NVSHMEM

NVSHMEM


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.


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Existing communication models, such as Message-Passing Interface (MPI), orchestrate data transfers using the CPU. In contrast, NVSHMEM uses asynchronous, GPU-initiated data transfers, eliminating synchronization overheads between the CPU and the GPU.

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.

Low Overhead

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.

Naturally Asynchronous

Asynchronous communications make it easier for programmers to interleave computation and communication, thereby increasing overall application performance.


What's new in NVSHMEM 2.0.3

  • Added the teams and team-based collectives APIs from OpenSHMEM 1.5.
  • Added support to use the NVIDIA® Collective Communication Library (NCCL) for optimized NVSHMEM host and on-stream collectives.
  • Added support for RDMA over Converged Ethernet (RoCE) networks.
  • Added support for PMI-2 to enable an NVSHMEM job launch with srun/SLURM.
  • Added support for PMIx to enable an NVSHMEM job launch with PMIx-compatible launchers, such as Slurm and Open MPI.
  • Uniformly reformatted the perftest benchmark output.
  • Added support for the putmem_signal and signal_wait_until APIs.
  • Improved support for single-node environments without InfiniBand.
  • Fixed a bug that occurred when large numbers of fetch atomic operations were performed on InfiniBand.
  • Improved topology awareness in NIC-to-GPU assignments for NVIDIA® DGX™ A100 systems.
  • Added the NVSHMEM_CUDA_LIMIT_STACK_SIZE environment variable to set the GPU thread stack size on Power systems.
  • Updated the threading level support that was reported for host and stream-based APIs to NVSHMEM_THREAD_SERIALIZED.
  • Device-side APIs support NVSHMEM_THREAD_MULTIPLE.

Key Features


  • 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

NVSHMEM Advantages


Increase Performance

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.

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Simplify Development

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

Resources



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