RDS Applications By GPU Test

GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. GPU-accelerated computing enhances application performance by offloading compute-intensive portions of the application to the GPU, while the remainder of the code still runs on the CPU.

In GPU-enabled virtual environments, if users to virtual applications complain of slowness when accessing graphic applications, administrators must be able to instantly figure out what is causing the slowness – is it because adequate GPU resources are not available to the host? Or is it because of excessive utilization of GPU memory and processing resources by any of the applications on the host?  Accurate answers to these questions can help administrators determine whether/not:

  • The host is sized with sufficient GPU resources;
  • The GPUs are configured with enough graphics memory;

Measures to right-size the host and fine-tune its GPU configuration can be initiated based on the results of this analysis. This is exactly what the RDS Applications By GPU test helps administrators achieve! 

To help with better utilization of resources, you can track the GPU usage rates of your instances for each application on the target RDS server. When you know the GPU usage rates, you can then perform tasks such as setting up managed instance groups that can be used to autoscale resources based on needs.

Target of the test : A Microsoft RDS server

Agent deploying the test : An internal/remote agent

Outputs of the test : One set of results for each application that is using GPU card on the Microsoft RDS server being monitored.

Configurable parameters for the test
Parameters Description

Test Period

How often should the test be executed. By default, this is 15 minutes.


The host for which the test is to be configured.


Refers to the port used by the target RDS server.

GPU Vendor

By default, Auto is selected from this drop-down list indicating that this test would automatically discover the vendor name of the GPU card installed on the target server and collect performance metrics. However, you can select NVIDIA from this list if NVIDIA GPU card is installed in the target server. Choosing NVIDIA from this list will enable this test to use nvidia-smi commands to collect performance metrics from the NVIDIA GPU card.


By default, this parameter is set to none indicating that the eG agent would automatically discover the location at which the nvidia-smi is installed for collecting the metrics of this test. If the nvidia-smi is installed in a different location in your virtual environment, then indicate that location in the NVIDIA Home text box.

Report by Domain Name

By default, the flag is set to Yes. This implies that by default, this test will report metrics for every domainname\username configured for this test. This way, administrators will be able to quickly determine which user logged in from which domain. If you want the test to report metrics for the username alone, then set this flag to No.

DD Frequency

Refers to the frequency with which detailed diagnosis measures are to be generated for this test. The default is 1:1. This indicates that, by default, detailed measures will be generated every time this test runs, and also every time the test detects a problem. You can modify this frequency, if you so desire. Also, if you intend to disable the detailed diagnosis capability for this test, you can do so by specifying none against DD Frequency.

Detailed Diagnosis

To make diagnosis more efficient and accurate, the eG Enterprise embeds an optional detailed diagnostic capability. With this capability, the eG agents can be configured to run detailed, more elaborate tests as and when specific problems are detected. To enable the detailed diagnosis capability of this test for a particular server, choose the On option. To disable the capability, click on the Off option.

The option to selectively enable/disable the detailed diagnosis capability will be available only if the following conditions are fulfilled:

  • The eG manager license should allow the detailed diagnosis capability
  • Both the normal and abnormal frequencies configured for the detailed diagnosis measures should not be 0.
Measurements made by the test
Measurement Description Measurement Unit Interpretation

GPU Instances Currently Running

Indicates the number GPU instances that are currently running for this application.



GPU Compute usage

Indicates the percentage of GPU compute capability utilized by this application.


The detailed diagnosis of this measure reveals the session ID, ID and name of process, total GPU utilization by each application, GPU utilized for performing encoder, decoder, 3D, copy and video operations, the amount of memory that is currently in use, image path and information about the GPU card.

Encoder usage

Indicates the percentage of GPU that is utilized for encoding process.


A value close to 100 is a cause of concern. By closely analyzing these measures, administrators can easily be alerted to situations where graphics processing is a bottleneck for any application.

Decoder usage

Indicates the percentage of GPU that is utilized for decoding process.


Memory Compute Usage

Indicates the percentage of the allocated GPU memory that is currently being utilized by this application.


A value close to 100% is a cause for concern as it indicates that the graphics memory on a GPU is almost always in use.

Memory Used

Indicates the amount of memory on the GPU used by this application.


For better user experience with graphic applications, sufficient memory should be available to the applications.

3D usage

Indicates the percentage of GPU utilized for processing 3D frames while running this application.


Compare the value of this measure across the users to identify which application is over-utilizing the GPU for processing 3D frames.

Copy usage

Indicates the percentage of the GPU utilized for copying operations.


Compare the value of this measure across the users to identify which application is over-utilizing the GPU for copying operations.

Video processing usage

Indicates the percentage of GPU utilized for performing video decoding process.


Compare the value of this measure across the users to identify which application is over-utilizing the GPU for video decoding.