GPU

Perfetto supports tracing GPU activity across a range of use-cases, from Android mobile graphics to high-end multi-GPU compute workloads.

Data sources

The following data sources are available for GPU tracing:

Data Source	Config	Purpose
`gpu.counters`	gpu_counter_config.proto	Periodic or instrumented GPU counter sampling
`gpu.renderstages`	gpu_renderstages_config.proto	GPU render stage and compute activity timeline
`vulkan.memory_tracker`	vulkan_memory_config.proto	Vulkan memory allocation and bind tracking
`gpu.log`	(none)	GPU debug log messages
`linux.ftrace`	ftrace_config.proto	GPU frequency, memory totals, DRM scheduler events

GPU producers commonly register data sources with a hardware-specific suffix, e.g. gpu.counters.adreno or gpu.renderstages.mali. The tracing service uses exact name matching, so the trace config must use the same suffixed name. The trace processor parses GPU data based on proto field types, so all suffixed variants are handled identically. When targeting a specific GPU vendor's producer, use the suffixed name in your trace config:

data_sources: {
    config {
        name: "gpu.counters"
        gpu_counter_config {
            counter_period_ns: 1000000
            counter_ids: 1
        }
    }
}

Traces include a gpu_id field to distinguish between GPUs and a machine_id field to distinguish between machines in multi-machine setups. GPU hardware metadata (name, vendor, architecture, UUID, PCI BDF) is recorded via the GpuInfo trace packet.

Android

GPU frequency

GPU frequency is collected via ftrace:

data_sources: {
    config {
        name: "linux.ftrace"
        ftrace_config {
            ftrace_events: "power/gpu_frequency"
        }
    }
}

GPU counters

Android GPU producers must use counter descriptor mode 1: the GpuCounterDescriptor is embedded directly in the first GpuCounterEvent packet of the session, and counter IDs are global. This is required for CDD/CTS compliance.

GPU counters are sampled by specifying device-specific counter IDs. The available counter IDs are described in GpuCounterSpec in the data source descriptor.

data_sources: {
    config {
        name: "gpu.counters"
        gpu_counter_config {
            counter_period_ns: 1000000
            counter_ids: 1
            counter_ids: 3
            counter_ids: 106
            counter_ids: 107
            counter_ids: 109
        }
    }
}

counter_period_ns sets the desired sampling interval.

Alternatively, counters can be selected by name using counter_names. Use one or the other, not both. Not all producers support this — check supports_counter_names in the GpuCounterDescriptor data source descriptor. Glob patterns may be used in counter_names to match multiple counters by name; check supports_counter_name_globs in the descriptor for support.

GPU memory

Total GPU memory usage per process is collected via ftrace:

data_sources: {
    config {
        name: "linux.ftrace"
        ftrace_config {
            ftrace_events: "gpu_mem/gpu_mem_total"
        }
    }
}

GPU render stages

Render stage tracing provides a timeline of GPU activity (graphics and compute submissions):

data_sources: {
    config {
        name: "gpu.renderstages"
    }
}

Vulkan memory

Vulkan memory allocation and bind events can be tracked with:

data_sources: {
    config {
        name: "vulkan.memory_tracker"
        vulkan_memory_config {
            track_driver_memory_usage: true
            track_device_memory_usage: true
        }
    }
}

GPU log

GPU debug log messages can be collected by enabling the data source:

data_sources: {
    config {
        name: "gpu.log"
    }
}

High-end GPGPU

For high-performance and data-center GPU workloads (CUDA, OpenCL, HIP), Perfetto supports multi-GPU and multi-machine tracing with instrumented counter sampling.

Instrumented counter sampling

Instead of global sampling, counters can be sampled by instrumenting GPU command buffers. This provides per-submission counter values:

data_sources: {
    config {
        name: "gpu.counters"
        gpu_counter_config {
            counter_ids: 1
            counter_ids: 2
            instrumented_sampling: true
        }
    }
}

For more control over which GPU activities are instrumented, use instrumented_sampling_config instead of the instrumented_sampling bool. This enables a pipeline of filters applied in the following order:

Activity name filtering: If activity_name_filters is non-empty, the activity must match at least one filter. Each filter requires a name_glob pattern and an optional name_base (defaults to MANGLED_KERNEL_NAME if not specified). If empty, all activities pass this step.
TX range filtering: If activity_tx_include_globs is non-empty, the activity must fall within a TX range (e.g. NVTX range for CUDA) matching one of the include globs. Activities in TX ranges matching activity_tx_exclude_globs are excluded (excludes take precedence over includes). TX ranges can be nested, and an activity matches if any range in its nesting hierarchy matches. If both are empty, all activities pass this step.
Range-based sampling: If activity_ranges is non-empty, only activities within the specified skip/count ranges are instrumented. skip defaults to 0 and count defaults to UINT32_MAX (all remaining activities) when not specified. If empty, all activities that passed the previous steps are instrumented.

Example configuration that instruments only activities with demangled kernel names matching "myKernel*" within TX ranges matching "training*", skipping the first 10 matching activities and then instrumenting 5:

data_sources: {
    config {
        name: "gpu.counters"
        gpu_counter_config {
          counter_names: "sm__cycles_elapsed.avg"
          counter_names: "sm__cycles_active.avg"
          instrumented_sampling_config {
            activity_name_filters {
              name_glob: "myKernel*"
              name_base: DEMANGLED_KERNEL_NAME
            }
            activity_tx_include_globs: "training*"
            activity_ranges {
              skip: 10
              count: 5
            }
          }
        }
    }
}

Counter descriptor mode 2 is recommended for GPGPU use-cases: the producer emits an InternedGpuCounterDescriptor referenced by IID, giving each trusted sequence its own scoped counter IDs. This avoids the global coordination required by mode 1 and supports multiple producers and GPUs naturally. See gpu_counter_event.proto for details on both modes.

Counter names and IDs are advertised by the GPU producer via GpuCounterSpec in the data source descriptor, which includes measurement units and descriptions.

Counter groups

Counter groups are used by the Perfetto UI to organize counter tracks into groups. Counters can be assigned to built-in groups (SYSTEM, VERTICES, FRAGMENTS, PRIMITIVES, MEMORY, COMPUTE, RAY_TRACING) via GpuCounterSpec.groups. Producers can also define custom counter groups using the GpuCounterGroupSpec message in GpuCounterDescriptor:

message GpuCounterGroupSpec {
    optional uint32 group_id = 1;
    optional string name = 2;
    optional string description = 3;
    repeated uint32 counter_ids = 4;
}

Custom groups can also be used to provide display names and descriptions for the fixed GpuCounterGroup enum values (SYSTEM, VERTICES, etc.). To do this, set group_id to the enum value and provide a name and/or description.

A counter's group membership is the union of groups assigned via GpuCounterSpec.groups (the fixed enum) and GpuCounterGroupSpec.counter_ids (custom groups).

For example, with custom groups "Compute Core" and "L2 Cache":

GPU > Counters > Compute Core > Counter A
GPU > Counters > Compute Core > Counter B
GPU > Counters > L2 Cache > Counter C

Multi-GPU

Each GPU in the system is assigned a gpu_id. Counter events, render stages, and other GPU trace data carry this ID so the UI can group tracks per GPU. GPU hardware details are recorded via the GpuInfo message, which includes:

name, vendor, model, architecture
uuid (16-byte identifier)
pci_bdf (PCI bus/device/function)

Multi-machine

When tracing across multiple machines, each GPU trace event also carries a machine_id to distinguish which machine the GPU belongs to. The Perfetto UI displays machine labels alongside GPU tracks.

Render stage event correlation

GPU render stage events can declare dependencies on other render stage events using the event_wait_ids field on GpuRenderStageEvent. Each entry is the event_id of another render stage event that this event had to wait on before it could run. The trace processor uses these to create flow arrows between the correlated GPU slices.

Example: a matmul kernel that depends on a previous asynchronous memcpy:

gpu_render_stage_event {
    event_id: 1
    duration: 50000
    hw_queue_iid: 1
    stage_iid: 2
    context: 0
    name: "Memcpy HtoD"
}

gpu_render_stage_event {
    event_id: 2
    duration: 40000
    hw_queue_iid: 3
    stage_iid: 4
    context: 0
    name: "matmul_kernel"
    event_wait_ids: 1
}

This creates a flow from the memcpy event (event_id 1) to the matmul kernel (event_id 2), visualizing the dependency in the Perfetto UI.

Host-to-GPU correlation

Host-side track events can be correlated with GPU render stage events using the GpuCorrelation TrackEvent extension. This is useful for connecting host API calls (e.g. cudaLaunchKernel, cudaMemcpyAsync) with the corresponding GPU work.

The extension provides two fields:

render_stage_submission_event_ids: event IDs of GPU render stage events that this host event submitted.
render_stage_wait_event_ids: event IDs of GPU render stage events that this host event waited on to complete.

Example: a host kernel launch correlated with a GPU compute kernel:

track_event {
    type: TYPE_SLICE_BEGIN
    name: "cudaLaunchKernel"
    [perfetto.protos.GpuTrackEvent.gpu_correlation] {
        render_stage_submission_event_ids: 1
    }
}

gpu_render_stage_event {
    event_id: 1
    duration: 50000
    hw_queue_iid: 1
    stage_iid: 2
    context: 0
    name: "matmul_kernel"
}

UI plugins

The Perfetto UI ships several plugins that consume GPU trace data. They register tracks, groups, and detail panes under the standard GPU group in the workspace tree (and, for per-process plugins, under each process group).

dev.perfetto.Gpu

The base plugin that lays out a GPU group per GPU and populates it with the leaf and summary tracks for everything in the gpu_counter_track, gpu_render_stage, gpu_log, vulkan_events, and graphics_frame_event families. Multi-GPU and multi-machine traces are split into per-GPU sub-groups (with machine labels appended when more than one machine is present); custom counter groups declared in GpuCounterDescriptor / GpuCounterGroupSpec show up as collapsible sub-groups under Counters.

dev.perfetto.GpuByProcess

Surfaces GPU concepts that are scoped to a single process and don't have a meaningful global representation. A CUDA stream, for example, is a per-process handle: the same numeric stream ID in two different processes refers to two unrelated streams, so showing all streams under a single shared GPU group would be misleading. This plugin places those tracks under each owning process instead.

For traces whose GPU slices carry device and stream launch args (e.g. CUDA, HIP), it nests gpu_render_stage slices under each process as <API> → Device #N → Context #N → Stream #N, collapsing any level that only has a single value. Slices that don't carry those args fall back to one track per hw_queue_id, named after the source hardware-queue track (typically "Channel #N"). When a process spans multiple GPUs the leaf tracks are nested under per-GPU sub-groups.

com.meta.GpuCompute

Compute-kernel deep dive. Adds three tabs that are populated whenever a compute gpu_render_stage slice (i.e. gpu_slice.render_stage_category = COMPUTE) is selected:

Summary — table of every kernel launch in the trace, sortable by duration, occupancy, and other hardware metrics. Double-click jumps to the details view for that kernel.
Details — per-section metric tables (Speed-of-Light, Launch Statistics, Occupancy, Workload Analysis), with optional baseline comparison between two kernels.
Toolbar — kernel selector, baseline pin, terminology switch (CUDA / OpenCL / vendor-supplied), and automatic unit conversion (bytes → KB, ns → s, etc.).

The core plugin ships CUDA and AMD support; additional vendors are added by companion plugins that register terminologies, metric sections, well-known metric IDs, and analysis providers. See com.meta.GpuCompute/README.md for the extension API.

Example queries

Top 5 longest-running kernels with time-weighted utilization

This query ranks compute kernels by duration and, for each one, computes the time-weighted average of the GPU Utilization counter over the kernel's execution window. counter_leading_intervals turns the sparse counter samples into (ts, dur, value) intervals (each sample's value holds until the next sample), and _interval_intersect clips those intervals against each kernel's [ts, ts + dur) window so the average is weighted by how long each counter value was actually in effect during the kernel.

INCLUDE PERFETTO MODULE counters.intervals;
INCLUDE PERFETTO MODULE intervals.intersect;

WITH
  -- The GPU Utilization counter, expanded into (ts, dur, value) intervals.
  -- Carries ugpu so the intersect can match each kernel to its own GPU.
  utilization AS (
    SELECT u.id, u.ts, u.dur, u.value, gct.ugpu
    FROM counter_leading_intervals!((
      SELECT c.id, c.ts, c.track_id, c.value
      FROM counter c
      JOIN gpu_counter_track gct ON gct.id = c.track_id
      WHERE gct.name = 'Utilization'
    )) u
    JOIN gpu_counter_track gct ON gct.id = u.track_id
  ),
  -- The 5 longest compute kernels (render_stage_category 2 = COMPUTE).
  top_kernels AS (
    SELECT
      s.id, s.ts, s.dur, s.name,
      extract_arg(t.dimension_arg_set_id, 'ugpu') AS ugpu
    FROM gpu_slice s
    JOIN gpu_track t ON s.track_id = t.id
    WHERE s.render_stage_category = 2 AND s.dur > 0
    ORDER BY s.dur DESC
    LIMIT 5
  )
SELECT
  k.name AS kernel,
  g.name AS gpu_name,
  k.dur AS dur_ns,
  -- Time-weighted average: sum(value * overlap_dur) / kernel_dur.
  SUM(u.value * ii.dur) / k.dur AS avg_utilization
FROM top_kernels k
LEFT JOIN gpu g ON g.id = k.ugpu
JOIN _interval_intersect!((top_kernels, utilization), (ugpu)) ii
  ON ii.id_0 = k.id
JOIN utilization u ON u.id = ii.id_1
GROUP BY k.id, k.name, g.name, k.dur
ORDER BY k.dur DESC;

Example output (two-GPU training trace):

kernel	gpu_name	dur_ns	avg_utilization
matmul_bwd_kernel	NVIDIA A100-SXM4-80GB #1	180000	78.27
matmul_bwd_kernel	NVIDIA A100-SXM4-80GB #2	180000	77.25
matmul_kernel	NVIDIA A100-SXM4-80GB #1	125000	78.70
matmul_kernel	NVIDIA A100-SXM4-80GB #2	125000	78.83
softmax_bwd_kernel	NVIDIA A100-SXM4-80GB #1	110000	73.76