TheRock/rocm-systems/projects/rocprofiler-compute/docs/design/hld-profiler-hub-integration.md

Integration of Profiler Hub in rocprof-compute for profiling phase

System context

Profiler Hub is a data storage level which abstracts profiling tools from concrete data format on disk.

Rocprof-compute produces two types of artifacts:

• Profiling data - raw unprocessed data from target application and system.
• Analysis data - processed data according to specific analysis type selected by user.

Profiler Hub is targeted to abstract both of these types of data. However, only profiling data is a focus of this HLD at the first step.

Profiling phase currently supports two output formats CSV and ROCPD. Currently CSV format is planned to be deprecated as part of another HLD. Therefore, scope of HLD is current ROCPD data collection on profiling phase only, and how to extend it switch it Profiler Hub.

Profiling phase

During profiling phase ROCm Compute loads two collectors via LD_PRELOAD:

• rocprofiler-sdk-tool.so - ROCm SDK-provided library which collects profiling data inside target process:
  • Collection result is in rocpd format containing kernel dispatches, agents info, kernel symbols.
  • Counters collection is disabled (when native collector is used, which is by default).
  • Separate databases are produced per each process and each run. Because many analysis types require ~10-20 passes and complex workloads could spawn multiple processes, this could produce ~100+ databases.
  • At the end of each collection pass python layer reads individual databases and merges them into a resulting database.
• rocprofiler-compute-tool.so - ROCm Compute-provided library which collects profiling data inside target process.
  • Collection result is in csv format containing hardware counters.
  • Collected data is accumulated in memory and is written on disk at the process end.
  • After each collection pass, the data from csv is merged into resulting database.

The following diagram illustrates the flow. Note that in real workloads there will be multiple processes and each process will do data writing independently from each other.

sequenceDiagram
    participant compute as Rocprof-compute (profile)
    participant process as Target process
    participant sdkTool as rocprofiler-sdk-tool.so
    participant computeTool as rocprofiler-compute-tool.so
    participant procRocpd as per-process rocpd
    participant procCsv as per-process csv
    participant passRocpd as Per-pass rocpd

    compute->>process: Launches
    process->>sdkTool: Loads via LD_PRELOAD
    process->>computeTool: Loads via LD_PRELOAD

    rect rgb(120, 120, 120)
    note left of compute: Loop: per collection pass
        rect rgb(140, 140, 140)
        note right of sdkTool: In-process data collection
            sdkTool->>procRocpd: Write rocpd (per-process)
            computeTool->>procCsv: Write csv (per-process)
        end

        rect rgb(140, 140, 140)
        note right of compute: Data merge
            procRocpd-->>compute: Rocpd data is read at the end of the pass
            procCsv-->>compute: CSV data is read at the end of the pass
            compute->>passRocpd: Intermediate rocpd and csv data is merged into a single per-pass rocpd
        end

        rect rgb(140, 140, 140)
        note right of compute: Removal of intermediate<br>data
            compute->>procRocpd: Deletes per-process rocpds
            compute->>procCsv: Deletes per-process CSVs
        end
    end

Note: Currently profile phase converts the databases into CSV files but this step is to be removed as part of another effort. Therefore, in scope of this HLD this step is ignored.

Analysis phase

During analysis phase ROCm Compute loads and merges all per-pass rocpd databases into a single Pandas dataframe in the memory. The merge is done by Kernel Name + Grid Size (default) or just Kernel Name.

sequenceDiagram
    participant compute as Rocprof-compute (profile)
    participant passRocpd as Per-pass rocpds

    passRocpd-->>compute: Read rocpd data
    note over compute: Merges rocpd data to<br/>pandas dataframe

Note: Currently analyze phase can load only CSVs (for this profiler phase converts rocpd to CSVs). But this step is to be removed soon. Therefore, in scope of this HLD this step is ignored and it's assumed that rocpd are read directly.

Problem statement

Multiple tools depend on rocpd format - ROCm SDK, ROCm Compute, ROCm Systems and ROCm Optiq. Therefore, this format is subject to change to meet the needs of all of these tools. In the current architecture such changes directly impact (potentially break) read/write logic for the rocpd. Therefore, it's important to abstract read/write operations by a more stable interface.

Additionally, we need a flexibility to support other formats in addition to rocpd. The primary motivation is to provide better size and performance characteristics compared to rocpd. For example, here are the current profiling data sizes across different frameworks:

Framework	Model	Disk: complete profiling
PyTorch (native)	DeepSeek-V3 (optional)	1.4–1.5 TB (inference)
JAX (Flax / jax-rocm)	Llama 3.1 8B (if Flax available)	~80–200 GB (training); ~25–60 GB (inference)
Megatron (Primus/ROCm)	DeepSeek-V3 (3-layer proxy)	4–6 TB (full); ~200–500 GB (3-layer proxy, short iters)
vLLM	Qwen3 Coder 480B (optional)	1.0–1.1 TB
SGLang	DeepSeek-V3	1.4–1.5 TB
Hugging Face TGI	GLM-4-9B or GLM-4.5-Air	50–120 GB (9B); 220–310 GB (Air)
Triton Inference Server	Qwen3-32B (if Triton + vLLM backend)	70–170 GB

Such support for additional formats shall have a manageable maintenance cost and therefore read/write operations again need to be abstracted by an interface from the rest of the product code.

Profiler Hub interface serves exactly this purpose, therefore this HLD defines the target architecture which we want to achieve as a result of Profiler Hub integration.

Requirements

Design requirements

• Profiler Hub shall abstract read write operations in native collector for profiling database in both profile and analysis phases of ROCm Compute.
• Design shall cover counters data and be extendable to PC Sampling data (including Source, ISA, instruction dependencies).
• Design shall allow easy swap of rocpd format to any other format (ex. Parquet). This means that ROCm Compute code shall not depend and know about a concrete format or file structure on the disk.
• Design shall cover counters data read/write on a first stage but be expandable for future data collected by native-collector, such as PC sampling.
• Profiling phase shall try to reduce number of artifacts produced in order to make it more convenient to manage them (ex. copy from remote machine or load in script).

Profiler Hub prerequisites

This section is a summary of requirements from Profiler Hub. Design section below provides more context on how these requirements will be used.

• Profiler Hub needs to have python bindings for its interface as they are required to access captured data in analysis scripts. See https://github.com/ROCm/rocm-systems/tree/develop/projects/rocprofiler-sdk/source/lib/python/rocpd for an example.
• Profiler Hub interface shall support storage of PC sampling data.
• Profiler Hub back-end shall be extendable to other data formats, ex Parquet.

Design

Linking with Profiler Hub

• Profiler Hub should be compiled and linked with rocprofiler-compute-tool.so via out-of-source-tree add_subdirectory.
• For this, compute shall be moved in projects/profilers directory, so that dependent projects are localized. This localization, for example, would allow to impose a clear requirement to sparse-checkout whole projects/profilers directory in order to build profilers. And this requirement in turn would make it easier to reuse code between components.
• Other options considered:
  • Sparse-checkout of Profiler Hub as submodule. Rejected because that adds an unnecessary complexity to the dependency with component which is part of profilers anyway.
  • Distribution of ProfilerHub and its includes as part of ROCm installation. Rejected because this could cause API and ABI incompatibilities.

Profiling phase

The following architectural changes are to be made to switch counter collection in profiling phase onto Profiler Hub:

• rocprofiler-compute-tool collector creates Profiler Hub storage file and writes counters data into it. This file name shall include <pid> because real workloads will spawn multiple processes.
• Profile mode of ROCm Compute still merges per-process Profiler Hub storage files into a single file in scope of a single pass. However, it does it by using Profiler Hub interface in order to abstract this analysis scripts from concrete format on the disk. Other options considered were:
  • Implementing merge functionality in Profiler Hub. Has been rejected because merge functionality is a compute requirement only.
  • Use existing merge functionality in analysis scripts. Has been rejected because this leaves a coupling of analysis script with concrete data format, which defeats the purpose of this design.
  • Remove merging logic completely. Has been rejected because this would produce up to 100 artifacts in multi-pass multi-process workloads even on a single node. And that number would be larger in multi-node scenarios.
• Data from rocpd and Profiler Hub produce separate artifacts and have separate processing code paths. This is because the long-term plan is to fully switch on rocprofiler-compute-tool for data collection and therefore on ProfilerHub for data read/write. Therefore, merge of rocpd and Profiler Hub data would introduce unnecessary coupling between these data formats which would increase the cost of aforementioned switch.

sequenceDiagram
    participant compute as Rocprof-compute (profile)
    participant process as Target process
    participant sdkTool as rocprofiler-sdk-tool.so
    participant computeTool as rocprofiler-compute-tool.so
    participant procRocpd as per-process rocpd
    participant procHub as Per-process Profiler Hub storage
    participant passRocpd as Per-pass rocpd (data from SDK tool)
    participant passHub as Per-pass Profiler Hub storage

    compute->>process: Launches
    process->>sdkTool: Loads via LD_PRELOAD
    process->>computeTool: Loads via LD_PRELOAD

    rect rgb(120, 120, 120)
    note left of compute: collection pass
        rect rgb(140, 140, 140)
        note right of sdkTool: In-process data collection
            sdkTool->>procRocpd: Write rocpd (per-process)
            computeTool->>procHub: Write data via Profiler Hub (per-process)
        end

        rect rgb(140, 140, 140)
        note right of compute: SDK data merge
            procRocpd-->>compute: Rocpd data is read at the end of the pass
            compute->>passRocpd: Intermediate rocpd data from SDK is merged into a single per-pass rocpd
        end

        rect rgb(140, 140, 140)
        note right of compute: Profiler Hub data merge
            procHub-->>compute: Profiler Hub Storage data is read at the end of the pass (via Profiler Hub interface)
            compute->>passHub: Intermediate Profiler Hub data is merged into a single per-pass rocpd
        end

        rect rgb(140, 140, 140)
        note right of compute: Removal of intermediate<br>data
            compute->>procRocpd: Deletes per-process rocpds
            compute->>procHub: Deletes per-process Profiler Hub Storage
        end
    end

This design also means that rocprof-compute will need to implement Python bindings for both read and write interfaces of Profiler Hub.

Analysis phase

The following architectural changes are to be made to switch counter collection in analysis phase onto Profiler Hub:

• Analysis phase reads both per-pass Profiler Hub and rocpd data and merges them both into a single Pandas dataframe.

sequenceDiagram
    participant compute as Rocprof-compute (profile)
    participant passRocpd as Per-pass rocpds from SDK Tool
    participant passHub as Per-pass Profiler Hub storage

    passRocpd-->>compute: Read rocpd data
    passHub-->>compute: Read native collector data via Profiler Hub
    note over compute: Merges all counters data to<br/>pandas dataframe

Addition of PC Sampling data support in Profiler Hub

PC Sampling data collection is spread between both rocprofiler-compute-tool and rocprofiler-sdk-tool. However, the long-term plan is to move all this data collection to rocprofiler-compute-tool only. This work is currently in progress.

Therefore, over the course of this transition the data which is collected in a rocprofiler-compute-tool shall be read/written via Profiler Hub. For this necessary interface extensions shall be made in a Profiler Hub. Details of this work is to be defined in a separate HLD.

Implementation phases

1. Integration of Profiler Hub in rocprofiler-compute-tool.so. This phase is covered by ./lld-profiler-hub-native-tool-integration.md. At the end of this phase:
   • Metrics are written into per process databases via Profiler Hub.
   • However analysis scripts still access per-process databases directly and merge both SDK and rocprofiler-compute-tool.so into a per-pass database.
2. Implementation of Python bindings for Profiler Hub. At the end of this phase:
   • Python bindings are added to Profiler Hub library (directly in Profiler Hub project directory).
   • Bindings are covered by unit tests which are run in CI.
3. Integration of Profiler Hub in analysis scripts. At the end of this phase:
   • Analysis scripts merge results from rocprofiler-compute-tool.so and rocprofiler-sdk-tool.so independently into two databases.
   • Read/write of results from rocprofiler-compute-tool.so is performed only via Profiler Hub python bindings.

Validation

• Validation is done via existing functional tests, they all shall pass.
• No additional tests or tracing/debug features required.