GPU-accelerated ECSs

GPU-accelerated ECSs provide outstanding floating-point computing capabilities. They are suitable for applications that require real-time, highly concurrent massive computing.

GPU-accelerated ECS Types

P series
- Computing-accelerated P2s (recommended)
- Inference-accelerated Pi2 (recommended)

Helpful links:

**Table 1** GPU-accelerated ECSs
Type	Series	GPU	CUDA Cores per GPU	Single-GPU Performance	Application
Computing-accelerated	P2s	NVIDIA V100	5,120	14 TFLOPS of single-precision floating-point computing 7 TFLOPS of double-precision floating-point computing 112 TFLOPS Tensor Cores for deep learning acceleration	AI deep learning training, scientific computing, computational fluid dynamics, computational finance, seismic analysis, molecular modeling, and genomics.
Inference-accelerated	Pi2	NVIDIA T4 (GPU passthrough)	2,560	8.1 TFLOPS of single-precision floating-point computing 130 INT8 TOPS 260 INT4 TOPS	Machine learning, deep learning, inference training, scientific computing, seismic analysis, computing finance, rendering, multimedia encoding and decoding

Images Supported by GPU-accelerated ECSs

**Table 2** Images supported by GPU-accelerated ECSs
Type	Series	Supported Image
Computing-accelerated	P2s	CentOS 8.2 64bit CentOS 7.6 64bit Ubuntu 20.04 Server 64bit Ubuntu 18.04 Server 64bit
Inference-accelerated	Pi2	CentOS 8.2 64bit CentOS 7.6 64bit Ubuntu 20.04 Server 64bit Ubuntu 18.04 Server 64bit

Computing-accelerated P2s

Overview

P2s ECSs use NVIDIA Tesla V100 GPUs to provide flexibility, high-performance computing, and cost-effectiveness. P2s ECSs provide outstanding general computing capabilities and have strengths in AI-based deep learning, scientific computing, Computational Fluid Dynamics (CFD), computing finance, seismic analysis, molecular modeling, and genomics.

Specifications

**Table 3** P2s ECS specifications
Flavor	vCPUs	Memory (GiB)	Max./Assured Bandwidth (Gbit/s)	Max. PPS (10,000)	Max. NIC Queues	Max. NICs	GPUs	GPU Connection	GPU Memory (GiB)	Virtualization
p2s.2xlarge.8	8	64	10/4	50	4	4	1 × V100	PCIe Gen3	1 × 32 GiB	KVM
p2s.4xlarge.8	16	128	15/8	100	8	8	2 × V100	PCIe Gen3	2 × 32 GiB	KVM
p2s.8xlarge.8	32	256	25/15	200	16	8	4 × V100	PCIe Gen3	4 × 32 GiB	KVM
p2s.16xlarge.8	64	512	30/30	400	32	8	8 × V100	PCIe Gen3	8 × 32 GiB	KVM

P2s ECS Features

CPU: 2nd Generation Intel® Xeon® Scalable 6278 processors (2.6 GHz of base frequency and 3.5 GHz of turbo frequency), or Intel® Xeon® Scalable 6151 processors (3.0 GHz of base frequency and 3.4 GHz of turbo frequency)
Up to eight NVIDIA Tesla V100 GPUs on an ECS
NVIDIA CUDA parallel computing and common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
14 TFLOPS of single-precision computing and 7 TFLOPS of double-precision computing
NVIDIA Tensor cores with 112 TFLOPS of single- and double-precision computing for deep learning
Up to 30 Gbit/s of network bandwidth on a single ECS
32 GiB of HBM2 GPU memory with a bandwidth of 900 Gbit/s
Comprehensive basic capabilities
- User-defined network with flexible subnet division and network access policy configuration
- Mass storage, elastic expansion, and backup and restoration
- Elastic scaling
Flexibility
Similar to other types of ECSs, P2s ECSs can be provisioned in a few minutes.
Excellent supercomputing ecosystem
The supercomputing ecosystem allows you to build up a flexible, high-performance, cost-effective computing platform. A large number of HPC applications and deep-learning frameworks can run on P2s ECSs.

Supported Common Software

P2s ECSs are used in computing acceleration scenarios, such as deep learning training, inference, scientific computing, molecular modeling, and seismic analysis. If the software is required to support GPU CUDA, use P2s ECSs. P2s ECSs support the following commonly used software:

Common deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet
CUDA GPU rendering supported by RedShift for Autodesk 3dsMax and V-Ray for 3ds Max
Agisoft PhotoScan
MapD

Notes

After a P2s ECS is stopped, basic resources (including vCPUs, memory, image, and GPUs) are not billed, but its system disk is billed based on the disk capacity. If other products, such as EVS disks, EIP, and bandwidth are associated with the ECS, these products are billed separately.

Resources will be released after a P2s ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop the ECS or change its billing mode to yearly/monthly.
By default, P2s ECSs created using a public image have the Tesla driver installed.
If a P2s ECS is created using a private image, make sure that the Tesla driver was installed during the private image creation. If not, install the driver for computing acceleration after the ECS is created. For details, see Installing a Tesla Driver and CUDA Toolkit on a GPU-accelerated ECS.
GPU-accelerated ECSs differ greatly in general-purpose and heterogeneous computing power. Their specifications can only be changed to other specifications of the same instance type.

Inference-accelerated Pi2

Overview

Pi2 ECSs use NVIDIA Tesla T4 GPUs dedicated for real-time AI inference. These ECSs use the T4 INT8 calculator for up to 130 TOPS of INT8 computing. The Pi2 ECSs can also be used for light-load training.

Specifications

**Table 4** Pi2 ECS specifications
Flavor	vCPUs	Memory (GiB)	Max./Assured Bandwidth (Gbit/s)	Max. PPS (10,000)	Max. NIC Queues	Max. NICs	GPUs	GPU Memory (GiB)	Local Disks	Virtualization
pi2.2xlarge.4	8	32	10/4	50	4	4	1 × T4	1 × 16	N/A	KVM
pi2.4xlarge.4	16	64	15/8	100	8	8	2 × T4	2 × 16	N/A	KVM
pi2.8xlarge.4	32	128	25/15	200	16	8	4 × T4	4 × 16	N/A	KVM
pi2.16xlarge.4	64	256	30/30	400	32	8	8 × T4	8 × 16	N/A	KVM

Pi2 ECS Features

CPU: 2nd Generation Intel® Xeon® Scalable 6278 processors (2.6 GHz of base frequency and 3.5 GHz of turbo frequency), or Intel® Xeon® Scalable 6151 processors (3.0 GHz of base frequency and 3.4 GHz of turbo frequency)
Up to four NVIDIA Tesla T4 GPUs on an ECS
GPU hardware passthrough
Up to 8.1 TFLOPS of single-precision computing on a single GPU
Up to 130 TOPS of INT8 computing on a single GPU
16 GiB of GDDR6 GPU memory with a bandwidth of 320 GiB/s on a single GPU
One NVENC engine and two NVDEC engines embedded

Supported Common Software

Pi2 ECSs are used in GPU-based inference computing scenarios, such as image recognition, speech recognition, and natural language processing. The Pi2 ECSs can also be used for light-load training.

Pi2 ECSs support the following commonly used software:

Deep learning frameworks, such as TensorFlow, Caffe, PyTorch, and MXNet

Notes

After a Pi2 ECS is stopped, basic resources (including vCPUs, memory, image, and GPUs) are not billed, but its system disk is billed based on the disk capacity. If other products, such as EVS disks, EIP, and bandwidth are associated with the ECS, these products are billed separately.

Resources will be released after a Pi2 ECS is stopped. If resources are insufficient at the next start, the start may fail. If you want to use such an ECS for a long period of time, do not stop the ECS or change its billing mode to yearly/monthly.

Pi2 ECSs support automatic recovery when the hosts accommodating such ECSs become faulty.
By default, Pi2 ECSs created using a public image have the Tesla driver installed.
If a Pi2 ECS is created using a private image, make sure that the Tesla driver was installed during the private image creation. If not, install the driver for computing acceleration after the ECS is created. For details, see Installing a Tesla Driver and CUDA Toolkit on a GPU-accelerated ECS.
GPU-accelerated ECSs differ greatly in general-purpose and heterogeneous computing power. Their specifications can only be changed to other specifications of the same instance type.

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