Learning Objectives

4.1. Learning Objectives#

This chapter introduces students to GPU offloading with OpenMP, focusing on device constructs, memory management, parallel execution, and performance optimization. By the end of this chapter, students will be able to:

Remember & Understand

  • Describe the role and architectural characteristics of GPU accelerators in modern parallel computing.

  • Explain OpenMP’s device constructs (target, teams, distribute, etc.) and their purpose in offloading computation to GPUs.

  • Understand the concepts of data mapping and the use of map clauses for host-device memory transfer.

  • Recognize how OpenMP supports asynchronous execution and explicit memory management on devices.

Apply

  • Write OpenMP code using target, teams, and distribute to implement GPU offloading for compute-intensive kernels.

  • Use map, target data, and target update clauses to control data movement between host and device memory.

  • Implement asynchronous execution using the nowait clause and task dependencies to overlap computation and communication.

  • Allocate and deallocate GPU memory using OpenMP runtime functions.

  • Apply best practices for parallel execution and synchronization on GPU devices.

Analyze

  • Analyze the impact of different memory mapping strategies on data locality and device performance.

  • Compare different parallel constructs (teams, distribute, parallel for) in terms of their execution behavior and applicability.

  • Investigate the effects of loop scheduling and thread distribution on GPU workload balancing.

Evaluate

  • Evaluate the performance benefits and trade-offs of GPU offloading for a given problem.

  • Assess the correctness and efficiency of data transfers and asynchronous execution mechanisms.

  • Critique the effectiveness of tuning strategies for memory usage, thread configuration, and offload scope.

Create

  • Design and implement optimized GPU-offloaded applications using a combination of OpenMP directives and memory management techniques.

  • Develop high-performance OpenMP programs that utilize advanced features such as dependency management, asynchronous execution, and architecture-specific tuning.