An equivalent Julia code to this small Python benchmark runs nearly 10x slower, even with higher levels of optimization enabled. import time def main(): N = 100000000 data = [] for i in range(N): value = i + 1 data.append(value) print(f'starting') time_start = time.time_ns() total = sum(data) time_stop = time.time_ns() print(f'finished') duration = time_stop - time_start duration = 1.0e-3 * duration print(f'time taken: {duration} us') print(f'total: {total}') if __name__ == '__main__': main() Here is the equivalent code in Julia. function main(ARGS) N = 100000000 data = Vector{Any}() for i in 1:N push!(data, i) end # throw 1 call away, JIT total = sum(data) println("starting") time_start = Base.time_ns() total = sum(data) time_stop = Base.time_ns() println("finished") duration = time_stop - time_start duration = 1.0e-3 * duration println("time taken: $(duration) us") println("total: $(total)") end main(ARGS) Python takes about 3 seconds on my machine. Julia takes about 20 seconds. Is there any particular reason for this or is it time to admit the over-hyped claims made about Julia’s performance are blatantly false? Close to C performance? I don’t think so.

Julia is slower than Python when appending elements to untyped arrays

Ian_L February 10, 2025, 7:12pm 41

This thread is…interesting…

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