NVIDIA cuDSS (Preview): A high-performance CUDA Library for Direct Sparse Solvers#

NVIDIA cuDSS (Preview) is a library of GPU-accelerated linear solvers with sparse matrices. It provides algorithms for solving linear systems of the following type:

\[A X = B\]

with a sparse matrix \(A\), right-hand side \(B\) and unknown solution \(X\) (could be a matrix or a vector).

The cuDSS functionality allows flexibility in matrix properties and solver configuration, as well as execution parameters like CUDA streams.

Note: Since the library is released as a preview, API is subject to change in later releases.

Download: developer.nvidia.com/cudss-downloads

Provide Feedback: cuDSS-EXTERNAL-Group@nvidia.com

Examples: cuDSS Example 1, cuDSS Example 2, cuDSS Example 3 cuDSS Example 4

Key Features and Properties#

  • Real/complex general/symmetric/positive-definite sparse matrices

  • Non-uniform batching (solving multiple different systems of different sizes)

  • Single and double precision datatype for values and int datatype for indices

  • Single and multiple right-hand sides

  • Multi-stage execution with three main phases: reordering & symbolic factorization, numerical factorization and solving

  • Different algorithms for reordering and factorization phases

  • Refactorization

  • Iterative refinement

  • User-defined device memory handlers and memory pools

  • Hybrid host/device memory mode/algorithm

  • Hybrid host/device execution mode

  • Multi-GPU multi-node (MGMN) execution with a user-definable communication layer

  • Multi-Threaded (MT) execution with a user-definable threading layer

  • Multi-threaded reordering for the default reordering algorithm

  • Synchronous API (asynchronous for factorization and solve when host memory/execution modes are not enabled)

  • Non-deterministic computations (no bit-wise reproducibility)

Support#

  • Supported configurations: single GPU, multi-GPU multi-node (MGMN)

  • Supported SM Architectures: all SM starting with Pascal (SM_87 and SM_101 only for aarch64 + Jetson build)

  • Supported OSes: Linux, Windows

  • Supported CPU Architectures: x86_64, ARM (SBSA), ARM (aarch64/Jetson) for Orin and Thor devices

  • Supported communication backends (for MGMN mode): pre-built for OpenMPI 4.x and NCCL 2.x + any GPU-aware stream-aware user-defined

  • Supported threading backends (for MT mode): pre-built for GNU OpenMP (Linux) and VCOMP (Windows) + any user-defined

Index#