"Supercomputer: A supercomputer is a computer with a high level of performance as compared to a general-purpose computer."
The use of the most powerful and advanced computers to process complex scientific computations and simulations.
Parallel Programming: Programming parallel computers is a critical requirement for supercomputing. It involves splitting large data sets or problems into smaller parts and executing them simultaneously on multiple processors.
Distributed Systems: Supercomputing doesn't work on a single system alone but involves the coordination of multiple systems. Distributed systems deal with distributing data, jobs, or applications across these systems.
Operating Systems: Different operating systems provide varying degrees of support for parallel computing. Understanding the basics of both Linux and Windows can prepare you for developing supercomputing applications.
High-Performance Computing Architecture: Understanding the architecture of high-performance computing is necessary when working with large-scale systems. Topics that need to be covered include hardware components, system interconnects, and memory hierarchies.
Grid Computing: Grid computing is a subset of distributed computing that deals with coordinating different resources within supercomputing to perform a set of tasks.
Scientific Computing: This is the use of mathematical modeling, simulation, and analysis to solve scientific problems. It involves applying numerical methods and algorithms to scientific data.
Graphics Processing Units (GPU): Graphics processing units have become more popular in high-performance computing due to their unique structure and high processing power. GPU programming requires advanced knowledge of parallel programming.
Machine Learning: Machine learning techniques use large datasets that require immense processing power to be efficient. Learning these techniques can leverage supercomputing benefits from them.
Cloud Computing: Cloud computing is becoming increasingly popular in supercomputing. It involves using resources provided by remote servers on a pay-per-use basis.
Quantum Computing: Quantum computers can process exponentially more data than classical supercomputers. Learning about their technology and how it could be applied to supercomputing can make you proficient in future technology.
Message Passing Interface (MPI): MPI is a communication protocol used in parallel computing. It allows programs running on different processors to communicate with each other.
OpenMP: It is an API that supports parallel programming in C, C++, and Fortran. OpenMP simplifies programming and improves application performance by allowing the computation to divide between multiple threads.
High-Performance Data Analysis: The development of big data has led to the demand for high-performance data analysis. The topic deals with processing large amounts of data and extracting useful information from it.
High-Performance Computing Applications: Applications developed for supercomputing can leverage high performance to achieve significant results. You need to have an overview of how applications exploit supercomputing to develop better software.
Computational Science: Computational science is the use of simulation, analysis, and modeling to solve scientific problems. It involves the application of algorithms and numerical methods to large-scale data sets.
Vector processing supercomputers: These supercomputers process data as vectors, creating greater efficiency and speed.
Shared-memory supercomputers: These supercomputers share the same memory space for multiple processors, allowing for efficient data communication between processors.
Distributed-memory supercomputers: These supercomputers have separate memory spaces for each processor, and each processor requests and communicates data with other processors when needed.
Massively parallel supercomputers: These supercomputers use thousands or even millions of processors to work on parallel tasks, providing high performance and speed.
Grid computing supercomputers: These supercomputers use distributed computing across many networked computers to perform complex computational tasks.
Cloud-based supercomputers: These supercomputers use cloud computing to provide scalable and cost-effective high-performance computing, particularly for big data processing.
Quantum computing: These supercomputers are based on quantum mechanics and use quantum bits, or qubits, to perform calculations thousands of times faster than traditional computing.
Neuromorphic computing: These supercomputers are designed to mimic the structure and function of the human brain, providing more efficient and versatile computing power.
Exascale supercomputing: These supercomputers are capable of processing one exaflop of data or more, representing a significant milestone in high-performance computing.
Artificial intelligence supercomputers: These supercomputers are designed to provide efficient and large-scale data processing for deep learning and other AI applications.
"The performance of a supercomputer is commonly measured in floating-point operations per second (FLOPS) instead of million instructions per second (MIPS)."
"For comparison, a desktop computer has performance in the range of hundreds of gigaFLOPS (1011) to tens of teraFLOPS (1013)."
"Since November 2017, all of the world's fastest 500 supercomputers run on Linux-based operating systems."
"Additional research is being conducted in the United States, the European Union, Taiwan, Japan, and China to build faster, more powerful and technologically superior exascale supercomputers."
"Supercomputers play an important role in the field of computational science and are used for a wide range of computationally intensive tasks in various fields, including quantum mechanics, weather forecasting, climate research, oil and gas exploration, molecular modeling, and physical simulations."
"Supercomputers were introduced in the 1960s, and for several decades, the fastest was made by Seymour Cray at Control Data Corporation (CDC), Cray Research, and subsequent companies."
"In the 1970s, vector processors operating on large arrays of data came to dominate. A notable example is the highly successful Cray-1 of 1976."
"From then until today, massively parallel supercomputers with tens of thousands of off-the-shelf processors became the norm."
"The US has long been the leader in the supercomputer field."
"As of May 2022, the fastest supercomputer on the TOP500 supercomputer list is Frontier, in the US."
"Frontier... with a LINPACK benchmark score of 1.102 ExaFlop/s."
"The US has five of the top 10 [supercomputers]."
"Japan, Finland, and France have one [supercomputer] each."
"In June 2018, all combined supercomputers on the TOP500 list broke the 1 exaFLOPS mark."
"They are used for a wide range of computationally intensive tasks... including quantum mechanics."
"Supercomputers... are used for... weather forecasting, climate research."
"Supercomputers... are used for... oil and gas exploration."
"[Simulations] such as simulations of the early moments of the universe, airplane and spacecraft aerodynamics, the detonation of nuclear weapons, and nuclear fusion."
"[Supercomputers] have been essential in the field of cryptanalysis." (No specific quote given.)