13 GPU profiling and tools

In this chapter, we will cover the tools and the different workflows that you can use to accelerate your application development. We show you how profiling tools for the GPU can be helpful. Additionally, we discuss how to deal with the challenges of using profiling tools when working on a remote HPC cluster.

The profiling tools continue to change and improve, so we’ll focus on the methodology rather than the details of any one tool. The main takeaway of this chapter will be understanding how to create a productive workflow when using the powerful GPU profiling tools.

13.1  Profiling tools overview

13.2  How to select a good workflow

13.3  Example problem: shallow water simulation

13.4  A sample of a profiling workflow

13.4.1    Run the shallow water application

13.4.2    Profile the CPU code to develop a plan of action

13.4.3    Add OpenACC compute directives to begin the implementation step

13.4.4    Add data movement directives

13.4.5    Guided analysis can give you some suggested improvements

13.4.6    The Nvidia Nsight suite of tools can be a powerful development aid

13.4.7    Code XL for the AMD GPU ecosystem

13.5  Don’t get lost in the swamp: focus on the important metrics

13.5.1    Occupancy -- is there enough work?

13.5.2    Issue efficiency -- are your warps on break too often?

13.5.3    Achieved bandwidth -- it always comes down to bandwidth

13.6  Containers and Virtual Machines provide alternate workflows

13.6.1    Docker containers as a workaround

13.6.2    Virtual machines using Virtual Box

13.7  Cloud options: a flexible and portable capability

13.8  Further explorations

13.8.1    Additional reading