Although CUDA kernel launches are asynchronous, all GPU-related tasks placed in one stream (which is the default behavior) are executed sequentially.
So, for example,
kernel1<<<X,Y>>>(...); // kernel start execution, CPU continues to next statement kernel2<<<X,Y>>>(...); // kernel is placed in queue and will start after kernel1 finishes, CPU continues to next statement cudaMemcpy(...); // CPU blocks until memory is copied, memory copy starts only after kernel2 finishes
So in your example, there is no need for cudaDeviceSynchronize. However, it might be useful for debugging to detect which of your kernel has caused an error (if there is any).
cudaDeviceSynchronize may cause some slowdown, but 7-12x seems too much. Might be there is some problem with time measurement, or maybe the kernels are really fast, and the overhead of explicit synchronization is huge relative to actual computation time.