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.
Efficient, Strong Scaling
NVSHMEM enables long-running kernels that include both communication and computation, reducing overheads that can limit an application’s performance when strong scaling.
One-sided communication primitives reduce overhead by allowing the initiating process or GPU thread to specify all information required to complete a data transfer. This low-overhead model enables many GPU threads to communicate efficiently.
Asynchronous communications make it easier for programmers to interleave computation and communication, thereby increasing overall application performance.
What's New in NVSHMEM 2.10.1
- This NVSHMEM release includes the following key features and enhancements:
- Support for single and multi-node Grace Hopper systems.
- Support for the EFA provider using the libfabric transport, which can be enabled with NVSHMEM_LIBFABRIC_PROVIDER=EFA. applications.
- NVRTC support was added for the NVSHMEM device implementation headers.
- Fixed memory leaks in nvshmem_finalize
- Added support for calling nvshmem_init and nvshmem_finalize in a loop with any bootstrap. Previously the support had existed only for MPI bootstrap
- Performance optimizations in Alltoall collective API.
- Implemented warp-level automated coalescing of nvshmem_
_g operations to contiguous addresses in IBGDA transport
- Removed redundant consistency operations in IBGDA transport
- Added support for synchronized memory operations when using VMM API for NVSHMEM symmetric heap.
- Code refactoring and bug fixes.
- Combines the memory of multiple GPUs into a partitioned global address space that’s accessed through NVSHMEM APIs
- Includes a low-overhead, in-kernel communication API for use by GPU threads
- Includes stream-based and CPU-initiated communication APIs
- Supports x86 and POWER9 processors
- Is interoperable with MPI and other OpenSHMEM implementations
Convolution is a compute-intensive kernel that’s used in a wide variety of applications, including image processing, machine learning, and scientific computing. Spatial parallelization decomposes the domain into sub-partitions that are distributed over multiple GPUs with nearest-neighbor communications, often referred to as halo exchanges.
In the Livermore Big Artificial Neural Network (LBANN) deep learning framework, spatial-parallel convolution is implemented using several communication methods, including MPI and NVSHMEM. The MPI-based halo exchange uses the standard send and receive primitives, whereas the NVSHMEM-based implementation uses one-sided put, yielding significant performance improvements on Lawrence Livermore National Laboratory’s Sierra supercomputer.
Efficient Strong-Scaling on Sierra Supercomputer
Efficient Strong-Scaling on NVIDIA DGX SuperPOD
Accelerate Time to Solution
Reducing the time to solution for high-performance, scientific computing workloads generally requires a strong-scalable application. QUDA is a library for lattice quantum chromodynamics (QCD) on GPUs, and it’s used by the popular MIMD Lattice Computation (MILC) and Chroma codes.
NVSHMEM-enabled QUDA avoids CPU-GPU synchronization for communication, thereby reducing critical-path latencies and significantly improving strong-scaling efficiency.
The conjugate gradient (CG) method is a popular numerical approach to solving systems of linear equations, and CGSolve is an implementation of this method in the Kokkos programming model. The CGSolve kernel showcases the use of NVSHMEM as a building block for higher-level programming models like Kokkos.
NVSHMEM enables efficient multi-node and multi-GPU execution using Kokkos global array data structures without requiring explicit code for communication between GPUs. As a result, NVSHMEM-enabled Kokkos significantly simplifies development compared to using MPI and CUDA.
Productive Programming of Kokkos CGSolve
- GTC 2022:
- NVSHMEM: CUDA-Integrated Communication for NVIDIA GPUs (a Magnum IO session) [S41044]
- Connect with the Experts: High-performance Data Analytics on the GPU [CWE41542]
- Scalable Simulation of Quantum Circuit with Noise on GPU-based HPC Systems [S41855]
- An Exploration of Pencil and Slab Decomposition Performance across Supercomputing Clusters [S41153]
- Connect with the Experts: NVIDIA Math Libraries [CWE41721]
- Introductory Webinar
- NVSHMEM Documentation
- NVSHMEM API Documentation
- OpenSHMEM Specification
- NVSHMEM Developer Forum
- For questions or to provide feedback, please contact email@example.com
- Related libraries and software:
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