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Popular deep learning frameworks require users to fine-tune their memoryusage so that the training data of a deep neural network (DNN) fits within theGPU physical memory. Prior work tries to address this restriction byvirtualizing the memory usage of DNNs, enabling both CPU and GPU memory to beutilized for memory allocations. Despite its merits, virtualizing memory canincur significant performance overheads when the time needed to copy data backand forth from CPU memory is higher than the latency to perform thecomputations required for DNN forward and backward propagation. We introduce ahigh-performance virtualization strategy based on a "compressing DMA engine"(cDMA) that drastically reduces the size of the data structures that aretargeted for CPU-side allocations. The cDMA engine offers an average 2.6x(maximum 13.8x) compression ratio by exploiting the sparsity inherent inoffloaded data, improving the performance of virtualized DNNs by an average 32%(maximum 61%).

Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks