Present-day high-performance computing (HPC) and deep learning applications benefit from, and even require, cluster-scale GPU compute power. Writing CUDA® applications that can correctly and efficiently utilize GPUs across a cluster requires a distinct set of skills. In this workshop, you’ll learn the tools and techniques needed to write CUDA C++ applications that can scale efficiently to clusters of NVIDIA GPUs.

NVSHMEM is a parallel programming interface based on OpenSHMEM that provides efficient and scalable communication for NVIDIA GPU clusters. NVSHMEM creates a global address space for data that spans the memory of multiple GPUs and can be accessed with fine-grained GPU-initiated operations, CPU-initiated operations, and operations on CUDA streams. NVSHMEM's asynchronous, GPU-initiated data transfers eliminate synchronization overheads between the CPU and the GPU. They also enable long-running kernels that include both communication and computation, reducing overheads that can limit an application’s performance when strong scaling. NVSHMEM has been used on systems such as the Summit supercomputer located at the Oak Ridge Leadership Computing Facility (OLCF), the Lawrence Livermore National Laboratory’s Sierra supercomputer, and the NVIDIA DGX™ A100.