Tomographic particle image velocimetry (Tomo PIV) is a new method to study gas and liquid flow. Tomo PIV allows measuring the three-dimensional flow characteristics in the measurement volume. One of the main problems of this measurement method is the high computing resource demands to its data processing algorithms. Several approaches to increase the computational performance of this method using high performance computing platforms, such as graphic processing units (GPU) and compute clusters, are discussed. Some problems connected porting the tomographic reconstruction and correlation algorithms onto many-core computing platforms and the proposed solutions are also discussed. The practical results of porting the tomographic reconstruction algorithm to GPU using OpenCL technology are described. The time estimates obtained experimentally for data processing are given for various computing platforms.

Keywords: tomography, particle image velocimetry, Tomo PIV, GPU, computing cluster, OpenCL, MPI, high performance computing

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