PyTorch Conference 2024

Memory intensive deep learning workloads require efficient use of all kinds of memories that are available in a system. In this session, we will discuss how we can utilize such heterogeneous memory through memory pools in PyTorch. We will show how to mix-and-match different CUDA system allocators in the same PyTorch program using memory pools. Consequently, this API unlocks new use cases such as Extended GPU Memory (EGM) based all-gathers, Unified Virtual Memory (UVM), and NVLink Sharp (NVLS) reductions. New NVIDIA architectures accelerate such use cases with high-bandwidth and low-latency interconnects in the hardware, driven by extended functionality of CUDA system allocators in the software. Learn how to use these techniques on memory-intensive deep learning models like LLMs, and discover new CUDA features powered by PyTorch.