Category:High Performing Computing
High-performance computing (HPC) uses supercomputers and computer clusters to solve advanced computing problems. Today, computer systems are approaching the petafloaps-region but usually or the computer systems in the region of the terafloaps are counted as HPC-computers.
The term HPC is most commonly associated with computing used for scientific research although recently, HPC has come to be applied to business uses of cluster-based supercomputers.
HPC is sometimes used as a synonym for supercomputing; but in other contexts, "supercomputer" is used to refer to a more powerful subset of "high performance computers," and the term "supercomputing" becomes a subset of "high performance computing." The potentially confusing overlap of these usages is apparent.
Grid computing is a form of distributed computing whereby a super/virtual computer is composed of a cluster of networked, loosely-coupled computers, acting in concert to perform very large tasks. This technology has been applied to computationally-intensive scientific, mathematical, and academic problems through volunteer computing, and it is used in commercial enterprises for such diverse applications as drug discovery, economic forecasting, seismic analysis, and back-office data processing in support of e-commerce and web services.
What distinguishes grid computing from typical cluster computing systems is that grids tend to be more loosely coupled, heterogeneous, and geographically dispersed. Also, while a computing grid may be dedicated to a specialized application, it is often constructed with the aid of general purpose grid software libraries.
The primary advantage of distributed computing is that each node can be purchased as commodity hardware, which when combined can produce similar computing resources to a multiprocessor supercomputer, but at lower cost. This is due to the economies of scale of producing commodity hardware, compared to the lower efficiency of designing and constructing a small number of custom supercomputers. The primary performance disadvantage is that the various processors and local storage areas do not have high-speed connections. This arrangement is thus well-suited to applications in which multiple parallel computations can take place independently, without the need to communicate intermediate results between processors.