> RAM used in GPUs has pretty good latency when compared to that used in typical CPU applications I see [0] that what I should have said is this (taken from pa

> RAM used in GPUs has pretty good latency when compared to that used in typical CPU applications I see [0] that what I should have said is this (taken from page 7): High throughput is obtained through the use of wide memory buses and specialized GDDR (graphics double data rate) memories that operate most efficiently when memory access granularities are large. Thus, GPU memory controllers must buffer, reorder, and then coalesce large numbers of memory requests to synthesize large operations that make efficient use of the memory system. Which roughly translates to higher latency from the point of view of any particular wavefront/warp, compared to the cache-heavy CPU approach. Of course, as the GPU's cores can easily switch between wavefronts, the latency from the wavefront's point of view isn't all that important; it's the overall throughput that counts. > Giving a GPU a single threaded workload would be like having a sausage factory start up to make a single sausage, it's inappropriate use of the tool, which will always give poor results. I don't like the analogy, as the single-threaded job could be enormous. Offloading a very small job onto a GPU is a bad idea because of orchestration overheads. Offloading a large single-threaded job onto a GPU is a bad idea because of poor core utilisation (you're using just one of the lanes of just one of the cores). Offloading a fixed-point-intensive job onto a GPU may be a bad idea because of inappropriate instruction-set. There's the matter of wavefront-coherent flow control. And if the job has chaotic memory access patterns, we're back to our original topic, and you'll get bitten by the wide memory bus. [0] https://www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/15869-f11/www/readings/fatahalian08_cacm.pdf https://www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/15869-f...