We've proven that we can also speed up IO-bound and disk-bound workloads.
Memory-bound jobs are not an issue either. In fact, 12GB Nvidia and AMD GPUs were announced this week.
Memory-bound jobs are not an issue either. In fact, 12GB Nvidia and AMD GPUs were announced this week.
I would be careful about phrasing it as "memory bound is not an issue". There's a distinction between memory bandwidth (which GPUs excel at as long as the data is resident), memory latency (which GPUs are pretty bad at), and memory capacity. 12gb is slim in the grand scheme of things; my laptop has 16gb and you can get machines with >1TB of memory. Outside of the GPU's memory limits, you're relying on streaming data to/from the device, and potentially bound on PCIe bandwidth, or partitioning the problem across multiple GPUs/machines.
PCIe is the least of our concerns at 16GB/s. Some of our tests indicate that our GPU analyzes data at a rate of ~5GB/s. If we can feed it the data in that amount of time, we're in business. We're also parallelizing disk reads, which is one bottleneck at this point.
Also, have you considered using Xeon Phi instead of GPUs? Seems like this would be a perfect use case, and it would probably be much easier to work with x86.