What Is a GPU Server?
A GPU server, or Graphics Processing Unit server, is a specialized type of computer hardware designed to handle complex computational tasks efficiently. GPU servers are optimized to process data in parallel, thus making them suitable for AI tasks such as machine learning and deep learning.
The primary component that sets GPU servers apart from traditional CPU-based servers is the inclusion of one or more high-performance GPUs. These powerful processors enable rapid execution of mathematical operations required by AI algorithms and provide significant performance improvements over conventional CPUs.
NVIDIA and AMD are two leading manufacturers offering GPUs tailored for use in data centers and enterprise environments. Their products have become essential tools for MLOps teams, machine learning engineers, and data scientists working on cutting-edge AI projects.
This is part of a series of articles about Multi GPU.
- What Makes a GPU Server Different from Other Servers?
- What Applications or Workloads Are GPU Servers Used for?
- What are the Main Components of a GPU Server?
- Examples of GPU Server Products
- ~NVIDIA DGX A100
- ~HPE Apollo 6500 Gen10
- ~ASUS ESC8000 G4
- ~Dell EMC PowerEdge R740
- ~Lenovo ThinkSystem SR670 V2
- Best Practice to Manage your GPU Servers
- ~Understanding Workload Requirements
- ~Optimizing Resource Allocation
- ~Monitoring and Metrics Collection
- ~Optimize Power Settings for Energy Efficiency
- ~Automation and Orchestration
- Multi GPU Processing With Run:ai
What Makes a GPU Server Different from Other Servers?
Unlike traditional CPU-based servers that focus on sequential processing tasks, GPU servers are built for parallel processing. GPU servers are well-suited for handling the intricate math needed in AI, deep learning, and machine learning.
There are several factors that set GPU servers apart from other types of servers:
- Parallelism: GPUs consist of thousands of small cores optimized for simultaneous execution of multiple tasks. This enables them to process large volumes of data more efficiently than CPUs with fewer but larger cores.
- Floating-point Performance: The high-performance floating-point arithmetic capabilities in GPUs make them well-suited for scientific simulations and numerical computations commonly found in AI workloads.
- Data transfer speeds: Modern GPUs come equipped with high-speed memory interfaces like GDDR6 or HBM2 which allow faster data transfer between the processor and memory compared to conventional DDR4 RAM used by most CPU-based systems.
In addition to these hardware advantages, there's also an extensive ecosystem surrounding GPUs including software libraries such as CUDA, frameworks like TensorFlow and PyTorch, as well as specialized tools provided by companies like Run.ai for optimizing AI infrastructure utilization.
What Applications or Workloads Are GPU Servers Used for?
GPU servers excel in handling compute-intensive workloads, which makes them suitable for a wide range of applications. Some common use cases include:
- Machine learning and deep learning: GPUs provide the necessary parallel processing capabilities to handle large datasets and complex algorithms involved in training neural networks. All modern machine learning frameworks can take advantage of GPU acceleration.
- Data analytics: With their ability to process massive amounts of data quickly, GPUs enable faster insights from big data analytics platforms such as Apache Spark, Hadoop, and SQL databases that support GPU-accelerated queries.
- High performance computing (HPC): Scientific simulations, weather modeling, molecular dynamics research—all these computationally demanding tasks benefit from the high-performance capabilities offered by GPU servers.
- Cryptocurrency mining: The parallel processing power provided by GPUs is well-suited for solving cryptographic puzzles required in mining cryptocurrencies like Bitcoin and Ethereum.
- Gaming and Virtual Reality (VR): High-quality graphics rendering is crucial for gaming experiences and VR applications. A powerful GPU server ensures smooth performance with minimal latency issues.
What are the Main Components of a GPU Server?
A GPU server is specifically designed to handle complex computations and data processing tasks. Some of the main components of a typical GPU server include:
- Graphics Processing Unit (GPU): The heart of a GPU server is its graphics processing unit(s). These specialized processors excel at parallel processing, making them ideal for handling large-scale mathematical operations required in AI workloads. Examples include NVIDIA's Tesla or A100 GPUs.
- CPU: While the focus is on GPUs, the CPU still plays an important role in managing system resources and executing code not optimized for GPUs. High-end CPUs such as Intel Xeon or AMD EPYC are essential for the management of system resources and code execution, making them key components in GPU servers.
- Memory: Adequate memory capacity ensures smooth operation during intensive tasks like training neural networks. Both RAM and VRAM (video memory) should be considered when evaluating a GPU server's specifications.
- Data storage: Fast storage solutions like NVMe SSDs help reduce bottlenecks caused by slow data access speeds during computation-heavy processes.
In addition to these core components, it's crucial to consider factors such as cooling systems, power supply units with sufficient wattage output, and networking capabilities for distributed computing setups (such as InfiniBand switches).
Examples of GPU Server Products
NVIDIA DGX A100
The NVIDIA DGX A100 is an AI infrastructure server that delivers 5 petaFLOPS of computing power within a single system. A single DGX A100 offers eight NVIDIA A100 GPUs, which can be divided into seven parts each, resulting in 56 independent GPUs with their dedicated connection, memory, cache, and compute capabilities.
You can create large clusters of DGX A100 systems, with support for thousands of units. Scaling is possible by adding more DGX units and dividing each A100 GPU into seven logical GPUs using NVIDIA’s multi instance GPU (MIG) technology.
HPE Apollo 6500 Gen10
The HPE Apollo 6500 Gen10 System is an HPC and deep learning platform, delivering multiple NVIDIA Quadro RTX GPUs, fast GPU interconnects, a high-bandwidth communication fabric, and configurable GPU topologies to cater to different workloads.
The system is designed for reliability, availability, and serviceability (RAS), accommodating up to eight GPUs, NVLink for swift GPU-to-GPU communication, Intel Xeon Scalable processor support, and a selection of high-speed/low-latency fabrics. It is suitable not only for deep learning workloads but also for simulation and modeling tasks.
ASUS ESC8000 G4
The ASUS ESC8000 G4 Series supports up to eight high-performance NVIDIA Quadro or Tesla GPU cards within a 4U chassis.
An optimized internal structure supports dual-slot GPUs with active or passive thermal modules. ESC8000 G4 offers multiple system topology configuration options, including a single or dual root complex. The patented Adaptable Topology design makes it possible to switch system topology through ASUS ASMB9-iKVM's web-based GUI without modifying any hardware configuration.
The ASUS ESC8000 G4 Series has eight PCIe Gen3 x16 slots for full-height, full-length GPU cards, two PCIe Gen3 x16 slots for high-speed networking cards, and one internal PCIe Gen3 x8 slot for HBA/RAID cards.
Dell EMC PowerEdge R740
The PowerEdge R740 provides both multiple accelerator cards and highly scalable storage. It is a 2-socket, 2U platform, accommodating up to three 300W or six 150W GPUs or up to three double-width or four single-width FPGAs. It supports up to 16 2.5" drives or 8 3.5" disk drives. It is suitable both for machine learning workloads and VDI deployments.
Lenovo ThinkSystem SR670 V2
The Lenovo ThinkSystem SR670 V2 is a flexible GPU-rich 3U rack server that houses eight double-wide GPUs, including the latest NVIDIA A100 and A40 Tensor Core GPUs or the NVIDIA HGX A100 4-GPU option with NVLink and Lenovo Neptune hybrid liquid-to-air cooling. It is based on the new third-generation Intel Xeon Scalable processor family (previously "Ice Lake") and the new Intel Optane Persistent Memory 200 Series. The server is designed for AI, HPC, and graphical workloads.
Best Practice to Manage your GPU Servers
Understanding Workload Requirements
The first step in managing your GPU servers effectively is understanding your workload requirements. This involves analyzing the nature of your tasks and determining the amount of computational power required to execute them. Are you running simple, single-threaded applications or complex, multi-threaded ones? Do your tasks require heavy data processing or minimal processing? Understanding these factors can help you choose the right GPU server configuration that meets your needs.
In addition, understanding your workload requirements can help you optimize your server resources. For instance, if your tasks are data-intensive, you might need to allocate more storage space and memory to your GPU servers. On the other hand, if your tasks are compute-intensive, you might need to invest in servers with higher computational power.
Optimizing Resource Allocation
Resource allocation is another crucial aspect of managing GPU servers. It involves distributing your server resources - such as processing power, memory, and storage - in a way that maximizes efficiency and performance. Optimizing resource allocation can help you get the most out of your GPU servers, allowing you to perform tasks faster and more efficiently.
There are several ways to optimize resource allocation in GPU servers. For instance, you can use resource management tools that allow you to monitor and control resource usage in real-time. These tools can help you identify resource bottlenecks, allocate resources based on demand, and even automate resource allocation tasks.
Monitoring and Metrics Collection
Monitoring your GPU servers and collecting metrics is another best practice that can help you manage your servers effectively. By monitoring your servers, you can keep track of their performance, identify issues early, and take corrective action before they escalate.
There are several metrics you should be collecting from your GPU servers. These include CPU and GPU utilization, memory usage, temperature, power consumption, and network throughput, among others. These metrics can give you insight into the health and performance of your servers, allowing you to make informed decisions on how to optimize them.
Optimize Power Settings for Energy Efficiency
GPU servers are known for their high power consumption, which can increase operational costs and carbon footprint. By optimizing your server power settings, you can reduce power consumption, save costs, and contribute to environmental sustainability.
Optimizing power settings involves configuring your GPU servers to use the least amount of power while still delivering the required performance. This can be achieved through techniques such as dynamic voltage and frequency scaling (DVFS), which adjusts the voltage and frequency of your servers based on workload demand. You can also use power management tools that provide real-time feedback on power usage, enabling you to make immediate adjustments for optimal energy efficiency.
Automation and Orchestration
Finally, automation and orchestration are key to managing GPU servers efficiently. Automation involves using software to perform repetitive tasks, such as provisioning new servers, deploying applications, and updating software. Orchestration, on the other hand, involves coordinating and managing multiple automated tasks to achieve a specific outcome.
By automating and orchestrating your GPU server tasks, you can save time, reduce errors, and increase efficiency. For instance, you can automate the process of deploying new applications to your GPU servers, ensuring that they are rolled out quickly and consistently. You can also orchestrate complex workflows, such as training machine learning models, to run seamlessly across your GPU servers.
Multi GPU Processing With Run:ai
Run:ai automates resource management and workload orchestration for machine learning infrastructure. With Run:ai, you can automatically run as many deep learning experiments as needed on multi-GPU infrastructure.
Here are some of the capabilities you gain when using Run:ai:
- Advanced visibility—create an efficient pipeline of resource sharing by pooling GPU compute resources.
- No more bottlenecks—you can set up guaranteed quotas of GPU resources, to avoid bottlenecks and optimize billing.
- A higher level of control—Run:ai enables you to dynamically change resource allocation, ensuring each job gets the resources it needs at any given time.
Run:ai simplifies machine learning infrastructure pipelines, helping data scientists accelerate their productivity and the quality of their models.
Learn more about the Run:ai GPU virtualization platform.