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> and therefore going to be less popular than using CUDA directly. The current ambition of NV is trying to make it as painless as possible, by relying on share

• dragontamer 5y ago
  > The current ambition of NV is trying to make it as painless as possible, by relying on shared virtual memory much more than before. Shared-virtual-memory is an OpenCL term. I think the NVidia term is "Unified Memory", but yes, I get what you're trying to say. I think that makes the mistake too far in the simplicity direction. Its over-simplified. Like, think about the following code: DataStructure data = foo(); // cudaMallocManaged() / Shared-virtual Memory bar(data); baz(data); In a shared-virtual memory situation, you'd probably have data sitting on CPU-side, and then SVM over the data around. With a little bit more code, you can have... Array<DataStructure> data = foo(); ArrayView<DataStructure> dataview = data; bar(data); baz(data); In this case, the data exists on GPU-side (explicitly: all Array<> in C++Amp are GPU-allocated). If foo, bar and baz are all GPU-functions, then the data never moves (no cudaMemcpy involved). If bar is a CPU-function, you get the copy back-and-forth for correctness. But that still leaves room for you to convert bar from CPU-code into GPU-code without affecting this section of code... giving you the ability to smoothly improve performance as your project matures. ------- CUDA / OpenCL default memory is too granular. Datastructure* data = cudaMalloc(...); // GPU allocation Datastructure* localData = malloc(...); cudaMemcpy(localData, data, stream-stuff, etc. etc.) // ugggh... too much. Lots of steps to do anything, thinking about streams and async and signals. ------- Finding the right compromise between performance and simplicity is key. CUDA/OpenCL have both something that's too simple(SVM / Unified Memory), and too complicated (cudaMalloc + cudaMemcpy). The answer IMO lies in-between somehow.
  • my123 5y ago
    > CUDA/OpenCL have both something that's too simple(SVM / Unified Memory) It’s sadly the only choice around for maximum code reuse… with then manually optimising the parts that need to be later through profiling, as developer time is quite limited. On systems where the CPU and GPU are coherent such SoCs w/ CPU and GPU on the same die, Infinity Fabric-linked GPUs or NVlink linked ones (Power9/Grace), that option is also quite attractive. The memory manager measures the frequency of access from the CPU and GPU to (try to) locate where a buffer should be stored or moved dynamically. However, on PCIe linked GPUs, reliance on heuristics is more necessary to reach good perf levels. You can also ask to have your memory allocation migrated to the GPU on-demand with cudaMemPrefetchAsync (finally implemented in ROCm 4.5 too, which has unified memory on AMD cards) before doing a kernel launch for the data that you’ll use. (See https://on-demand.gputechconf.com/gtc/2017/presentation/s7285-nikolay-sakharnykh-unified-memory-on-pascal-and-volta.pdf https://on-demand.gputechconf.com/gtc/2017/presentation/s728... for the changes that happened in Volta on that front) The goal is to make the use of regular cudaMalloc/hipMalloc not necessary for good performance in a wide variety of scenarios. The explicit storage location APIs will stay available for when more optimisation is necessary.