Gpu utilization что это такое

GPU утилизация: что это такое и как она работает

GPU utilization (графический процессор расходования) — это показатель того, насколько полностью графический процессор используется во время выполнения графических задач. GPU utilization является важным инструментом для оптимизации работы графических приложений и обеспечения максимальной производительности.

Как правило, GPU utilization измеряется в процентах и показывает, сколько времени графический процессор выполняет полезную работу по обработке графики, а не простаивает. Высокий уровень GPU utilization говорит о том, что графический процессор работает на максимальной производительности, что может быть полезно для игр, видеоредактирования, машинного обучения и других графически интенсивных задач.

Чтобы эффективно использовать GPU utilization, необходимо учитывать особенности вашей системы и приложений. Важно выбрать подходящую графическую карту и драйверы, чтобы обеспечить максимальную совместимость и производительность. Также рекомендуется оптимизировать настройки вашего приложения и использовать соответствующие инструменты, такие как CUDA, OpenCL или Vulkan, чтобы раскрыть потенциал вашего графического процессора.

Что такое использование графического процессора и как его применять

Использование графического процессора (GPU utilization) — это процентное отношение времени, которое графический процессор проводит на выполнение вычислений, к общему времени его работы.

Графический процессор (GPU) является основным компонентом графической карты, который отвечает за обработку и генерацию графики. Он специализирован для выполнения параллельных вычислений и может эффективно выполнять большое количество задач одновременно.

Использование графического процессора может быть полезно в различных областях:

• Игровая индустрия: в играх графический процессор отвечает за отрисовку 3D графики и обеспечение плавной анимации. Увеличение использования графического процессора может улучшить качество графики и производительность игры.
• Научные исследования: графические процессоры часто используются для выполнения сложных вычислений в таких областях, как моделирование климата, искусственный интеллект и обработка больших данных. Это позволяет ускорить процесс и получить результаты быстрее.
• Компьютерное зрение и обработка изображений: графический процессор используется для обработки изображений, распознавания образов и сжатия данных. Это позволяет улучшить качество изображений и ускорить обработку.

Для эффективного использования графического процессора необходимо учитывать следующие моменты:

1. Оптимизация кода: использование параллельных алгоритмов и оптимизированного кода позволит увеличить использование графического процессора. Например, можно разделить задачи на несколько независимых потоков выполнения, которые будут выполняться параллельно.
2. Управление ресурсами: следует правильно управлять ресурсами графического процессора, чтобы избежать утечек памяти и других проблем. Неиспользуемые ресурсы следует освобождать, а необходимые использовать эффективно.
3. Мониторинг использования: регулярное мониторинг использования графического процессора позволяет оптимизировать работу и обнаружить возможные узкие места в процессе вычислений. Это может быть полезно при разработке программ, требующих использования графического процессора.

В итоге, использование графического процессора позволяет увеличить производительность в различных областях и получить более качественные результаты в работе с графикой и вычислениями.

Понятие Gpu utilization и его роль в работе графической карты

Gpu utilization (GPU использование) является одним из ключевых параметров, характеризующих работу графической карты. Он описывает процент времени, в течение которого графический процессор был активен и выполнял вычислительные задачи.

Gpu utilization играет важную роль в определении производительности графической карты и ее способности обрабатывать требовательные графические задачи. Чем выше уровень Gpu utilization, тем эффективнее используются вычислительные ресурсы графического процессора.

Повышение Gpu utilization может быть результатом оптимизации программного обеспечения или аппаратной конфигурации. Это может быть достигнуто путем улучшения параллельной обработки данных, оптимизации алгоритмов или увеличения тактовой частоты графического процессора.

Высокий уровень Gpu utilization особенно важен при выполнении требовательных графических задач, таких как 3D-моделирование, обработка видео или игры с высоким разрешением. Низкий уровень Gpu utilization может указывать на неэффективное использование графической карты или ее ограничения по производительности.

Для контроля и мониторинга Gpu utilization разработаны различные программные инструменты, которые позволяют отслеживать и анализировать загрузку графической карты. Эти инструменты позволяют определить, насколько эффективно используется графический процессор и помогают оптимизировать работу графической карты для достижения максимальной производительности.

Таким образом, Gpu utilization играет важную роль в работе графической карты, определяя ее способность обрабатывать требовательные графические задачи и влияя на ее производительность. Оптимальное использование ресурсов графического процессора позволяет достичь максимальной производительности и качества графики.

Understanding NVIDIA GPU Performance: Utilization vs. Saturation (2023)

GPU performance metrics reported by tools like nvidia-smi may be misleading. This blog delves into the underlying issue to provide a deeper understanding.

1 NVIDIA «GPU util» : a confusing phenomenon

Even when there is only a single task that is running on a small portion of a GPU, the «GPU util» metric reported by tools such as nvidia-smi or other nvml-based tools may indicate that the device is fully occupied, which is rather confusing for users.

To provide a clearer understanding, consider the example from the NVIDIA developer forum:

This code snippet will launch a specified kernel (thread) on a single Streaming Multiprocessor (SM). Based on conventional understanding, the “utilization” of the GPU should be calculated as 1 / num_sm * 100% . For instance:

• If there are 10 SMs on the GPU, the “GPU utilization” should be 10%.
• If there are 20 SMs on the GPU, the “GPU utilization” should be 5%.

However, it has been observed that nvidia-smi may report a «GPU-Util» of 100%, as demonstrated in the following example output:

What’s the problem? Let’s find the answer.

2 GPU Util : a misleading term?

We first do some searches to gain a better understanding.

2.1 Definition from official documentation

The nvidia-smi command line tool is based on the NVIDIA Management Library (NVML), which unfortunately is not open-sourced. To find some clarifications, we refer to the official NVML documentation. According to the documentation,

GPU utilization: Current utilization rates are reported for both the compute resources of the GPU and the memory interface.

This information does not provide the desired clarity. So, move on.

2.2 Exploring the code

Although the NVML library itself is not open-sourced, we discovered that there are open-source language bindings available for it. This means that we can at least access the structure and field definitions , typically provided in a C/C++ header file. Here we choose the gonvml project, which provides a Golang binding for NVML. Here is an excerpt from the NVML header file that defines the terms «GPU Util» and “Memory Util”:

With the comments above, we get the answer.

2.3 Explanation

According to the definition provided by NVML, “utilization” refers to the percentage of time that certain activities occurred during the past sample period . Specifically:

• GPU utilization: This represents the percentage of time during which one or more kernels were executing on the GPU.
• Memory utilization: This represents the percentage of time during which global (device) memory was being read from or written to.

In other words, the concept of “utilization” defined by NVML may not align with our common understanding . It solely measures the portion of time that the device is being used within the given sampling period, without considering the number of streaming multiprocessors (SMs) being utilized during that time. Typically, we perceive “utilization” as the portion of GPU processors being used.

I’m not sure why NVIDIA’s defines “utilization” in this unconventional way. But, it may be related to the “utilization” definition within the “USE” (Utilization/Saturation/Error) methodology.

2.4 The “USE” methodology

If you are familiar with the book “Systems Performance: Enterprise and the Cloud”, you may recall the “USE” methodology introduced by Brendan Gregg. This methodology focuses on three key metrics: Utilization, Saturation, and Errors. According to the “USE” blog, the terminology definitions are as follows:

• utilization: the average time that the resource was busy servicing work [2]
• saturation: the degree to which the resource has extra work which it can’t service, often queued
• errors: the count of error events

The “USE” methodology provides an extra explanation for “utilization”:

There is another definition where utilization describes the proportion of a resource that is used , and so 100% utilization means no more work can be accepted, unlike with the «busy» definition above .

In summary, within the “USE” methodology, “utilization” refers to the portion of time a resource is actively serving or working, without considering the allocated capacity . For the latter, the term “saturation” is used. While the “USE” methodology offers valuable insights into resource usage evaluation, redefining a well-established term like “utilization” can lead to confusion. Many people still prefer the concept of “utilization” representing capacity usage or saturation.

If necessary, an alternative term to replace “utilization” could be «used-frequency» , indicating how frequently the device is being utilized .

2.5 Two metric sources: NVML / DCGM

In most cases, the metrics that we are primarily concerned with are related to “saturation”. So, where can we find these GPU metrics?

There are two popular methods for collecting GPU performance metrics:

Using command line tools such as nvidia-smi , which can output data in formats like pretty-print and xml .

• This tool is based on NVML (NVIDIA Management Library) internally.
• It collects high-level metrics such as GPU and memory “utilization” (used-frequency), device temperature, power usage, etc.

Using services like dcgm-exporter , which can output data in Prometheus format.

• This service is based on DCGM (Data Center GPU Management) internally.
• In addition to high-level metrics, it can also perform profiling and collect detailed saturation data about the GPU devices.

Below are two collections of dashboards that show the metrics collected from nvidia-smi and dcgm-exporter :

Metrics from nvidia-smi

Note that GPU util is 100%. Below are the metrics collected from dcgm-exporter :

Metrics from dcgm-exporter

We can see that SM occupancy is very low ( <20% ), and floating point operations (FP32/FP16/TensorCore) also stays in a very low percent, indicating that the GPU is not saturated.

3 Conclusion and general advice

3.1 “Utilization” vs. saturation

Not sure if the NVML designer intentionally adopted the “USE” methodology mentioned above, but it seems that its definition of “utilization” (including GPU and memory utilization) aligns with the “USE” standard. The reported “utilization” simply indicates the frequency (in percentage of time) at which the device is being used, without considering the amount of capacity being utilized.

3.2 General advice: prioritizing saturation metrics

While nvidia-smi is a commonly used and convenient tool, it is not the optimal choice for performance measurements. For GPU applications in real deployment, it is recommended to utilize metrics based on DCGM, such as those provided by dcgm-exporter .

Furthermore, paying closer attention to saturation metrics can be beneficial. These metrics include FP64/FP32/FP16 activation, tensor core activation percentage, NVLINK bandwidth, GPU memory bandwidth percentage, and more.

Metrics from dcgm-exporter

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