Computer performance

Depending on the context, good computer performance may involve one or more of the following:

• Short response time for a given piece of work
• High throughput (rate of processing work)
• Low utilization of computing resource(s)
• High availability of the computing system or application
• Fast (or highly compact) data compression and decompression
• High bandwidth / short data transmission time

Performance metrics

Computer performance metrics include availability, response time, channel capacity, latency, completion time, service time, bandwidth, throughput, relative efficiency, scalability, performance per watt, compression ratio, Instruction path length and speed up.

Aspect of software quality

Computer software performance, particularly software application response time, is an aspect of software quality that is important in human–computer interactions.

Technical and non-technical definitions

The performance of any computer system can be evaluated in measurable, technical terms, using one or more of the metrics listed above. This way the performance can be

- compared relative to other systems or the same system before/after changes
- defined in absolute terms, e.g. for fulfilling a contractual obligation

Whilst the above definition relates to a scientific, technical approach, the following definition given by Arnold Allen would be useful for a non-technical audience:

The word performance in computer performance means the same thing that performance means in other contexts, that is, it means "How well is the computer doing the work it is supposed to do?"

Technical performance metrics

There are a wide variety of technical performance metrics that indirectly affect overall computer performance. Occasionally a CPU designer can find a way to make a CPU with better overall performance by improving one of these technical performance metrics without sacrificing any other (relevant) technical performance metric -- for example, building the CPU out of better, faster transistors. However, sometimes pushing one technical performance metric to an extreme leads to a CPU with worse overall performance, because other important technical performance metrics were sacrificed to get one impressive-looking number -- for example, the Megahertz myth.

The total amount of time (t) required to execute a particular benchmark program is

$t=N*C/f$

• N is the number of instructions actually executed (the instruction path length). The code density of the instruction set strongly affects N. The value of N can either be determined exactly by using an Instruction Set Simulator (if available) or by estimation - itself based partly on estimated or actual frequency distribution of input variables and by examining generated machine code from an HLL compiler. It cannot be determined from the number of lines of HLL source code. N is not affected by other processes running on the same processor. The significant point here is that hardware normally does not keep track of (or at least make easily available) a value of N for executed programs. The value can therefore only be accurately determined by instruction set simulation, which is rarely practised.
• f is the clock frequency in cycles per second.
• C is the average Cycles Per Instruction (CPI) for this benchmark.

Even on one machine, a different compiler or the same compiler with different compiler optimization switches can change N and CPI -- the benchmark executes faster if the new compiler can improve N or C without making the other worse, but often there is a tradeoff between them -- is it better, for example, to use a few complicated instructions that take a long time to execute, or to use instructions that execute very quickly, although it takes more of them to execute the benchmark?

A CPU designer is often required to implement a particular instruction set, and so cannot change N. Sometimes a designer focuses on improving performance by making significant improvements in f (with techniques such as deeper pipelines, faster caches), while (hopefully) not sacrificing too much C -- leading to a speed-demons CPU design. Sometimes a designer focuses on improving performance by making significant improvements in CPI (with techniques such as out-of-order execution, superscaler CPUs, larger caches, caches with improved hit rates, improved branch prediction, speculative execution, etc), while (hopefully) not sacrificing too much clock frequency -- leading to a brainiac CPU design.

See also

• Algorithmic efficiency
• Benchmark (computing)
• Computer architecture
• Network performance
• Optimization (computer science)
• Performance analysis
• Performance engineering
• Performance management
• Performance testing
• Performance tuning

References