Clustering 관련된 세미나를 학교에서 하려고 하는데요. Testing

SPEC -- probably the "best" of the application-level benchmark suites.
Fairly tight rules, but deep-pocketed vendors doubtless maintain an edge beyond just having decent hardware.

lmbench -- I think without question the best of the microbenchmark suites. If you want to find out how fast the CPU does any basic operation, this is probably the first place to look. This suite is heavily used by the linux kernel developers including Linus Himself because it provides accurate and reproducible timings of things like interrupt handling rates, context switch rates, memory latency and bandwidth, and some selected CPU operational rates.

stream -- If you are interested in CPU-memory combined rates in operations on streaming vectors (e.g. copy, add, multiply-add) stream is the microbenchmark of choice. Its one weakness is that it doesn't provide one (easily) with a picture of rates as a function of vector size, so that one cannot observe the variation as one increases the vector size across the various CPU cache sizes. It is therefore better suited (as a predictor of application performance) for people running large applications involving linear algebra than for people operating on small blocks of data. Oh, and another weakness is that it doesn't provide any measure that includes the division operation. This is important because some code REQUIRES division in a streaming context or otherwise, and division is often several times slower than multiplication.

linpack -- Another linear algebra type benchmark. Not terribly relevant at the application level any more, and a bit too complex to be a microbenchmark -- IMHO this is a benchmark that could be retired without anyone really missing it for practical reasons. However, it is has been around a long time and there is a fair bit of data derived from it. When someone tells you how many "MFLOPS" a system has, they are probably referring to Linpack MFLOPS. Historically, this has been a highly misleading predictor of relative systems performance at the application level and has also proven relatively easy to "cheat" on a bit at the hardware and software level, but there it is.

savage -- This is a nearly forgotten benchmark that measures how fast a system does transcendental function evaluations (e.g. sin, tan).
These are typically library calls, but some CPUs have had them built into microcode so that they execute several times faster (typically) than library code. Libaries can also exhibit some variation depending on the algorithms used for evaluation.