GPU applications on unified-memory (UMA) edge platforms often inherit a discrete-GPU memory abstraction in which they allocate one buffer for the CPU, another for the GPU, and copy data between them before and after GPU execution. On UMA hardware these buffers reside in the same physical DRAM pool, so the copies consum...
Yu-Heng Zhu, Yan-Bo Zhao, Jia-Jia Li et al.· 0 citations
GPU applications on unified-memory (UMA) edge platforms often inherit a discrete-GPU memory abstraction in which they allocate one buffer for the CPU, another for the GPU, and copy data between them before and after GPU execution. On UMA hardware these buffers reside in the same physical DRAM, so the copies consume ban...
Yu-Heng Zhu, Yan-Bo Zhao, Jia-Jia Li et al.· 0 citations
Sparse computations are important workloads in applications such as scientific computing, graph neural networks (GNNs), and machine learning. While many sparse operations can benefit from modern GPUs, the sparsity pattern remains important to performance because it affects memory coalescing, block organization, and loa...
Rui-Feng Zhang, Sai Akhil Varma Manthena, Jia-Jia Li et al.· 0 citations
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