Massively Parallel Compute as a Service

Back in the Spring of 2012, I asked several panelists at VMWorld to weigh in on vector processing with GPUs as a big data/big compute solution. The response was a resounding "not yet," as the infrastructure has not yet reached commodity level and GPU processing was greatly constrained by memory paging. It now seems like both obstacles are being removed.

Amazon Web Services is now offering EC2 instances that offer up virtualized instances of NVIDIA's Kepler GPUs as "G2" instances. This supports H.264 encoding, OpenCL, CUDA and OpenGL toolsets which allows for more mature toolsets to build apps targeted to these vector processing instances. This kind of support allows for commodity toolchains and commodity infrastructure to allow for massively parallel processing on demand.

Memory paging should soon be addressed by NVIDIA via CUDA 6, and should also be addressed by AMD with its upcoming Kaveri architecture. Once memory addressing is unified, the swapping of memory regions should become unnecessary and allow for memory to be addressed locally without pagination. This simplifies application development, virtualization and hardware architectures considerably.

I believe that very soon we will see vector processing at scale garner as much attention as map/reduce clusters currently do. Massive data parsing has been commoditized, and now we have an opportunity to commoditize massive algorithmic crunching.