The Future of Supercomputers and High-Performance Computing

Introduction

High-performance computing (HPC) is the use of computers and parallel processing techniques for solving complex computational problems. HPC is used in a wide variety of fields, from engineering (e.g. in the automotive industry for complex crash simulations), bioinformatics (e.g. for protein folding), ecology (e.g. complex Earth systems modeling), etc. Another important application for HPC today is co-called Big Data. The Big Data, in general, refers to the problems where enormous amount of data must be processed and analyzed in a short time.

The high-end HPC is performed on super computers. Those are computers build as a cluster of thousand or even millions of processing elements. They are specially designed to solve specific problems, where usually the same basic computation is repeated on million pieces of data. There, a speedup is achieved by executing the same instructions on multiple data at the same time. This approach is referred to as single instruction multiple data (SIMD), in contrast to the single instruction single data (SISD) approach used by general-purpose central processing units (CPUs). The supercomputers are enormous, expensive and have a huge power consumption. Therefore, only the biggest corporations can afford them. There is an official web site called Top500.org (Top500, 2014), where a list of five hundred top-ranked supercomputers is maintained. The list is updated every six months. In Table 1, the top 10 computers from this list are presented. The R_max factor represents the speed of the supercomputers. This is the highest score measured using the LINPACK benchmark suite and it is expressed in Pflops (number of 10¹⁵ floating-point instructions executed per seconds). In the column beside is the power consumption expressed in mega watts.