Social Media posts

Overview

Social media refers to the means of interactions among people in which they create, share, and/or exchange information and ideas in virtual communities and networks. The Office of Communications and Marketing manages the main Facebook, Twitter, Instagram, LinkedIn and YouTube accounts.

Features


Finish First

by delivering the fastest time to solution

Solve

previously unsolvable challenges

Save

with the best performance ROI across workloads

Purpose


How it Works

A cloud graphics processing unit (GPU) provides hardware acceleration for an application, without requiring that a GPU is deployed on the user’s local device. Common use cases for cloud GPUs are:

  • Visualization workloads: Powerful server/desktop applications often employ graphically demanding content. Cloud GPUs can be used to accelerate video encoding, rendering, and streaming, as well as computer-aided design (CAD) applications.

  • Computational workloads: Large-scale mathematical modeling, deep learning, and analytics require the parallel processing abilities of general-purpose graphics processing unit (GPGPU) cores.


Technology

Augmented Reality. AR and mixed reality are some of the most popular social media application features. There are a number of use cases that social media houses experiment with when integrating AR with their applications but the one that has witnessed mass popularity is the use of face filters

Other Technology used are

  • AI Targeted Marketing.

  • Influencer Marketing.

  • Privacy and Security Features.

  • Augmented Reality.

  • Communication Mediums.

FAQs

What is computing acceleration?

Computing acceleration is used to perform floating-point computing and graphic processing with a hardware accelerator or a coprocessor, which is more efficient than using a software running on CPU. Tencent Cloud three two computing acceleration models: GPU computing (GN2, GN8) for generic computing, and GPU rendering GA2 for graphics-intensive applications.


What are the advantages of GPU over CPU?

GPU has more arithmetic logic units (ALU) than CPU and supports large-scale multi-threaded parallel computing.


When should I use GPU instances?

GPU instances are most suitable for parallel applications requiring high concurrency, such as workloads that use thousands of threads. When a great deal of computation is required for graphics processing where each task is relatively small, a group of operations to be performed form a pipeline. The throughput of this pipeline is more important than the latency of a single operation. To build an application that makes full use of this parallelism, you need to master the expertise of GPU devices, and to learn how to program for various graphical APIs (DirectX, OpenGL) or GPU computing programming models (CUDA, OpenCL).


How are GPU instances billed?

GPU instances are billed per usage. The bills are calculated down to the second and settled on an hourly basis. You can purchase and release the instances any time. GPU instances are applicable to scenarios where the demand for devices fluctuates dramatically, such as flash sale on an ecommerce site.


Applications