This thesis is focused on possibility of constructing distributed underwater sonar which has an ability to increase or decrease number of sensor elements without any modification on its hardware. Each sonar module works individually. Its synchronization and communication between modules and the image processing unit are archived by using CAN protocol. This new design concept gives several advantages over conventional design.Only one prototype was made, but several modules must be combined in the actual application. The discussion about a complete system can be found in the future work section.The project composes of two main parts: design and building a prototype. The testing procedure and result of both parts are described and summarized with a conclusion.

In this master thesis, algorithms for acoustic simulations in underwater environments are ported for GPU processing. The GPU parallel computing platforms used are CUDA, OpenCL and SkePU. The purpose of this master thesis is to adapt and evaluate the ported algorithms' performance on two modern NVIDIA GPUs, Tesla K20 and Quadro K5000. Several optimizations, described in existing literature for GPU processing (e.g. usage of shared memory, coalesced memory accesses), are implemented and multiple versions of each algorithm are created to study their trade-offs. Evaluation on two GPUs showed that different versions of the same algorithm have different performance characteristic and execution with the best performing version can give better performance than the original algorithm executing on 8 CPUs. A performance comparison between CUDA, OpenCL and SkePU versions of one algorithm is also made.