QuoteOriginally posted by: outrunQuoteOriginally posted by: jawabeanQuoteOriginally posted by: outrunQuoteOriginally posted by: jawabeanintel was showing off 48 core CPU. would it compete with GPU?I don't think so (for numerical jobs). Today's GPU serves (1u) have 2048 cores, 2Tflop double precision. GPU's are more specialized, CPU more genericcan you point to case studies on CPU -> GPU code porting and ROI?I don't have studies, just some personal observations:* a friend a Shell uses GPU for numerical stuff, running on hardware+code delivered by another friend at some other company* Matlab introduced GPU extensions, so does R* the new Apple OSX is able to offload threadsto GPU* generic matrix algebra code (like FD) is easily ported to GPU with CBlas* GPU deliver 1Ghz cores at cost $5-$10 per core, about 10x cheaper than CPUI myself haven't done GPU coding yet, but I will somewhere nextyear as part of a C++ numerical projectI totally agree. We have developed on GPU (NVIDIA Tesla) for more than a year and now optimize for their latest "personal supercomputer" (CPUs plus GPUs). C++ environment is still lower level (has to do with the weakness of C++ with massive threading?). However, I expect all of the major PC makers will add GPU ... Price per performance is decreasing drastically.At the other hand the lack of massive threading support in plain C++ will limit its further expansion (Java much better).However, do we need this speed, if we use clever techniques, like principal component application, surrogate model implementations? IMO, yes, because it will open a path for much more brute force algorithms (which are cheaper in development)? And multi-method and muli-strategy systems (overdue in quant finance IMO).
Last edited by exneratunrisk
on December 6th, 2009, 11:00 pm, edited 1 time in total.