Download Documentation Blog Community Learn Research JSoC Julia Micro-Benchmarks These micro-benchmarks, while not comprehensive, do test compiler performance on a range of common code patterns, such as function calls, string parsing, sorting, numerical loops, random number generation, recursion, and array operations. It is important to note that the benchmark codes are not written for absolute maximal performance (the fastest code to compute recursion_fibonacci(20) is the constant literal 6765). Instead, the benchmarks are written to test the performance of identical algorithms and code patterns implemented in each language. For example, the Fibonacci benchmarks all use the same (inefficient) doubly-recursive algorithm, and the pi summation benchmarks use the same for-loop. The “algorithm” for matrix multiplication is to call the most obvious built-in/standard random-number and matmul routines (or to directly call BLAS if the language does not provide a high-level matmul), except where a matmul/BLAS call is not possible (such as in JavaScript). Note that the Julia results depicted above do not include compile time. The vertical axis shows each benchmark time normalized against the C implementation. The benchmark data shown above were computed with Julia v1.0.0, SciLua v1.0.0-b12, Rust 1.27.0, Go 1.9, Java 1.8.0_17, Javascript V8 6.2.414.54, Matlab R2018a, Anaconda Python 3.6.3, R 3.5.0, and Octave 4.2.2. C and Fortran are compiled with gcc 7.3.1, taking the best timing from all optimization levels (-O0 through -O3). C, Fortran, Go, Julia, Lua, Python, and Octave use OpenBLAS v0.2.20 for matrix operations; Mathematica uses Intel® MKL. The Python implementations of matrix_statistics and matrix_multiply use NumPy v1.14.0 and OpenBLAS v0.2.20 functions; the rest are pure Python implementations. Raw benchmark numbers in CSV format are available here and the benchmark source code for each language can be found in the perf. files listed here. The plot is generated using this IJulia benchmarks notebook. These micro-benchmark results were obtained on a single core (serial execution) on an Intel® Core™ i7-3960X 3.30GHz CPU with 64GB of 1600MHz DDR3 RAM, running openSUSE LEAP 15.0 Linux. Other Relevant Benchmarks DataFrames: https://h2oai.github.io/db-benchmark/ Graphs: https://www.timlrx.com/blog/benchmark-of-popular-graph-network-packages-v2 CSV and Pandas: https://www.queryverse.org/benchmarks/ About Get Help Governance Publications Sponsors Downloads All Releases Source Code Current Stable Release Longterm Support Release PkgServer Status Documentation JuliaAcademy YouTube Getting Started FAQ Books Community Code of Conduct Diversity JuliaCon User/Developer Survey Shop Merchandise Contributing Contributor's Guide Issue Tracker Report a Security Issue Help Wanted Issues Good First Issue Dev Docs Built with Franklin.jl and the Julia Programming Language. We thank Fastly for their generous infrastructure support. ©2021 JuliaLang.org contributors. The content on this website is made available under the MIT license.