Context:
Facebook-parent Meta announced that it is building an AI supercomputer, the AI Research SuperCluster (RSC). The company said that this will be the fastest supercomputer in the world once fully built by mid-2022. The device is said to accelerate AI research and help in building the metaverse, the next major computing platform.
Relevance:
GS III- Science and Technology
Dimensions of the Article:
- What are supercomputers?
- What is Artificial Intelligence (AI)?
- What is the RSC?
- Current challenges
What are supercomputers?
- A supercomputer can perform high-level processing at a faster rate when compared to a normal computer.
- Supercomputers are made up of hundreds or thousands of powerful machines which use better artificial intelligence (AI) models to improve operations that process huge amounts of data in less time than normal computers.
- They work together to perform complex operations that are not possible with normal computing systems. Supercomputers require high-speed and specialised chip architectures. The chip performs 660 operations per cycle and thus run up to 230 gigaflops at 350 MHz.
- AI supercomputers are built by combining multiple graphic processing units (GPUs) into compute nodes, which are then connected by a high-performance network fabric to allow fast communication between those GPUs.
What is Artificial Intelligence (AI)?
- Artificial intelligence is the branch of computer science concerned with making computers behave like humans.
- AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making.
- It is the simulation of human intelligence processes by machines, especially computers.
- It refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making and execute tasks in real time situations without constant supervision.
- Particular applications of AI include expert systems, speech recognition and machine vision.
What is the RSC?
- Meta considers the RSC as a powerful supercomputer capable of quintillions of operations per second.
- It can perform tasks like translating text between languages and help identify potentially harmful content on Meta’s platform.
- The RSC, compared with Meta’s legacy production and research infrastructure, can run computer vision workflows up to 20 times faster, and train large-scale natural language processing (NLP) models three times faster.
- Meta estimates that a model with billions of parameters can finish training in three weeks, compared to the nine weeks it was before.
- RSC’s storage tier has 175 petabytes of Pure Storage FlashArray, 46 petabytes of cache storage in Penguin Computing Altus systems, and 10 petabytes of Pure Storage FlashBlade.
What changes can the RSC bring about?
RSC will help its researchers build better AI models that can learn from trillions of examples,
- Work across hundreds of different languages,
- Seamlessly analyse text, images and video together,
- Power real-time voice translations to large groups of people speaking different languages so that they can Collaborate on a research project or play an AR game together,
- Develop new augmented reality tools.
- Researchers will be able to train the largest models needed to develop advanced AI for computer vision, speech recognition.
Current challenges
- The company however reckons that to fully realise the benefits of advanced AI— self-supervised learning of various domains whether vision, speech, language —will require training large and complex models for critical use cases like identifying harmful content on Meta’s platform.
- Computer vision, for example, needs to process larger, longer videos with higher data sampling rates.
- Speech recognition needs to work well even in challenging conditions with a lot of background noise and needs to understand more languages, dialects, and accents.
- The company therefore decided that the best way to accelerate progress was to design a new computing infrastructure, RSC.
- There are very large-scale scientific problems that need the right level of depth, accuracy and speed, like modelling all the climate change phenomena, which cannot be handled with the current generation of supercomputers.
-Source: The Hindu
