Accelerating biomedical signal processing algorithms with parallel programming on graphic processor units

This paper investigates the benefits derived by adopting the use of Graphics Processing Unit (GPU) parallel programming in the field of biomedical signal processing. The differences in execution time when computing the Correlation Dimension (CD) of multivariate neurophysiological recordings and the Skin Conductance Level (SCL) are reported by comparing several common programming environments. Moreover, as indicated in this study, the combination of parallel programming with special design techniques dealing with memory management issues such as data transfer between device memory and GPU may further accelerate the processing speed. So, the minimization achieved in the time execution by means of proper parallel architecture design may reach a factor of 29 in comparison with pure C language. Therefore, the role of parallel GPU programming environment may be beneficial for numerous biomedical applications within the sphere of biosignal processing.