After numerous trials with CUDA enabled machines, I found the "cost function" models are better at determining efficiency. For example: * A single GPU machin

After numerous trials with CUDA enabled machines, I found the "cost function" models are better at determining efficiency. For example: * A single GPU machine is often more efficient than multiple cards due to bus i/o bandwidth constraints, lack of software support, and obscure driver failure modes * A single CPU machine is often more efficient than multi-chip solutions due to memory access latency and caching issues * A sequential drive data access machine is often more efficient than arrays due to pipelined memory cache layout and baked access latency * A single CPU core bound process is often more efficient due to avoiding threading and or mailbox overheads Thus, if a problem is truly separable, than it is sometimes wiser to bind n-many slow jobs to n-many cores rather than parallelize 1 job to try to get it done more quickly. I found this rather surprising in processing large video media data sets. Some may disagree, but they are mostly 15% to 30% more wrong for truly separable tasks. YMMV =3