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2.7 Performance Models     continued...

We conclude this section with a few remarks on some metrics that are commonly used to describe the performance of computer systems. MIPS stands for ``millions of instructions per second.'' With the variation in instruction styles, internal organization, and number of processors per system it is almost meaningless for comparing two systems. As a point of reference, the DEC VAX 11/780 executed approximately one million instructions per second. You may see a system described as having performance rated at ``X VAX MIPS.'' This is a measure of performance normalized to VAX 11/780 performance. What this means is someone ran a program on the VAX, then ran the same program on the other system, and the ratio is X. The term ``native MIPS'' refers to the number of millions of instructions of the machine's own instruction set that can be executed per second.

MFLOPS (pronounced ``megaflops'') stands for ``millions of floating point operations per second.'' This is often used as a ``bottom-line'' figure. If you know ahead of time how many operations a program needs to perform, you can divide the number of operations by the execution time to come up with a MFLOPS rating. For example, the standard algorithm for multiplying matrices requires operations ( inner products, with n multiplications and n - 1 additions in each product). Suppose you compute the product of two matrices in 0.35 seconds. Your computer achieved

Obviously this type of comparison ignores the overhead involved in setting up loops, checking terminating conditions, and so on, but as a ``bottom line'' it gets to the point: what you care about (in this example) is how long it takes to multiply two matrices, and if that operation is a major component of your research it makes sense to compare machines by how fast they can multiply matrices. A standard set of reference programs known as LINPACK (linear algebra package) is often used to compare systems based on their MFLOPS ratings by measuring execution times for Gaussian elimination on matrices [8].