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Stochastic differential equations (SDEs) are widely used to model systemsaffected by random processes. In general, the analysis of an SDE model requiresnumerical solutions to be generated many times over multiple parametercombinations. However, this process often requires considerable computationalresources to be practicable. Due to the embarrassingly parallel nature of thetask, devices such as multi-core processors and graphics processing units(GPUs) can be employed for acceleration.  Here, we present {\bf SODECL} (\url{https://github.com/avramidis/sodecl}), asoftware library that utilises such devices to calculate multiple orbits of anSDE model. To evaluate the acceleration provided by SODECL, we compared thetime required to calculate multiple orbits of an exemplar stochastic model whenone CPU core is used, to the time required when using all CPU cores or a GPU.In addition, to assess scalability, we investigated how the model size affectedexecution time on different parallel compute devices.  Our results show that when using all 32 CPU cores of a high-endhigh-performance computing node, the task is accelerated by a factor of up to$\simeq$6.7, compared to when using a single CPU core. Executing the task on ahigh-end GPU yielded accelerations of up to $\simeq$4.5, compared to a singleCPU core.

SODECL: An Open Source Library for Calculating Multiple Orbits of a System of Stochastic Differential Equations in Parallel