As existing large scale dynamic vision measurement systems based on large amount of data processing and computing have a lower data processing speed,a high speed and large scale dynamic vision measurement system was proposed. The parallel optimization techniques involving in the system such as target locating, code detecting, camera orientation and other systematic algorithms were researched. Firstly, time consumptions of corresponding key algorithms under different considerations were analyzed. Then, the traditional target centroid and code detecting algorithms were introduced, their parallelisms were analysis, and the fast target center location algorithm and code recognition algorithm under the general parallel architecture were put forward. Moreover, the implement methods of the two parallel algorithms were explained in detail. Finally, the Warp Atom Operation Optimization (WAOO) method for massive atomic operations was proposed. The experimental results under the same location precision and recognition rate show that the processing time is reduced by 42% and 91% for 300 and 20 000 targets respectively when the parallel algorithm is used to replace the traditional serial algorithm. The algorithms proposed in this paper are verified effective in accelerating the processing speed and improving the real-time problem in large scale vision measurement systems.