ABSTRACT
We speak of parallel computing whenever a number of “compute elements” (cores) solve a problem in a cooperative way. All modern supercomputer architectures depend heavily on parallelism, and the number of CPUs in large-scale supercomputers increases steadily. A common measure for supercomputer “speed” has been established by the Top500 list [W121], which is published twice a year and ranks parallel computers based on their performance in the LINPACK benchmark. LINPACK solves a dense system of linear equations of unspecified size. It is not generally accepted as a good metric because it covers only a single architectural aspect (peak performance). Although other, more realistic alternatives like the HPC Challenge benchmarks [W122] have been proposed, the simplicity of LINPACK and its ease of use through efficient open-source implementations have preserved its dominance in the Top500 ranking for nearly two decades now. Nevertheless, the list can still serve
as an important indicator for trends in supercomputing. The main tendency is clearly visible from a comparison of processor number distributions in Top500 systems (see Figure 4.1): Top of the line HPC systems do not rely on Moore’s Law alone for performance but parallelism becomes more important every year. This trend has been accelerating recently by the advent of multicore processors — apart from the occasional parallel vector computer, the latest lists contain no single-core systems any more (see also Section 1.4). We can certainly provide no complete overview on current parallel computer technology, but recommend the regularly updated Overview of recent supercomputers by van der Steen and Dongarra [W123].
