High-performance computing (HPC) applications such as numerical simulation require a large number of CPUs for processing. To meet these needs, customers must buy a large-scale system that enables parallel processing so that the simulation can be completed in the shortest possible time. Such solutions are available in two forms: scale-up and scale-out.
Traditionally, scale-up customers have had no choice but to purchase high-cost, proprietary shared-memory symmetric multiprocessing (SMP) systems with proprietary operating systems. These systems require significant investment in system-level architecture by computer manufacturers. While SMP systems with up to eight processors can use off-the-shelf chipsets, systems with more processors are generally expensive solutions that use proprietary technology.
Then came the Beowulf project, which helped pave the path to an entirely new approach to SMP. The Beowulf cluster project pioneered the use of off-the-shelf, commodity computers running open source, Unix-like operating systems for HPC. With Beowulf clusters, there was a lower initial purchase price, open architecture and better performance than SMP systems running proprietary Unix.
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