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Windows Accelerated Computing Instances
If you require high processing capability, you'll benefit from using accelerated computing instances, which provide access to hardware-based compute accelerators such as Graphics Processing Units (GPUs) or Field Programmable Gate Arrays (FPGAs). Accelerated computing instances enable more parallelism for higher throughput on compute-intensive workloads.
GPU-based instances provide access to NVIDIA GPUs with thousands of compute cores. You can use GPU-based accelerated computing instances to accelerate scientific, engineering, and rendering applications by leveraging the CUDA or Open Computing Language (OpenCL) parallel computing frameworks. You can also use them for graphics applications, including game streaming, 3-D application streaming, and other graphics workloads.
If your application needs a small amount of additional graphics acceleration, but is better suited for an instance type with different compute, memory, or storage specifications, use an Elastic Graphics accelerator instead. For more information, see Amazon Elastic Graphics.
FPGA-based instances provide access to large FPGAs with millions of parallel system logic cells. You can use FPGA-based accelerated computing instances to accelerate workloads such as genomics, financial analysis, real-time video processing, big data analysis, and security workloads by leveraging custom hardware accelerations. You can develop these accelerations using hardware description languages such as Verilog or VHDL, or by using higher-level languages such as OpenCL parallel computing frameworks. You can either develop your own hardware acceleration code or purchase hardware accelerations through the AWS Marketplace.
Important
FPGA-based instances do not support Microsoft Windows.
You can cluster accelerated computing instances into a cluster placement group. Cluster placement groups provide low latency and high-bandwidth connectivity between the instances within a single Availability Zone. For more information, see Placement Groups.
Contents
- Accelerated Computing Instance Families
- Hardware Specifications
- Instance Performance
- Network Performance
- Instance Features
- Release Notes
- AMIs for GPU-Based Accelerated Computing Instances
- Installing the NVIDIA Driver on Windows
- Activate NVIDIA GRID Virtual Applications on G3 Instances
- Optimizing GPU Settings
For information about Linux accelerated computing instances, see Linux Accelerated Computing Instances in the Amazon EC2 User Guide for Linux Instances.
Accelerated Computing Instance Families
Accelerated computing instance families use hardware accelerators, or co-processors, to perform some functions, such as floating point number calculations, graphics processing, or data pattern matching, more efficiently than is possible in software running on CPUs. The following accelerated computing instance families are available for you to launch in Amazon EC2.
P3 Instances
P3 instances use NVIDIA Tesla V100 GPUs and are designed for general purpose GPU computing using the CUDA or OpenCL programming models or through a machine learning framework. P3 instances provide high-bandwidth networking, powerful half, single, and double-precision floating-point capabilities, and up to 32 GiB of memory per GPU, which makes them ideal for deep learning, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, genomics, rendering, and other server-side GPU compute workloads. Tesla V100 GPUs do not support graphics mode. For more information, see Amazon EC2 P3 Instances.
P3 instances support NVIDIA NVLink peer to peer transfers.
To view topology information about the system, run the following command:
nvidia-smi topo -m
For more information, see NVIDIA NVLink.
P2 Instances
P2 instances use NVIDIA Tesla K80 GPUs and are designed for general purpose GPU computing using the CUDA or OpenCL programming models. P2 instances provide high-bandwidth networking, powerful single and double precision floating-point capabilities, and 12 GiB of memory per GPU, which makes them ideal for deep learning, graph databases, high-performance databases, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, genomics, rendering, and other server-side GPU compute workloads.
P2 instances support NVIDIA GPUDirect peer to peer transfers.
To view topology information about the system, run the following command:
nvidia-smi topo -m
For more information, see NVIDIA GPUDirect.
G4 Instances
G4 instances use NVIDIA Tesla GPUs and provide a cost-effective, high-performance platform for general purpose GPU computing using the CUDA or machine learning frameworks along with graphics applications using DirectX or OpenGL. G4 instances provide high- bandwidth networking, powerful half and single-precision floating-point capabilities, along with INT8 and INT4 precisions. Each GPU has 16 GiB of GDDR6 memory, making G4 instances well-suited for machine learning inference, video transcoding, and graphics applications like remote graphics workstations and game streaming in the cloud.
G4 instances support NVIDIA GRID Virtual Workstation. For more information, see NVIDIA Marketplace offerings.
G3 Instances
G3 instances use NVIDIA Tesla M60 GPUs and provide a cost-effective, high-performance platform for graphics applications using DirectX or OpenGL. G3 instances also provide NVIDIA GRID Virtual Workstation features, such as support for four monitors with resolutions up to 4096x2160, and NVIDIA GRID Virtual Applications. G3 instances are well-suited for applications such as 3D visualizations, graphics-intensive remote workstations, 3D rendering, video encoding, virtual reality, and other server-side graphics workloads requiring massively parallel processing power.
G3 instances support NVIDIA GRID Virtual Workstation and NVIDIA GRID Virtual Applications. To activate either of these features, see Activate NVIDIA GRID Virtual Applications on G3 Instances.
G2 Instances
G2 instances use NVIDIA GRID K520 GPUs and provide a cost-effective, high-performance platform for graphics applications using DirectX or OpenGL. NVIDIA GRID GPUs also support NVIDIA’s fast capture and encode API operations. Example applications include video creation services, 3D visualizations, streaming graphics-intensive applications, and other server-side graphics workloads.
Hardware Specifications
The following is a summary of the hardware specifications for accelerated computing instances.
| Instance type | Default vCPUs | Memory (GiB) | Accelerators |
|---|---|---|---|
p2.xlarge |
4 | 61 | 1 |
p2.8xlarge |
32 | 488 | 8 |
p2.16xlarge |
64 | 732 | 16 |
p3.2xlarge |
8 | 61 | 1 |
p3.8xlarge |
32 | 244 | 4 |
p3.16xlarge |
64 | 488 | 8 |
p3dn.24xlarge |
96 | 768 | 8 |
g2.2xlarge |
8 | 15 | 1 |
g2.8xlarge |
32 | 60 | 4 |
g3s.xlarge |
4 | 30.5 | 1 |
g3.4xlarge |
16 | 122 | 1 |
g3.8xlarge |
32 | 244 | 2 |
g3.16xlarge |
64 | 488 | 4 |
g4dn.xlarge |
4 | 16 | 1 |
g4dn.2xlarge |
8 | 32 | 1 |
g4dn.4xlarge |
16 | 64 | 1 |
g4dn.8xlarge |
32 | 128 | 1 |
g4dn.12xlarge |
48 | 192 | 4 |
g4dn.16xlarge |
64 | 256 | 1 |
f1.2xlarge |
8 | 122 | 1 |
f1.4xlarge |
16 | 244 | 2 |
f1.16xlarge |
64 | 976 | 8 |
For more information about the hardware specifications for each Amazon EC2 instance type, see Amazon EC2 Instance Types.
For more information about specifying CPU options, see Optimizing CPU Options.
Instance Performance
EBS-optimized instances enable you to get consistently high performance for your EBS volumes by eliminating contention between Amazon EBS I/O and other network traffic from your instance. Some accelerated computing instances are EBS-optimized by default at no additional cost. For more information, see Amazon EBS–Optimized Instances.
Network Performance
You can enable enhanced networking capabilities on supported instance types. Enhanced networking provides significantly higher packet-per-second (PPS) performance, lower network jitter, and lower latencies. For more information, see Enhanced Networking on Windows.
Instance types that use the Elastic Network Adapter (ENA) for enhanced networking deliver high packet per second performance with consistently low latencies. Most applications do not consistently need a high level of network performance, but can benefit from having access to increased bandwidth when they send or receive data. Instance sizes that use the ENA and are documented with network performance of "Up to 10 Gbps" or "Up to 25 Gbps" use a network I/O credit mechanism to allocate network bandwidth to instances based on average bandwidth utilization. These instances accrue credits when their network bandwidth is below their baseline limits, and can use these credits when they perform network data transfers.
The following is a summary of network performance for accelerated computing instances that support enhanced networking.
| Instance type | Network performance | Enhanced networking |
|---|---|---|
|
|
Up to 10 Gbps |
|
|
|
10 Gbps |
|
|
|
Up to 25 Gbps |
|
|
|
25 Gbps |
|
|
|
50 Gbps |
|
|
|
100 Gbps |
Instance Features
The following is a summary of features for accelerated computing instances.
| EBS only | NVMe EBS | Instance store | Placement group | |
|---|---|---|---|---|
|
G2 |
No |
No |
SSD |
Yes |
|
G3 |
Yes |
No |
No |
Yes |
|
G4 |
No |
Yes |
NVMe * |
Yes |
|
P2 |
Yes |
No |
No |
Yes |
|
P3 |
All other sizes: Yes |
All other sizes: No |
|
Yes |
|
F1 |
No |
No |
NVMe * |
Yes |
* The root device volume must be an Amazon EBS volume.
For more information, see the following:
Release Notes
-
You must launch the instance using an HVM AMI.
-
The following are requirements for G4 instances:
-
NVMe drivers must be installed. EBS volumes are exposed as NVMe block devices.
-
Elastic Network Adapter (ENA) drivers must be installed.
The following AMIs meet these requirements:
-
Amazon Linux 2
-
Amazon Linux AMI 2018.03
-
Ubuntu 14.04 (with
linux-awskernel) or later -
Red Hat Enterprise Linux 7.4 or later
-
SUSE Linux Enterprise Server 12 SP2 or later
-
CentOS 7.4.1708 or later
-
FreeBSD 11.1 or later
-
-
GPU-based instances can't access the GPU unless the NVIDIA drivers are installed. For more information, see Installing the NVIDIA Driver on Windows.
-
There is a limit of 100 AFIs per region.
-
There is a limit on the number of instances that you can run. For more information, see How many instances can I run in Amazon EC2? in the Amazon EC2 FAQ. To request an increase in these limits, use the following form: Request to Increase Amazon EC2 Instance Limit.
-
If you launch a multi-GPU instance with a Windows AMI that was created on a single-GPU instance, Windows does not automatically install the NVIDIA driver for all GPUs. You must authorize the driver installation for the new GPU hardware. You can correct this manually in the Device Manager by opening the Other device category (the inactive GPUs do not appear under Display Adapters). For each inactive GPU, open the context (right-click) menu, choose Update Driver Software, and then choose the default Automatic Update option.
-
When using Microsoft Remote Desktop Protocol (RDP), GPUs that use the WDDM driver model are replaced with a non-accelerated Remote Desktop display driver. To access your GPU hardware, you must use a different remote access tool, such as Teradici Cloud Access Software, NICE Desktop Cloud Visualization (DCV), or VNC. You can also use one of the GPU AMIs from the AWS Marketplace because they provide remote access tools that support 3D acceleration.
AMIs for GPU-Based Accelerated Computing Instances
To help you get started, NVIDIA and others provide AMIs for GPU-based accelerated computing instances. These reference AMIs include the NVIDIA driver, which enables full functionality and performance of the NVIDIA GPUs.
For a list of AMIs with the NVIDIA driver, search AWS Marketplace as follows:
You can launch accelerated computing instances using any HVM AMI.
Important
These AMIs include drivers, software, or toolkits that are developed, owned, or provided by NVIDIA Corporation. By using these AMIs, you agree to use these NVIDIA drivers, software, or toolkits only on Amazon EC2 instances that include NVIDIA hardware.
You can also install the NVIDIA driver. For more information, see Installing the NVIDIA Driver on Windows.
