Skip to main content

VPC Peering Limitations


To create a VPC peering connection with another VPC, you need to be aware of the following limitations and rules:
  • You cannot create a VPC peering connection between VPCs that have matching or overlapping CIDR blocks.
  • You cannot create a VPC peering connection between VPCs in different regions.
  • You have a limit on the number active and pending VPC peering connections that you can have per VPC. For more information, see Amazon VPC Limits in the Amazon VPC User Guide.
  • VPC peering does not support transitive peering relationships; in a VPC peering connection, your VPC does not have access to any other VPCs that the peer VPC may be peered with. This includes VPC peering connections that are established entirely within your own AWS account. For more information about unsupported peering relationships, see Invalid VPC Peering Connection Configurations. For examples of supported peering relationships, see VPC Peering Scenarios.
  • You cannot have more than one VPC peering connection between the same two VPCs at the same time.
  • The Maximum Transmission Unit (MTU) across a VPC peering connection is 1500 bytes.
  • A placement group can span peered VPCs; however, you do not get full-bisection bandwidth between instances in peered VPCs. For more information about placement groups, see Placement Groups in the Amazon EC2 User Guide for Linux Instances.
  • Unicast reverse path forwarding in VPC peering connections is not supported. For more information, see Routing for Response Traffic.
Labels:

Comments

Post a Comment

Popular posts from this blog

What's replicated, what's not?

Logged operations are replicated. These include, but are not limited to: DDL DML Create/alter table space Create/alter storage group Create/alter buffer pool XML data. Logged LOBs Not logged operations are not replicated. These include, but are not limited to: Database configuration parameters (this allows primary and standby databases to be configured differently). "Not logged initially" tables Not logged LOBs UDF (User Defined Function) libraries. UDF DDL is replicated. But the libraries used by UDF (such as C or Java libraries)  are not replicated, because they are not stored in the database. Users must manually copy the libraries to the standby. Note: You can use database configuration parameter  BLOCKNONLOGGED  to block not logged operations on the primary.
Post a Comment
Read more

What is Tensor Parallelism and relationship between Buffer and GPU

  Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU .   It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...
Post a Comment
Read more

What is the benefit of using Quantization in LLM

Quantization is a technique used in LLMs (Large Language Models) to reduce the memory requirements for storing and training the model parameters. It involves reducing the precision of the model weights from 32-bit floating-point numbers (FP32) to lower precision formats, such as 16-bit floating-point numbers (FP16) or 8-bit integers (INT8). Bottomline: You can use Quantization to reduce the memory footprint off the model during the training. The usage of quantization in LLMs offers several benefits: Memory Reduction: By reducing the precision of the model weights, quantization significantly reduces the memory footprint required to store the parameters. This is particularly important for LLMs, which can have billions or even trillions of parameters. Quantization allows these models to fit within the memory constraints of GPUs or other hardware accelerators. Training Efficiency: Quantization can also improve the training efficiency of LLMs. Lower precision formats require fewer computati...
Post a Comment
Read more