Due to its irregular nature, string is an extremely good benchmark of the scalar performance of a computer. Hence, we timed it on several machines we had access to, yielding the numbers in Table 7.1. Note that we timed one processor of the parallel machines. We see immediately that the Sun 4/60, known as the SPARCstation 1, had the highest performance of the Suns we tested. Moreover, this machine (running with TI 8847 floating-point processor at clock rate of ) is as fast as the Motorola 88000 processor (at ) which is used in the TC2000 Butterfly. Turning to the hypercubes, we see that the nCUBE-2 is faster than the Meiko, which is twice as fast as the (scalar) Symult, which in turn is twice as fast as the nCUBE-1, per processor, for the string program. We have also run on the Weitek vector processors of the Mark III and Symult. The vector processors are faster than the scalar processors, but since string is entirely scalar, it does not run very efficiently on the vector processors and, hence, is still slower than on the Sun 4/60. The Mark III is as fast as the Symult, despite having one-third the clock rate, because it has a high-performance cache between its vector processor and memory. We have also timed the code on the new IBM and Hewlett-Packard workstations, and Cimarron Boozer of Sky Computers has optimized the code for the Intel i860. As a final comparison, the modern RISC workstations run the string code as fast as the CRAY X-MP.
Table 7.1: Time Taken to Execute the String Program
We should emphasize that these performances are for scalar codes. A completely different picture emerges for codes which vectorize well, like QCD. QCD, with dynamical fermions, runs on the CRAY X-MP at around and pure-gauge QCD runs on one processor of the Mark III at . In contrast, the Sun 4/60 only achieves about for pure-gauge QCD. This ratio of QCD performance (which we may claim as the ``realistic peak'' performance of the machines) 100:6:1 compares with 5:0.7:1 for strings. Thus, these two calculations from one area of physics illustrate clearly that the preferred computer architecture depends on the problem.