GPUs have soared in popularity tremendously over the past few years. They are primarily intended for graphics processing but
can be used for multiple other applications.
The biggest barrier parallel programmers face is usually maintaining memory consistency between processors. In the case of graphics
applications, data movement can be mostly uni-directional. Data can be taken from common source structures (lists of shapes/etc) and frame
information computed. Data dependency becomes less of an issue as it is simpler for each processor to address its own dependency needs,
even if redundant calculation is necessary, than it is to coordinate between different processors and issue expensive semaphore or memory coherence calls.
A Personal Jetson Nano project I worked:
Relevant work experience:
At Resi Media I did some work on IP frame expansion/mapping with GPUs for a prototype system.
Projects I would like to pursue:
- OpenCL on ARM GPUs.
- Overlay Generation