  Stochastic differential equations (SDEs) are widely used to model systems
affected by random processes. In general, the analysis of an SDE model requires
numerical solutions to be generated many times over multiple parameter
combinations. However, this process often requires considerable computational
resources to be practicable. Due to the embarrassingly parallel nature of the
task, 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}), a
software library that utilises such devices to calculate multiple orbits of an
SDE model. To evaluate the acceleration provided by SODECL, we compared the
time required to calculate multiple orbits of an exemplar stochastic model when
one 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 affected
execution time on different parallel compute devices.
  Our results show that when using all 32 CPU cores of a high-end
high-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 a
high-end GPU yielded accelerations of up to $\simeq$4.5, compared to a single
CPU core.
