Analyzing the Loop Scheduling Mechanisms on Julia Multithreading

Julia is a quite recent dynamic language proposed to tackle the trade-off between productivity and efficiency. The idea is to provide the usability o flanguages such as Python or MATLAB side by sidewith the performance of C and C++. The support for multithreading programming in Julia was only released last year, and therefore still requires performance studies. In this work, we focus on the parallel loops and more specifically on the available mechanisms for assigning the loop iterations to the threads. We analyse the per-formance of the macros @spawn and @threads, used for loop parallelization. Our results show that there is no best fit solution for all cases. The use of @spawn provides better load balance for unbalanced loops with reasonably heavy iterations, but incurs in high overhead for workstealing. While @threads has low overhead, and workswell for loops with good balance among iterations.