8-Jan-2018: Pratyush, India's fastest supercomputer
India’s supercomputing prowess moved up several notches after it unveiled Pratyush, an array of computers that can deliver a peak power of 6.8 petaflops. One petaflop is a million billion floating point operations per second and is a reflection of the computing capacity of a system.
Pratyush is the fourth fastest supercomputer in the world dedicated for weather and climate research, and follows machines in Japan, USA and the United Kingdom. It will also move an Indian supercomputer from the 300s to the 30s in the Top500 list, a respected international tracker of the world’s fastest supercomputers.
The machines will be installed at two government institutes: 4.0 petaflops HPC facility at IITM, Pune; and 2.8 petaflops facility at the National Centre for Medium Range Weather Forecast, Noida.
The government had sanctioned ₹400 crore last year to put in place a 10-petaflop machine. A key function of the machine’s computing power would be monsoon forecasting using a dynamic model. This requires simulating the weather for a given month — say March — and letting a custom-built model calculate how the actual weather will play out over June, July, August and September.
With the new system, it would be possible to map regions in India at a resolution of 3 km and the globe at 12 km.
24-Dec-2017: New system can help machines think like humans
Scientists have developed a new type of neural network chip that can dramatically improve the efficiency of teaching machines to think like humans. The network is called- Reservoir Computing System.
Researchers from University of Michigan in the US created their system using memristors, which require less space and can be integrated more easily into existing silicon-based electronics. Memristors are a special type of resistive device that can both perform logic and store data.
Researchers used a special memristor that memorizes events only in the near history. Inspired by brains, neural networks are composed of neurons, or nodes, and synapses, the connections between nodes. To train a neural network for a task, a neural network takes in a large set of questions and the answers to those questions. In this process of what’s called supervised learning, the connections between nodes are weighted more heavily or lightly to minimise the amount of error in achieving the correct answer. Once trained, a neural network can then be tested without knowing the answer. For example, a system can process a new photo and correctly identify a human face, because it has learned the features of human faces from other photos in its training set.
Reservoir computing systems built with memristors can skip most of the expensive training process and still provide the network the capability to remember. This is because the most critical component of the system – the reservoir – does not require training. When a set of data is inputted into the reservoir, the reservoir identifies important time-related features of the data, and hands it off in a simpler format to a second network. This second network then only needs training like simpler neural networks, changing weights of the features and outputs that the first network passed on until it achieves an acceptable level of error.
The system can predict words before they are said during conversation, and help predict future outcomes based on the present.
24-Oct-2017: BadRabbit Ransomware Attacks Hit Russia, Ukraine
A ransomware attack has put a halt to business inside a handful of Russian media outlets and a number of major organizations in the Ukraine, including Kiev’s public transportation system and the country’s Odessa airport.
The attacks are known as Bad Rabbit and harken back to the ExPetr/NotPetya attacks of this summer which also concentrated in Ukraine and Russia, but instead spread wiper malware used in the Petya attacks of 2016.
This ransomware infects devices through a number of hacked Russian media websites. This has been a targeted attack against corporate networks, using methods similar to those used during the ExPetr attack.
ExPetr emerged in late June and was quickly scrutinized as more dangerous than WannaCry, which spread globally just a month earlier. Like WannaCry, the attackers behind ExPetr used the leaked NSA exploit EternalBlue to spread the malware. In the early hours of the attack, Danish shipping giants Maersk and Russian oil company Rosneft were reporting infections and impacts to their respective businesses. It was eventually determined that ExPetr was not a ransomware attack, but a wiper.