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What to measure before opening a profiler
Decide which part of the workload you want to improve, and keep its boundaries explicit. A single end-to-end number can hide whether time is spent loading a model, executing it, waiting for a GPU result, transferring data, generating a texture, or rendering the result.
- Model load: time from the start of loading until the model is ready to run.
- Warm-up: record separately from steady-state runs; state how many warm-up iterations you use.
- Inference: time the model execution itself. If execution is asynchronous, record the submission and the point at which the result is actually available.
- Synchronization and readback: measure GPU-to-CPU waits and data transfers separately where applicable. A timer around only command submission may not capture the time until the result can be consumed.
- Texture generation and upload: time generation and transfer as distinct phases when the implementation allows it. Note whether generation runs on the CPU, GPU, or across a transfer boundary.
- Presentation or rendering: measure separately if the generated texture is subsequently displayed or used in another rendering pass.
For every comparison, record the app build, device and GPU/SoC, Android version, driver, model, input and output dimensions, input content, precision, warm-up policy, repeat count, and thermal and power conditions. Fix the workload and change one factor at a time; otherwise, a faster trace may reflect a different input or device state rather than an optimization.
Choose the profiler for the question
System profiling and frame profiling answer different questions. Use both when you need to connect a particular command or resource to behavior across time.
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| Tool or capture | Best suited to | What it can show | Important qualification |
|---|---|---|---|
| Android Performance Analyzer (APA) System Profiler | System behavior across a workload | CPU, GPU, memory, power, and interaction with system behavior | Google’s May 19, 2026 announcement described the System Profiler as open beta. The announcement said Android 12 or later provides the best experience for system-wide performance, GPU counters, and render stages; confirm current availability and device support. |
| Android GPU Inspector (AGI) system profiling | Cross-frame behavior and CPU/GPU/API correlation | App trace markers, CPU and process scheduling, GPU counters, activity and lifecycle, Vulkan API traces, memory, and battery data | Specifying the app is recommended. AGI documentation says a trace without the app specified lacks that application’s ATrace markers and GPU activity. |
| AGI frame profiling | Inspection of one frame or a selected workload segment | Vulkan calls, framebuffer content, draw calls, RAM and GPU memory values, GPU rendering events, pipeline and render state, textures, and shaders | AGI traces Vulkan directly. For OpenGL ES, AGI uses a custom ANGLE build to translate commands into Vulkan for tracing, so select the capture API that matches the app. |
| Vendor-specific profiler | GPU-vendor-specific counters or shader investigation | Capabilities depend on the profiler, GPU, driver, and device support | The Vulkan Documentation Project tutorial lists Arm Performance Studio for Mali/Immortalis, Qualcomm Snapdragon Profiler for Adreno, and Imagination PVRTune for Imagination GPUs. Verify current vendor requirements and supported hardware. |
APA’s May 2026 announcement described it as Google’s new Android profiler, available as a standalone desktop app and through the updated Android Studio System Trace viewer in Panda 4 Canary builds and later, for Windows, macOS, and Linux. That announcement does not establish the beta or download status today. It also says trace rendering is “typically 6x to 26x faster than Android GPU Inspector.” This is Google’s claim about rendering a trace, not inference speed; the cited announcement passage does not give benchmark methodology.
Prepare an Android development build
- Connect the target device: connect the Android device to the computer with a USB data cable and configure adb. Match both cable ends to the host and device, and make sure the cable supports data rather than charging only.
- Use a debuggable app: the AGI quickstart requires a debuggable app. For a Vulkan app, it also requires Vulkan validation layers to be enabled and advises fixing validation warnings and errors before profiling.
- Keep instrumentation appropriate to development: Android’s Vulkan implementation documentation explains that development-time validation and profiling layers are not intended for production system images. Layer loading depends on debug status and Android configuration, so do not assume the same capture workflow works on a shipping, non-debuggable process.
- Choose the capture API deliberately: select Vulkan for an app that uses Vulkan directly. If tracing OpenGL ES through AGI, account for its custom ANGLE translation path rather than treating the capture as a direct Vulkan application trace.
- Stabilize the test conditions: close unrelated work where practical, let the device return to a comparable thermal state between runs, and note battery or power conditions. Keep the same build and workload for each before-and-after comparison.
Capture system behavior first
Start with APA System Profiler or AGI system profiling to see whether the workload is limited by CPU scheduling, GPU execution, memory behavior, power conditions, or API submission overhead. Use a representative run that includes the app-side phases you timed, not just an isolated launch or a convenient idle frame.
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- In AGI, specify the app when possible so its trace markers and GPU activity are present.
- Inspect CPU scheduling alongside Vulkan API-call durations. AGI’s Vulkan event track reports function-call duration, which helps identify CPU-side API overhead; it is not a direct measurement of GPU execution time.
- Correlate GPU activity and available counters with app timings. A counter viewed by itself does not establish the cause of a delay.
- Use memory and battery/power data to look for pressure or changing device conditions that coincide with slower runs.
- Capture repeated runs under the same conditions. Keep trace duration and capture overhead in mind when comparing results, and do not compare unlike capture modes as though they were equivalent.
There is no source-supported universal latency target or counter threshold for Android Vulkan inference. Interpret counters in the context of the device, workload, and the app timing markers around the phase in question.
Capture and inspect the relevant frame or segment
Once the system trace suggests where to look, take an AGI frame capture around the relevant work. Use manual triggering or schedule the capture so it covers the representative commands, rather than assuming an arbitrary frame contains the expensive operation.
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- Choose the Vulkan capture API for a Vulkan workload.
- Trigger the capture during the inference, texture generation, transfer, or rendering segment you want to inspect.
- Inspect the Vulkan commands and draw calls alongside framebuffer content, textures and shaders, memory values, GPU rendering events, and pipeline/render state.
- Use the app’s timestamps to connect a resource or command sequence to a measured phase. The frame capture exposes graphics work; it does not supply the application’s model-level latency or validate the numerical correctness of model outputs.
A frame capture is useful for detailed command and resource inspection, while the system trace supplies the broader scheduling and cross-frame context. A single frame should not stand in for a sustained or multi-frame workload.
Investigate texture generation and transfers
For texture work, find which commands, texture or shader resources, pipeline state, and GPU events coincide with the app-timed generation and upload phases. If generation happens on the CPU or requires GPU-to-CPU readback, inspect that boundary explicitly; the term “texture generation” does not by itself mean the model performed the work.
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For sustained behavior, compare the frame-level observations with system-profile memory and GPU data. The Vulkan Documentation Project tutorial suggests comparing measured external memory traffic with a kernel’s theoretical minimum input-plus-output traffic to investigate redundant movement. Its example that traffic three to four times that minimum is worth investigating is tutorial guidance, not a universal acceptance threshold for every device or workload.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Repeat the experiment and assess changes
- Run on actual target hardware. The Vulkan Documentation Project tutorial warns, “Emulators and desktop GPUs will lie to you about mobile performance.” Treat this as a reason to validate on the real device, not as proof that every emulator measurement is useless.
- Repeat across representative devices and drivers. Android GPU behavior and available counters vary by hardware and driver; a result on one device does not establish a result for every Android device.
- Change one factor at a time. Keep the app build and workload fixed for a before-and-after capture, then compare the app timings with CPU scheduling, API duration, GPU activity, memory, and relevant frame events.
- Validate output quality independently. If you change precision or another numerical setting, compare output quality as well as latency. A faster GPU trace does not prove that model results remain acceptable.
The Vulkan Documentation Project tutorial says many modern mobile GPUs execute FP16 at twice the rate of FP32 while moving half as many bytes, and characterizes reduced precision as “often a near-free 2x” for workloads that tolerate it. This is a conditional generalization, not a guaranteed inference speedup: actual results depend on hardware, kernel implementation, and model, and precision changes require a separate quality check.
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Published case studies are not portable performance promises. In its May 2026 announcement, Google reported that The Forge saw about a 50% reduction in CPU setup cost after batching vkCmdBindDescriptorSets, and that Netmarble reduced GPU cost by up to 90% for some scenes after shader precision and upscaling work in a named game. Those reported outcomes are specific to those projects, not general expectations for another app’s Vulkan inference or texture generation.
What a defensible result should report
Share enough detail for another engineer to reproduce the comparison and understand its limits:
- Device model, GPU/SoC, Android version, driver, profiler and capture mode.
- App build, model and input/output dimensions, precision, and whether the workload was CPU-, GPU-, or transfer-based.
- What each timer includes, including warm-up and synchronization/readback boundaries.
- Thermal and power conditions, repeat count, and how repeated values were summarized.
- The before-and-after change, app-level timings, and the trace evidence that supports the interpretation.
- Any output-quality check for numerical changes, plus device or workload conditions that limit the conclusion.
Tool selection has no universal winner across Android hardware. Use APA’s system profiler for broad system analysis where supported, AGI frame profiling for Vulkan command and resource inspection, and a vendor profiler when the target GPU’s specific counters or shader detail warrant it. Confirm current tool support and capture requirements for the device before relying on a result.
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