#Cpu speed accelerator mac keygen generator
The performance of MATLAB’s new generator is on-par with Julia’s although I’ll repeat that these timing tests are far far from rigorous.Ġ.052981 seconds (6 allocations: 76.294 MiB, 29.40% gc time) In Julia, if we set the seed to 1 as we did for MATLAB and ask for 5 random numbers, we get something different from MATLAB: julia> using RandomĠ.236033 0.346517 0.312707 0.00790928 0.48861 The MATLAB documentation does not tell us which algorithm their implementation is based on but it seems to be different from Julia’s. Gives the following results for a typical run on my Dell which supports AVX instructions number_of_randoms = So it’s clearly a different algorithm and, on CPUs that support the relevant instructions, it’s about twice as fast! Using my very unscientific test code: format compact This new Mersenne Twister implementation gives different random variates to the original implementation (which I d emonstrated is the same as Numpy’s implementation in an older post) as you might expect Just recently, I learned that MATLAB now has its own SIMD accelerated version of Mersenne Twister which can be activated like so: This reminded me of the one of the f irst times I played with the Julia language where I learned that Julia’s random number generator used a SIMD-accelerated implementation of Mersenne Twister called dSFMT to generate random numbers much faster than MATLAB’s Mersenne Twister implementation. In a recent blog post, Daniel Lemire explicitly demonstrated that vectorising random number generators using SIMD instructions could give useful speed-ups.
This is getting on for twice as fast as the GPU on my 2015 Apple MacbookPro which managed 349 Gigaflops….a machine I still use today although primarily for Netflix. The other result I find interesting with respect to my personal history of devices is the 622 Gigaflops result for single precision from the Intel i7 CPU. I stopped being upset about that years ago! At only slightly shy of 10.4 Teraflops that’s almost an order of magnitude faster than the results that impressed me on my laptop 4 years ago!ĭouble precision performance is nothing to write home about but it never is on consumer NVIDIA GPU cards. The standout figure is 10,399 Gigaflops for single precision Matrix-Matrix multiplication. This is the first desktop machine I’ve personally owned for almost two decades and the amount of performance I got for less money than the high spec laptops I used to favour is astonishing! Here are the GPU Bench results using MATLAB 2021a I swapped a 15 inch screen for a 49 inch Ultrawide monitor and my laptop is gathering dust while I enjoy my new Dell desktop with an 8 core Intel i7-11700 and an NVIDIA GTX 3070. The pandemic has confined me to my home office and high performance laptops don’t have quite the same allure that they did when I was doing most of my interesting computational work in airports and coffee shops. Double precision performance stank of course but I had gotten used to that with laptop GPUs!įast forward to 2021 and my personal computational landscape has changed substantially.
Just over 4 years ago I was very happy with the 1.2 Teraflops of single precision performance I measured on my then-new Dell XPS 95600 laptop using MATLAB’s GPU Bench and noted that its performance was on-par with the first supercomputer I supported professionally.
0 kommentar(er)
