#Unreal engine rebuild lighting code#

In GPU Visualizer, we then identify an expensive dynamic light source by name. We see both Shadow Depths and Lights->ShadowedLights costing us frame time. We can choose to remove or optimize this mesh to balance time taken for opaque and translucent draws. In GPU Visualizer, we then find a translucent mesh that takes nearly 1ms.

We see Translucency being slower than BasePassД«y 1 ms. The stat commands are great for a real-time view of performance, but suppose you find a GPU bottleneck in your scene and wish to dig deeper into a single-frame capture.Д¬ommand allows you expand one frameвЂ™s GPU work in the GPU Visualizer, useful for cases that require detailed info from the engine. ue4statsДЇile, which can be opened in Unreal Frontend: : Dumps all the real-time stat data within the start/stop duration to a. : Shows memory counters, useful for debugging memory pressure scenarios.

#Unreal engine rebuild lighting code#

Developers with UE4 source code may zoom in on specific GPU work with the SCOPED_GPU_STAT You may have to set r.GPUStatsEnabled 1ДЇor this to work.

Useful for shader iteration and optimization. Examples: dynamic lights, translucency cost, draw call count, etc. : Good for identifying bottlenecks in the overall UE4 rendering pipeline. Useful for detecting hitches in otherwise smooth gameplay. : Shows the вЂstat unitвЂ™ data with a real-time line graph plot.

DynRes: Shows the ratio of primary to secondary screen percentage, separately for viewport width and height (if dynamic resolution is enabled).

RHIT: RHI thread time, should be just under the current frame time.

Game: C++ or Blueprint gameplay operation.

Frame: Total time to finish each frame, similar to ms per frame.

: Unobtrusive view of frames per second (FPS) and ms per frame. The most important commands pruned from the above list: These can serve as a supplement to profiling with RGP.ДЄ list of all stat commands is officially documented here: This section covers the built-in UE4 profiling tools. Next, we can enable our optimization to see the performance impact: r. 1ДЄfter taking another performance capture with RDP and going back to the Event Timings view in RGP: We did this by switching to a compute shader and leveraging LDS (local data store/groupshared memory) вЂ“ a hardware feature available on modern GPUs which support Shader Model 5.

) for this shader shows that there is a lengthy loop that we need to parallelize if we want to maximize the GPU hardware and eliminate any partial waves. The ISA view is also useful for other optimizations like scalarization which are not covered here ( ) The ISA tab will give us the exact shader instructions that are executed on GPU hardware as well as VGPR/SGPR occupancy. On GCN GPUs and above, this kind of GPU workload will execute in вЂpartial wavesвЂ™ which means the GPU is being underutilized. The Information tab shows that our pixel shader is only running 1 wavefront and only taking up 32 threads of that wavefront. To inspect the details of the pixel shader running on the GPU, right-click on the draw call, select вЂњView in Pipeline StateвЂќ and click on PS in the pipeline. We can see that the DrawIndexedInstanced()Д¬all takes 211us to complete. Many third-party tools exist, but the Radeon Developer Panel that comes with the Radeon GPU Profiler has a Device Clocks tab under Applications which can be used to set a stable clock on AMD RDNAв„ў GPUs, as shown below: You may fix the clocks on your GPU to reduce this variance. But this trades lower power consumption for performance and can introduce noise in our benchmarks, as the clocks may not scale the same way between runs of our application. Most GPUs have a default power management system that switches to a lower clock frequency when idle to save power. This can be useful in gathering repeatable average frame time data for your level.ДЄnother technique for helping reduce noise in profile results is to run with fixed clocks. It will then shutdown automatically after a fixed number of frames. This means that, if you have your project set up to run a camera flythrough on startup, it will advance through the flythrough using fixed timesteps and a fixed random seed. Rather, it runs 211Г—60=12,660 frames using a fixed timestep of 1/60=16.67 milliseconds. In the above example, benchmarkseconds is not wall-clock seconds (unless every frame of the demo runs at exactly 60 fps).