NVIDIA CUDA Profiling Tools Interface (CUPTI) - CUDA Toolkit

The NVIDIA CUDA Profiling Tools Interface (CUPTI) is a library that enables the creation of profiling and tracing tools that target CUDA applications. CUPTI provides a set of APIs targeted at ISVs creating profilers and other performance optimization tools:

  • the Activity API,
  • the Callback API,
  • the Host Profiling API,
  • the Range Profiling API,
  • the PC Sampling API,
  • the SASS Metric API,
  • the PM Sampling API,
  • the Checkpoint API,
  • the Profiling API,
  • the Python API (available separately)

Using these CUPTI APIs, independent software developers can create profiling tools that provide low and deterministic profiling overhead on the target system, while giving insight into the CPU and GPU behavior of CUDA applications. Normally packaged with the CUDA Toolkit, NVIDIA occasionally uses this page to provide CUPTI improvements and bug fixes between toolkit releases.



There is currently no CUPTI update to the CUDA Toolkit 13.2. You may obtain the latest version of CUPTI by Downloading the CUDA Toolkit 13.2.0

Download the CUDA Toolkit 13.2 Now

Download the CUPTI Python API 13.0.0 Now
Revision History

Key Features

  • Trace CUDA API by registering callbacks for API calls of interest
    • Full support for entry and exit points in the CUDA C Runtime (CUDART) and CUDA Driver
  • GPU workload trace for the activities happening on the GPU, which includes kernel executions, memory operations (e.g., Host-to-Device memory copies) and memset operations.
  • CUDA Unified Memory trace for transfers from host to device, device to host, device to device and page faults on CPU and GPU etc.
  • Normalized timestamps for CPU and GPU trace
  • Profile hardware and software event counters, including:
    • Utilization metrics for various hardware units
    • Instruction count and throughput
    • Memory load/store events and throughput
    • Cache hits/misses
    • Branches and divergent branches
    • Many more
  • Enables automated bottleneck identification based on metrics such as instruction throughput, memory throughput, and more
  • Range profiling to enable metric collection over concurrent kernel launches within a range
  • Metrics attribution at the high-level source code and the executed assembly instructions.
  • Device-wide sampling of the program counter (PC). The PC Sampling gives the number of samples for each source and assembly line with various stall reasons.
See the CUPTI User Guide for a complete listing of hardware and software event counters available for performance analysis tools.

Updates in CUDA Toolkit 13.2

    New Features

    • User-Defined Activity Records: CUPTI now supports user-defined activity records, allowing users to select specific fields for activity records instead of collecting complete predefined records. This feature addresses the limitations of fixed-field records by providing significant memory efficiency through custom field selection tailored to application-specific profiling needs. Key benefits include optimized memory usage by eliminating unused fields and padding, improved performance through compact data structures and faster data access, and improved backward compatibility as new fields can be added in future CUPTI versions without impacting existing user code. The feature is enabled using the CUPTI_ACTIVITY_ATTR_USER_DEFINED_RECORDS attribute, and new APIs have been added to support this functionality. For detailed information, see CUPTI User-Defined Activity Records and CUDA tracing with User-Defined Activity Records.

      Note

      This feature is currently in beta. APIs and behavior may change in future releases.

    • Added tracing support for Memory Locality Optimized Partition (MLOPart) devices.

    • Added numTpcs parameter to device record to report the total number of Thread Processing Clusters (TPCs) in the device. The activity record CUpti_ActivityDevice5 is deprecated and replaced by CUpti_ActivityDevice6.

    • Added activity kind CUPTI_ACTIVITY_KIND_GREEN_CONTEXT and structure CUpti_ActivityGreenContext to trace green context allocations, which enable GPU resource partitioning by assigning dedicated subsets of SMs and TPCs to specific contexts for fine-grained resource management and isolation. Starting from CUDA 13.2, CUPTI only emits CUpti_ActivityGreenContext for green contexts and no longer emits CUpti_ActivityContext for them.

    • The portDev0 and portDev1 fields in NVLink records are now dynamically allocated arrays, via malloc(), sized to physicalNvLinkCount ports. Clients must release this memory using free() when no longer needed. The record CUpti_ActivityNvLink4 has been deprecated and replaced by CUpti_ActivityNvLink5. This change is not backward compatible. Clients using CUDA 13.2 or later must update their code to use the new record structure.

    • Introduced CUPTI_MAX_DEVICES macro in cupti_common.h to represent the theoretical maximum number of devices supported by CUPTI.

    Resolved Issues

    • Report CUPTI_CBID_RESOURCE_GRAPH_NODE_SET_PARAMS callback for CUDA Graph nodes like memcpy, memset, host and event.

Requirements

    Supported platforms
    • Linux x86_64[1]
    • Windows x86_64[1]
    • Linux aarch64 SBSA[1]
    • DRIVE OS QNX aarch64[2]
    • DRIVE OS Linux aarch64[2]
    [1] available in the CUDA Desktop Toolkit only
    [2] available in the Embedded or Drive toolkits only
    Supported NVIDIA GPU architectures
    • Activity and Callback APIs
      • All architectures supported by CUDA Toolkit
    • Profiling and PC Sampling APIs
      • Blackwell: B100, GB10x, GB11x
      • Hopper: GH100
      • Ada: AD10x
      • Ampere: A100 with Multi-Instance GPU, GA10x
      • Turing
    CUDA Toolkit
    Drivers

Documentation

Support

To provide feedback, request additional features, or report issues, please use the Developer Forums.

Installation Overview

When installing CUDA Toolkit 13.2 and specifying options, be sure to select CUDA > Development > Tools > CUPTI.