BenchmarkTools already takes care of this for you. Println(estimate_pi(1000)) # to warm up fnīy the way, there’s no need to do this. In this case, the Python file is using the random library to calculate pi. This uses a popular method called the Monte Carlo method where you rely on random sampling to obtain certain results. Once entered, the program should return an estimate of pi. %timeit -n 10 pi_est = estimate_pi_numba_prange(nMC) The more you type in, the more accurate the output will be. Print(estimate_pi_numba_prange(1000)) # to warm up fn %timeit -n 10 pi_est = estimate_pi_numba(nMC) Print(estimate_pi_numba(1000)) # to warm up fn
# parallel estimate_pi_numba_prange(nMC): Y = (np.random.random() - 0.5) * diameter X = (np.random.random() - 0.5) * diameter Println(estimate_pi_thread(1000)) # to warm up pi_est = estimate_pi_thread(nMC2) Println(estimate_pi(1000)) # to warm up pi_est =estimate_pi(nMC2) # threaded version (think this is the right approach) of 7 runs, 10 loops each)ħ1.8 ms ± 6.15 ms per loop (mean ± std. I get the following running Julia (with export JULIA_NUM_THREADS = 4): 3.092ģ98.524 ms (7851017 allocations: 119.80 MiB)Īnd running Numba (parallel is speeding it up): 3.196ġ47 ms ± 11.9 ms per loop (mean ± std.
I’m still trying to wrap my head around all the different optimisations, Threads. However the threading for Julia seems to slow it down (thus Numba can be faster when in parallel). Julia, and expand it to parallel execution.įor non-parallel Julia is slightly faster, not as much as the post, but reasonably quicker. I’m running a simple Monte Carlo estimate of Pi.
Hello, I’m benchmarking Julia against some other langauges, mainly against Cython, Numba etc (in this case mainly Numba).
0 kommentar(er)
