Devectorize.jl

is the constant literal 6765). Rather, all of the benchmarks are written to test the performance of specific algorithms, expressed in a reasonable idiom in each language. ... The point of these benchmarks is to compare the performance of specific algorithms across language implementations, not to compare the fastest means of computing a result, which in most high-level languages relies on calling C code." We all know C compilers make fast machine code, so it seems kind of silly to tediously write apple-oranges benchmarks in ways which will usually just end up benchmarking language X FFI anyway. Excepting BLAS stuff (as noted) pure Julia is within 2x of C, and that is really the take-home message of the benchmarks. Here's why these small, hot-point benchmarks matter: the reasonable idiom in Julia is to rewrite slow algorithms in the same language. See this recent contribution for a great example of what is possible: https://github.com/lindahua/Devectorize.jl This combination of meta-programming expressiveness with speed in one language is really quite profound. Among other things, I think it will go a long way towards reducing the impedance-mismatch between "C core developers" and "end-user" communities in other technical computing environments (see NumPy,R). Julia has a beautiful, small, and stunningly accessible language implementation in C (isolated C++ for LLVM stuff); and it is certainly pragmatic by using BLAS,FFTW, etc. where appropriate --- but the numerical core is defined in Julia itself, and - IMHO - that is a very fundamental shift for building an open-source computing environment and community. (LuaJIT is a masterpiece and it would be cool to see it in the benchmarks, but there are many reasons why I personally have no interest in numerical work using Lua)