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4. Special Configurations

4.1. Multi-Instance GPU (MIG)

Multi-Instance GPU (MIG) is a feature that allows a GPU to be partitioned into multiple CUDA devices. The partitioning is carried out on two levels: First, a GPU can be split into one or multiple GPU Instances. Each GPU Instance claims ownership of one or more streaming multiprocessors (SM), a subset of the overall GPU memory, and possibly other GPU resources, such as the video encoders/decoders. Second, each GPU Instance can be further partitioned into one or more Compute Instances. Each Compute Instance has exclusive ownership of its assigned SMs of the GPU Instance. However, all Compute Instances within a GPU Instance share the GPU Instance's memory and memory bandwidth. Every Compute Instance acts and operates as a CUDA device with a unique device ID. See the driver release notes as well as the documentation for the nvidia-smi CLI tool for more information on how to configure MIG instances.

From the profiling perspective, a Compute Instance can be of one of two types: isolated or shared.

An isolated Compute Instance owns all of it's assigned resources and does not share any GPU unit with another Compute Instance. In other words, the Compute Instance is of the same size as its parent GPU Instance and consequently does not have any other sibling Compute Instances. Tracing and Profiling works for isolated Compute Instances.

A shared Compute Instance uses GPU resources that can potentially also be accessed by other Compute Instances in the same GPU Instance. Due to this resource sharing, collecting profiling data from shared units is not permitted. Attempts to collect metrics from a shared unit will result in NaN values. Better error reporting will be done in a future release. Collecting metrics from GPU units that are exclusively owned by a shared Compute Instance is still possible. Tracing works for shared Compute Instances.

To allow users to determine which metrics are available on a target device, new APIs have been added which can be used to query counter availability before starting the profiling session. See APIs NVPW_RawMetricsConfig_SetCounterAvailability and cuptiProfilerGetCounterAvailability.

All Compute Instances on a GPU share the same clock frequencies. To get consistent metric values with multi-pass collection, it is recommended to lock the GPU clocks during the profiling session. CLI tool nvidia-smi can be used to configure a fixed frequency for the whole GPU by calling nvidia-smi --lock-gpu-clocks=tdp,tdp. This sets the GPU clocks to the base TDP frequency until you reset the clocks by calling nvidia-smi --reset-gpu-clocks.

4.2. NVIDIA Virtual GPU (vGPU)

CUPTI supports tracing and profiling features on NVIDIA virtual GPUs (vGPUs). Activity, Callback and Profiling APIs are supported but Event and Metric APIs are not supported on NVIDIA vGPUs. If you want to use profiling features that NVIDIA vGPU supports, you must enable them for each vGPU VM that requires them. These can be enabled by setting a vGPU plugin parameter enable_profiling. How to set the parameter for a vGPU VM depends on the hypervisor that you are using. Tracing is enabled by default, it doesn't require any specific setting. However tracing results might not be accurate after virtual machine (VM) migration. Therefore it is recommended to set the vGPU plugin parameter enable_profiling for accurate results. Refer to the NVIDIA Virtual GPU Software documentation for the list of supported GPUs, how to enable profiling features using the vGPU plugin parameter and for limitations on use of CUPTI with NVIDIA vGPU.

4.3. Windows Subsystem for Linux (WSL)

WSL or Windows Subsystem for Linux is a Windows feature that enables users to run native Linux applications, containers and command-line tools directly on Windows 11 and later OS builds. CUPTI supports tracing APIs Activity and Callback on the second generation of WSL (WSL 2) on Volta and later GPU architectures. Profiler APIs Event, Metric, Profiling and PC Sampling are not supported on WSL.