A dynamic rather than static shared memory allocation in the kernel code.Įxample of dynamic: extern _shared_ int shared_mem Įxample of static: _shared_ int shared_mem ĭynamically allocated shared memory also requires a size to be passed in the kernel launch configuration parameters (an example is given in the question).The opt-in mechanism already described above.it should be stored in shared memory on device, as parameters in a kernel call. Outside of processing, the changes affected the pre-allocated memory and added one statically-defined boolean inside the kernel. Question: Here I tried to self-explain the CUDA launch parameters. The system worked prior to a few changes. All memory for the kernel is pre-allocated before the call. The programmer has no ability to use more than 48KB per threadblock.Īlthough it doesn't pertain to the code presented here (which is already using a dynamic shared memory allocation), note from the excerpted documentation quote that using more than 48KB of shared memory on devices that support it requires 2 things: The purpose of this article is to explain how a kernel can invoke (out-of-memory) oom killer to abruptly kill a process which might be a java or node process of your application and what steps we can take to resolve such type of issues. The error persists even when the amount of free memory, per cudaMemGetInfo, is up to 900mb. In some cases, these GPUs may have more shared memory per multiprocessor, but this is provided to allow for greater occupancy in certain threadblock configurations. GPUs with compute capability prior to 7.x have no ability to address more than 48KB per threadblock. Note that the above covers the Volta (7.0) Turing (7.5) and Ampere (8.x) cases. If you are on a Jupyter or Colab notebook, after you hit RuntimeError: CUDA out of memory. In a similar fashion kernels on Ampere devices should be able to use up to 160KB of shared memory (cc 8.0) or 100KB (cc 8.6), dynamically allocated, using the above opt-in mechanism, with the number 98304 changed to 163840 (for cc 8.0, for example) or 102400 (for cc 8.6). And of course 65536 would be sufficient for your example as well, although not sufficient to use the maximum available on volta, as stated in the question title. For a Turing device, you would want to change that number from 98304 to 65536. This allows for processes to overcommit 'reasonable' amounts of memory. Mode 0 is the default mode for SLES servers. When I add that line to the code you have shown, the invalid value error goes away. This file details the following 3 modes available for overcommit memory in the Linux kernel: 0 - Heuristic overcommit handling. Kernels relying on shared memory allocations over 48 KB per block are architecture-specific, as such they must use dynamic shared memory (rather than statically sized arrays) and require an explicit opt-in using cudaFuncSetAttribute() as follows: cudaFuncSetAttribute(my_kernel, cudaFuncAttributeMaxDynamicSharedMemorySize, 98304) Compute capability 7.x devices allow a single thread block to address the full capacity of shared memory: 96 KB on Volta, 64 KB on Turing.
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