The world's first microprocessor and supercomputer have been created at the Bauman Moscow State Technical University (MSTU), in which a set of Discrete Mathematics Instruction Set computer commands is implemented at the hardware level. The Teragraph supercomputer is designed for storing and processing ultra-large-dimensional graphs and will be used for modeling biological systems, analyzing financial flows in real time, for storing knowledge in artificial intelligence systems and in other applied tasks.
Most important computational tasks require storing and processing huge arrays of discrete information. For efficient and parallel processing of sets, Bauman Moscow State Technical University has developed a unique Leonhard Euler microprocessor, which contains 24 specialized heterogeneous DISC Lnh64 cores. Leonhard takes over that part of the computational load that universal arithmetic microprocessors (for example, Intel or ARM) or graphics accelerators do not cope well with. The results of executing commands for processing sets or graphs from the Leonard Euler microprocessor are sent to the host system for further use during the computational process.
The Leonard Euler microprocessor takes up 200 times less crystal resources than a single Intel Xeon microprocessor, while consuming 10 times less energy. With a relatively low clock frequency of about 200 MHz, the performance of the Leonard Euler microprocessor significantly exceeds the performance of Intel Xeon family microprocessors (3 GHz). This is achieved due to parallelism in processing complex data models, which allows it to process up to 120 million graph vertices per second.
Scientists of Bauman Moscow State Technical University have built a Teragraph supercomputer based on Leonhard multicore microprocessors. It is capable of processing ultra-large-dimensional graphs up to one trillion vertices (10 to the 12th power). Technologies of representation and processing of knowledge in the form of graphs have already become a breakthrough for those industrial solutions in which other methods have shown low efficiency.
"The instruction set of our processor consists of actions such as adding elements to a set, searching in a set, crossing sets, searching for the nearest one, and a number of other operations. We have created a processor device that operates with huge sets, for example, containing billions of numeric keys. And with the help of a single intersection command, for example, we can create a new set, which is the result of the intersection of two initial sets," says Alexey Popov, chief processor developer, associate professor of Computer Systems and Networks at Bauman Moscow State Technical University.
Thanks to the ability to store information about various objects and phenomena and take into account the connections between them, knowledge graphs can be used in the analysis of big data in bioinformatics, medicine, urban security systems, computer networks, the financial sector, in the control of complex industrial production, for the analysis of social network information and in many other areas.
It is also worth mentioning the importance of hardware support for discrete mathematics, because most computational problems are discrete in nature, that is, they really require processing sets of numbers. These are numerous optimization problems, graph problems, machine learning problems. Of course, arithmetic processing, for example, comparing numbers, is also important, but it is only a small part of the actions in optimization algorithms. Modern computing systems spend most of their time searching for information, sorting through elements of sets, and similar actions.
That is why Leonhard, originally designed for discrete optimization tasks, works significantly faster than universal microprocessors designed for arithmetic processing. At the same time, the Bauman processor consumes much less electricity.
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