eG Monitoring
 

Measures reported by XenGPUStatsTest

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.

Imagine if you could access to your GPU-accelerated applications anywhere on any device, even those requiring intensive graphics power. NVIDIA GRID makes this possible. With NVIDIA GRID, a virtualized GPU designed specifically for virtualized server environments, data center managers can bring true PC graphics-rich experiences to users.

The NVIDIA GRID GPUs will be hosted in enterprise data centers and allow users to run virtual desktops or virtual applications on multiple devices connected to the internet and across multiple operating systems, including PCs, notebooks, tablets and even smartphones. Users can utilize their online-connected devices to enjoy the GPU power remotely.

In VDI/virtualized server environments, the NVIDIA GRID delivers GPU resources to virtual desktops/VMs using one of the two technologies described below:

  • Dedicated GPU or GPU Pass-through Technology: NVIDIA GPU pass-through technology lets you create a virtual workstation that gives users all the benefits of a dedicated graphics processor at their desk. By directly connecting a dedicated GPU to a virtual machine through the hypervisor, you can now allocate the full GPU and graphics memory capability to a single virtual machine without any resource compromise.

  • Shared GPU or Virtual GPU (vGPU) Technology: GRID vGPU is the industry's most advanced technology for sharing true GPU hardware acceleration between multiple virtual desktops-without compromising the graphics experience. With GRID vGPU technology, the graphics commands of each virtual machine are passed directly to the GPU, without translation by the hypervisor. This allows the GPU hardware to be time-sliced to deliver improved shared virtualized graphics performance. The GRID vGPU manager allows for management of user profiles. IT managers can assign the optimal amount of graphics memory and deliver a customized graphics profile to meet the specific needs of each user. Every virtual desktop has dedicated graphics memory, just like they would at their desk, so they always have the resources they need to launch and run their applications.

In GPU-enabled Citrix XenServer environments, if users to VMs/virtual desktops 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 a few VMs/virtual desktops 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;

  • The GPU technology in use - i.e., the GPU Pass-through technology or the Shared vGPU technology - is ideal for the graphics processing requirements of the environment;

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 XenGPUStatsTest test helps administrators achieve!

Using this test, administrators can identify the physical GPUs on the NVIDIA GRID card used by the host. For each physical GPU, administrators can determine how actively memory on that GPU is utilized, thus revealing the GPU on which memory is used consistently. In addition, the test also indicates how busy each GPU is, and in the process pinpoints those physical GPUs that are being over-utilized by the VMs/virtual desktops on the host. The adequacy of the physical GPU resources is thus revealed. Moreover, the power consumption and temperature of each GPU of the host is also monitored and its current temperature and power usage can be ascertained; administrators are thus alerted to abnormal power usage of the GPU and unexpected fluctuations in its temperature. The power limit set and the clock frequencies configured are also revealed, so that administrators can figure out whether the GPU is rightly configured for optimal processing or is any fine-tuning required.

Note:

  • This test will run and report GPU usage metrics only if the Shared GPU or Virtual GPU (vGPU) Technology is used to deliver GPU resources to virtual desktops/VMs. Where the Dedicated GPU or Pass-through GPU Technology is used, this test will report only the Virtual machines and the Mode metrics.

  • This test also reports metrics from a Summary descriptor. For the summary descriptor, this test will report the values aggregated across shared GPUs and those that are configured as Unclassified.
The measures made by this test are as follows:

Measurement Description Measurement Unit Interpretation
Util_compute Indicates the proportion of time over the past sample period during which one or more kernels was executing on this GPU. Percent A value close to 100% indicates that the GPU is busy processing graphic requests almost all the time.

In a Shared vGPU environment on the other hand, a GPU may be in use almost all the time, if one/more VMs/virtual desktops on the host are running highly graphic-intensive applications. If you find that only a single VM/virtual desktop has been consistently hogging the GPU resources, you may want to switch to the Dedicated GPU mode, so that excessive GPU usage by that VM/virtual desktop has no impact on the performance of other VMs/virtual desktops on that host.

If all GPUs are found to be busy most of the time, you may want to consider augmenting the GPU resources of the host.

Compare the value of this measure across physical GPUs to know which GPU is being used more than the rest.
Power Indicates the current power usage of this GPU. Watts A very high value is indicative of excessive power usage by the GPU.

In such cases, you may want to enable Pwr_mgmt so that the GPU limits power draw under load to fit within a predefined power envelope by manipulating the current performance state.
Temperature Indicates the current temperature of this GPU. Celsius Ideally, the value of this measure should be low. A very high value is indicative of abnormal GPU temperature.
FB_tot Indicates the total size of frame buffer memory of this GPU. MiB Frame buffer memory refers to the memory used to hold pixel properties such as color, alpha, depth, stencil, mask, etc.
Memory_used Indicates the amount of frame buffer memory on-board this GPU that has been allocated to the host. MiB Frame buffer memory refers to the memory used to hold pixel properties such as color, alpha, depth, stencil, mask, etc.

Properties like the screen resolution, color level, and refresh speed of the frame buffer can impact graphics performance.

Also, if Error-correcting code (ECC) is enabled, the available frame buffer memory may be decreased by several percent. This is because, ECC uses up memory to detect and correct the most common kinds of internal data corruption. Moreover, the driver may also reserve a small amount of memory for internal use, even without active work on the GPU; this too may impact frame buffer memory.

For optimal graphics performance therefore, adequate frame buffer memory should be allocated to the host.
Memory_free Indicates the amount of frame buffer memory on-board this GPU that has not been allocated to the host. MiB  
Memory_util Indicates the proportion of time over the past sample period during which global (device) memory was being read or written on this GPU. Percent A value close to 100% is a cause for concern as it indicates that graphics memory on a GPU is almost always in use.

In a Shared vGPU environment on the other hand, memory may be consumed all the time, if one/more VMs/virtual desktops on the host utilize the graphics memory excessively and constantly. If you find that only a single VM/virtual desktop has been consistently hogging the graphic memory resources, you may want to switch to the Dedicated GPU mode, so that excessive memory usage by that VM/virtual desktop has no impact on the performance of other VMs/virtual desktops on that host.

If the value of this measure is high almost all the time for most of the GPUs, it could mean that the host is not sized with adequate graphics memory.
BAR_tot Indicates the total size of the BAR1 memory of this GPU. MiB BAR1 is used to map the frame buffer (device memory) so that it can be directly accessed by the CPU or by 3rd party devices (peer-to-peer on the PCIe bus).
BAR_used Indicates the amount of BAR1 memory on this GPU that is allocated to the host. MiB For better user experience with graphic applications, enough BAR1 memory should be available to the host.
BAR_free IIndicates the total size of BAR1 memory of this GPU that is still not allocated to the host. MiB  
Pwr_mgmt Indicates whether/not power management is enabled for this GPU.   Many NVIDIA graphics cards support multiple performance levels so that the server can save power when full graphics performance is not required.

The default Power Management Mode of the graphics card is Adaptive. In this mode, the graphics card monitors GPU usage and seamlessly switches between modes based on the performance demands of the application. This allows the GPU to always use the minimum amount of power required to run a given application. This mode is recommended by NVIDIA for best overall balance of power and performance. If the power management mode is set to Adaptive, the value of this measure will be Supported.

Alternatively, you can set the Power Management Mode to Maximum Performance. This mode allows users to maintain the card at its maximum performance level when 3D applications are running regardless of GPU usage. If the power management mode of a GPU is Maximum Performance, then the value of this measure will be Maximum.

The numeric values that correspond to these measure values are discussed in the table below:

Measure Value Numeric Value
Supported 1
Maximum 0

Note:

By default, this measure will report the Measure Values listed in the table above to indicate the power management status. In the graph of this measure however, the same is represented using the numeric equivalents only.
Pwr_limit Indicates the power limit configured for this GPU. Watts This measure will report a value only if the value of the ‘Pwr_mgmt’ measure is ‘Supported’.

The power limit setting controls how much voltage a GPU can use when under load. Its not advisable to set the power limit at its maximum - i.e., the value of this measure should not be the same as the value of the Pwr_maxLimit measure - as it can cause the GPU to behave strangely under duress.
Pwr_dLimit Indicates the default power management algorithm's power ceiling for this GPU. Watts This measure will report a value only if the value of the ‘Pwr_mgmt’ measure is ‘Supported’.
Pwr_enfLimit Indicates the power management algorithm's power ceiling for this GPU. Watts This measure will report a value only if the value of the ‘Pwr_mgmt’ measure is ‘Supported’.

The total board power draw is manipulated by the power management algorithm such that it stays under the value reported by this measure.
Pwr_minLimit The minimum value that the power limit of this GPU can be set to. Watts This measure will report a value only if the value of the ‘Pwr_mgmt’ measure is ‘Supported’.
Pwr_maxLimit The maximum value that the power limit of this GPU can be set to. Watts This measure will report a value only if the value of the ‘Pwr_mgmt’ measure is ‘Supported’.

If the value of this measure is the same as that of the Power limit measure, then the GPU may behave strangely.
Clk_grap Indicates the current frequency of the graphics clock of this GPU. MHz GPU has many more cores than your average CPU but these cores are much simpler and much smaller so that many more actually fit on a small piece of silicon. These smaller, simpler cores go by different names depending upon the tasks they perform. Stream processors are the cores that perform a single thread at a slow rate. But since GPUs GPU has many more cores than your average CPU but these cores are much simpler and much smaller so that many more actually fit on a small piece of silicon. These smaller, simpler cores go by different names depending upon the tasks they perform. Stream processors are the cores that perform a single thread at a slow rate. But since GPUs contain numerous stream processors, they make overall computation high. The streaming multiprocessor clock is how fast the stream processors run. The memory clock is how fast the memory on the card runs. The GPU core clock is the speed at which the GPU assigned to the VM/virtual desktop operates.

By correlating the frequencies of these clocks - i.e., the value of these measures - with the memory usage, power usage, and overall performance of the GPU, you can figure out if overclocking is required or not.

Overclocking is the process of forcing a GPU core/memory to run faster than its manufactured frequency. Overclocking can have both positive and negative effects on GPU performance. For instance, memory overclocking helps on cards with low memory bandwidth, and with games with a lot of post-processing/textures/filters like AA that are VRAM intensive. On the other hand, speeding up the operation frequency of a shader/streaming processor/memory clock, without properly analyzing its need and its effects, may increase its thermal output in a linear fashion. At the same time, boosting voltages will cause the generated heat to sky rocket. If improperly managed, these increases in temperature can cause permanent physical damage to the core/memory or even “heat death”.

Putting an adequate cooling system into place, adjusting the power provided to the GPU, monitoring your results with the right tools and doing the necessary research are all critical steps on the path to safe and successful overclocking.
Clk_mem Indicates the current frequency of the memory clock of this GPU. MHz
Clk_sm Indicates the current frequency of the streaming multiprocessor clock of this GPU. MHz
Fan_speed Indicates the percent of maximum speed that this GPU's fan is currently intended to run at. Percent The value of this measure could range from 0 to 100%.

An abnormally high value for this measure could indicate a problem condition - eg., a sudden surge in the temperature of the GPU that could cause the fan to spin faster.

Note that the reported speed is only the intended fan speed. If the fan is physically blocked and unable to spin, this output will not match the actual fan speed. Many parts do not report fan speeds because they rely on cooling via fans in the surrounding enclosure. By default the fan speed is increased or decreased automatically in response to changes in temperature.
Compute_proc Indicates the number of processes having compute context on this GPU. Number Use the detailed diagnosis of this measure to know which processes are currently using the GPU. The process details provided as part of the detailed diagnosis include, the PID of the process, the process name, and the GPU memory used by the process.

Note that the GPU memory usage of the processes will not be available in the detailed diagnosis, if the Windows platform on which XenApp operates is running in the WDDM mode. In this mode, the Windows KMD manages all the memory, and not the NVIDIA driver. Therefore, the NVIDIA SMI commands that the test uses to collect metrics will not be able to capture the GPU memory usage of the processes.
Vol_sin_ecc_err Indicates the number of volatile single bit errors in this GPU. Number Volatile error counters track the number of errors detected since the last driver load. Single bit ECC errors are automatically corrected by the hardware and do not result in data corruption.

Ideally, the value of this measure should be 0.
Vol_dou_ecc_err Indicates the total number of volatile double bit errors in this GPU. Number Volatile error counters track the number of errors detected since the last driver load. Double bit errors are detected but not corrected.

Ideally, the value of this measure should be 0.
Agg_sin_ecc_err Indicates the total number of aggregate single bit errors in this GPU. Number Aggregate error counts persist indefinitely and thus act as a lifetime counter. Single bit ECC errors are automatically corrected by the hardware and do not result in data corruption.

Ideally, the value of this measure should be 0.
Agg_dou_ecc_err Indicates the total number of aggregate double bit errors in this GPU. Number Aggregate error counts persist indefinitely and thus act as a lifetime counter. Double bit errors are detected but not corrected.

Ideally, the value of this measure should be 0.
No_of_vms Indicates the number of virtual machines allocated to this GPU Grid card. Number If the Divice_mode measure reports the value Pass-through, then the value of this measure will be 1. Using the detailed diagnosis of this measure, you can identify the VMs that are using the GPU grid card.
Divice_mode Indicates the mode using which the GPU resources were delivered to the VMs.   The values that this measure can take and their corresponding numiec values are as follows:

Measure Value Numeric Values
Pass through 0
Shared 1
Unavailable (GPU card is not allocated to VM) 2

Note:

By default, this test reports the Measure Values listed in the table above to indicate the mode of GPU delivery. In the graph of this measure however, the same is represented using the numeric equivalents only.
Remain_capacity Indicates the number of virtual machines that can still be accomodated on this GPU grid card i.e., the virtual machines that can utilize this GUP grid card. Number  
BAR1_mem_util Indicates the percentage of the total BAR1 memory on this GPU that is currently being utilized by the host. Percent A value close to 100% is indicative of excessive BAR1 memory usage by the host. For best graphics performance, sufficient BAR1 memory resources should be available to the host.
FB_mem_util Indicates the percentage of frame buffer memory on-board this GPU that is being utilized by the host. Percent A value close to 100% is indicative of excessive frame buffer memory usage.

Properties like the screen resolution, color level, and refresh speed of the frame buffer can impact graphics performance.

Also, if Error-correcting code (ECC) is enabled on a host, the available frame buffer memory may be decreased by several percent. This is because, ECC uses up memory to detect and correct the most common kinds of internal data corruption. Moreover, the driver may also reserve a small amount of memory for internal use, even without active work on the GPU; this too may impact frame buffer memory.

For optimal graphics performance therefore, adequate frame buffer memory should be allocated to the host.

Use the detailed diagnosis of this measure to know the frame buffer memory usage of each VM on the monitored server. This will point you to those VMs that are draining the frame buffer memory.
Encoder Indicates the amount of this GPU that is utilized for encoding process by the host. Percent These measures will report metrics only VMs configured with a Tesla GPU card.

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.
Decoder Indicates the amount of this GPU that is utilized by the host for decoding process. Percent