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High-performance computing (HPC) as a term arose after the term "supercomputing". [3] 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".
In computing, computer performance is the amount of useful work accomplished by a computer system. Outside of specific contexts, computer performance is estimated in terms of accuracy, efficiency and speed of executing computer program instructions. When it comes to high computer performance, one or more of the following factors might be involved:
[1] [2] Computing power of the top 1 supercomputer each year, measured in FLOPS. A supercomputer is a type of computer with a high level of performance as compared to a general-purpose computer. The performance of a supercomputer is commonly measured in floating-point operations per second instead of million instructions per second (MIPS).
There are many differences between high-throughput computing, high-performance computing (HPC), and many-task computing (MTC). HPC tasks are characterized as needing large amounts of computing power for short periods of time, whereas HTC tasks also require large amounts of computing, but for much longer times (months and years, rather than hours and days).
In computer architecture, speedup is a number that measures the relative performance of two systems processing the same problem. More technically, it is the improvement in speed of execution of a task executed on two similar architectures with different resources.
Bunyip was the first sub-US$ 1/MFLOPS computing technology. It won the Gordon Bell Prize in 2000. May 2000: $640 $1,132 KLAT2: KLAT2 was the first computing technology which scaled to large applications while staying under US$ 1/MFLOPS. [80] August 2003: $83.86 $138.9 KASY0 KASY0 was the first sub-US$ 100/GFLOPS computing technology. KASY0 ...
However, other systems such as the K computer continue to use conventional processors such as SPARC-based designs and the overall applicability of GPGPUs in general purpose high performance computing applications has been the subject of debate, in that while a GPGPU may be tuned to score well on specific benchmarks its overall applicability to ...
Meanwhile, performance increases in general-purpose computing over time (as described by Moore's law) tend to wipe out these gains in only one or two chip generations. [58] High initial cost, and the tendency to be overtaken by Moore's-law-driven general-purpose computing, has rendered ASICs unfeasible for most parallel computing applications.