7. Real-World Case Studies and Examples

We provide practical, real-world case studies that demonstrate the performance benefits of optimizing HPC applications on AMD Instinct™ GPUs. Examples include:

  • Scientific Simulations: Learn how AMD Instinct™ GPUs accelerate climate modeling, fluid dynamics, and particle physics simulations.
  • AI and Machine Learning Workloads: Case studies showing how AMD Instinct™ GPUs reduce training time and improve accuracy for deep learning models.
  • Financial and Engineering Applications: Examples of how financial simulations and computational engineering tasks have been significantly accelerated using AMD Instinct™ hardware.

Hands-On Labs and Practical Exercises

Our training includes hands-on labs where participants will develop, optimize, and run HPC applications on AMD Instinct™ GPUs. These labs provide practical experience with AMD’s ROCm platform, HIP programming, and advanced optimization techniques to help you master the nuances of GPU-accelerated computing on AMD hardware

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Who Should Attend?

This training is ideal for:

  • HPC Developers and Engineers looking to optimize their applications for AMD Instinct™ GPUs.
  • AI and Machine Learning Developers interested in accelerating deep learning workloads on AMD hardware.
  • Data Scientists and Researchers working on large-scale simulations and data processing tasks.
  • Software Developers who want to leverage the open-source ROCm platform for multi-GPU programming.
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Prerequisites

Participants should have:

    • Basic experience with GPU programming (CUDA, HIP, or OpenCL).
    • Proficiency in C++ programming.
    • Familiarity with HPC application development and parallel computing is recommended but not mandatory.

Maximize Performance with AMD Instinct™ GPUs

Our AMD Instinct™ GPU Optimization Training provides your team with the knowledge and practical skills to accelerate HPC workloads, enabling faster results and more efficient resource utilization. By optimizing applications for AMD’s powerful GPU architecture, you can unlock new levels of performance and scalability for even the most demanding computational challenges.

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