-------------------------- Tela/Matlab benchmark data -------------------------- Benchmark-1 Benchmark-2 Tela-1.12 SGI/R4400 115 s 16 IBM Power-2 86 7.6 Matlab-4.2a SGI/R4400 892 24 IBM Power-2 624 13 Tela/Matlab speed ratio SGI/R4400 7.75 1.5 IBM Power-2 7.25 1.7 IBM/SGI speed ratio Tela 1.3 2.1 Matlab 1.4 1.8 Both benchmarks are "real-world" examples provided by Ari.Viljanen@fmi.fi. Benchmark-1 is scalar code and benchmark-2 operates mainly on complex vectors. Notice that Tela/Matlab speed ratio is rather independent on architecture. On platform comparison, IBM is relatively faster on more vectorized computations, which is not surprising. ------------------------- Experiences in using Tela ------------------------- Case 1, provided by Ari.Viljanen@fmi.fi ======================================= There were 1300 lines of badly vectorizable Matlab code, some of it quite old. It was translated using m2t to tela. It took about 2 workdays to get the program running in Tela. Most of the time went to correcting calls to zeros function, which was translated incorrectly by m2t. This and some other bugs were afterwards fixed in m2t. CPU timings (seconds). The four machines are all Irises, simppu has R3000 processor, sumppu has R4000, and merta and nuotta have R4400 processors. The program does both computation and binary and ASCII I/O. Part of the computation uses complex numbers. merta nuotta simppu sumppu MatLab 26.7 27.6 85.2 39.4 Tela 10.7 10.3 39.9 15.7 Slight tuning of the Tela-code yielded still 20 percent improvement, but the above comparison is more fair one. Case 2, provided by Ari.Viljanen@fmi.fi ======================================= CPU timings (seconds). Nuotta is SGI R4400 (150/75 MHz), simppu is SGI R4600PC (without L2 cache), sumppu is R4000 (100/50 MHz). nuotta simppu sumppu MatLab 123 212 240 Tela 59 91 86