This layer is used with Arm GPU tooling that shows the scheduling of workloads on to the GPU hardware queue. The layer provides additional semantic annotation, extending the scheduling data from the Android Perfetto render stages telemetry with useful API-aware context.
Most properties we track are a property of the command buffer recording in isolation. However, the user debug label stack is a property of the queue and persists across submits. We therefore only determine the debug label associated with a workload in the command stream at submit time, and must resolve it per workload inside the command buffer.
To support this we implement a software command stream that contains simple bytecode actions that represent the sequence of debug label and workload commands inside each command buffer. This "command stream" is played to update the queue state at submit time, triggering metadata submission for each workload that snapshots the current state of the user debug label stack at that point in the command stream.
The protocol between the layer and the host tools uses Google Protocol Buffers to implement the message encoding.
The layer implementation uses Protopuf, a light-weight implementation which
is easily integrated in to the layer. Protopuf message definitions are
defined directly in the C++ code (see timeline_protobuf_encoder.cpp) and do
not use the timeline.proto definitions.
The host implementation uses the Google protoc compiler to generate native
bindings from the timeline.proto definition. When updating the protocol
buffers you must ensure that the C++ and proto definitions match.
To regenerate the Python bindings, found in lglpy/timeline/protos, run the
following command from the layer_gpu_timeline directory:
protoc ./timeline.proto --python_out=../lglpy/timeline/protos/layer_driver/Copyright © 2024-2025, Arm Limited and contributors.