Update Simple_MultiGPU_GP_Regression.ipynb
Paper link and minor edits.
"source": [
"# Exact GP Regression with Multiple GPUs and Kernel Partitioning\n",
"\n",
- "In this notebook, we'll demonstrate training exact GPs on very large datapoints using two key features:\n",
+ "In this notebook, we'll demonstrate training exact GPs on large datasets using two key features from the paper https://arxiv.org/abs/1903.08114: \n",
"\n",
"1. The ability to distribute the kernel matrix across multiple GPUs, thereby using additional parallelism.\n",
"2. Partitioning the kernel in to chunks that are computed on-the-fly when performing each MVM so as to reduce memory usage enough.\n",
"\n",
- "We'll be using the `protein` dataset, which will have about 37000 training examples. Note that although the techniques in this notebook can be applied to much larger datasets than this, the training time required will depend dramatically on the computational resources you have available: both the number of GPUs available and the amount of memory they have (which determines the partition size) have a dramatic effect on performance."
+ "We'll be using the `protein` dataset, which has about 37000 training examples. The techniques in this notebook can be applied to much larger datasets, but the training time required will depend on the computational resources you have available: both the number of GPUs available and the amount of memory they have (which determines the partition size) have a significant effect on training time."
]
},
{
