Package Health Score

72 / 100

security
No known security issues
popularity
Small
maintenance
Sustainable
community
Sustainable

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cuda
42

Popularity

Small

GitHub Stars: 1.58K
Forks: 337
Contributors: 30

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package nvidia-cusparselt-cu12, we found that it has been starred 1,581 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Oct 15, 2024
Direct Vulnerabilities: 0
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0
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0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

NVIDIA Proprietary Software

Security Policy

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Maintenance

Sustainable

Commit Frequency

Open Issues: 47
Open PR: 15
Last Release: 20 days ago
Last Commit: 13 days ago

Further analysis of the maintenance status of nvidia-cusparselt-cu12 based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Sustainable.

We found that nvidia-cusparselt-cu12 demonstrates a positive version release cadence with at least one new version released in the past 3 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Sustainable

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 30
Funding: No

With more than 10 contributors for the nvidia-cusparselt-cu12 repository, this is possibly a sign for a growing and inviting community.

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Package

Python Versions Compatibility: Unspecified

Age: 7 months
Latest Release: 20 days ago
Dependencies: N/A
Versions: 3
Maintainers: 1
Wheels: Linux, Windows

Readme

cuSPARSELt: A High-Performance CUDA Library for Sparse Matrix-Matrix Multiplication

NVIDIA cuSPARSELt is a high-performance CUDA library dedicated to general matrix-matrix operations in which at least one operand is a sparse matrix:

D = Activatio**n(αop(A) ⋅ o**p(B) + βop(C) + bia**s) ⋅ scale

where o**p(A)/o**p(B) refers to in-place operations such as transpose/non-transpose, and alpha, bet**a, scale are scalars.

The cuSPARSELt APIs allow flexibility in the algorithm/operation selection, epilogue, and matrix characteristics, including memory layout, alignment, and data types.

Download: developer.nvidia.com/cusparselt/downloads

Provide Feedback: Math-Libs-Feedback@nvidia.com

Examples: cuSPARSELt Example 1, cuSPARSELt Example 2

Blog post:

Key Features

NVIDIA Sparse MMA tensor core support
Mixed-precision computation support:

> > > > > > > > > > > > > > > > > > > > > > > > > > > > > >

Input A/B Input C Output D Compute
FP32 FP32 FP32 FP32
FP16 FP16 FP16 FP32
> > \+ + + +-------------+ | | | | FP16 > | > +--------------+----------------+-----------------+-------------+ > | BF16 | class="title-ref">BF16 | class="title-ref">BF16 | class="title-ref">FP32 | > +--------------+----------------+-----------------+-------------+ > | INT8 | class="title-ref">INT8 | class="title-ref">INT8 | class="title-ref">INT32 | > \+ +----------------+-----------------+ + | | class="title-ref">INT32 | class="title-ref">INT32 | | > \+ +----------------+-----------------+ + | | class="title-ref">FP16 | class="title-ref">FP16 | | > \+ +----------------+-----------------+ + | | class="title-ref">BF16 | class="title-ref">BF16 | | > +--------------+----------------+-----------------+-------------+ > | E4M3 | class="title-ref">FP16 | class="title-ref">E4M3 | class="title-ref">FP32 | > \+ +----------------+-----------------+ + | | class="title-ref">BF16 | class="title-ref">E4M3 | | > \+ +----------------+-----------------+ + | | class="title-ref">FP16 | class="title-ref">FP16 | | > \+ +----------------+-----------------+ + | | class="title-ref">BF16 | class="title-ref">BF16 | | > \+ +----------------+-----------------+ + | | class="title-ref">FP32 | class="title-ref">FP32 | | > +--------------+----------------+-----------------+-------------+ > | E5M2 | class="title-ref">FP16 | class="title-ref">E5M2 | class="title-ref">FP32 | > \+ +----------------+-----------------+ + | | class="title-ref">BF16 | class="title-ref">E5M2 | | > \+ +----------------+-----------------+ + | | class="title-ref">FP16 | class="title-ref">FP16 | | > \+ +----------------+-----------------+ + | | class="title-ref">BF16 | class="title-ref">BF16 | | > \+ +----------------+-----------------+ + | | class="title-ref">FP32 | class="title-ref">FP32 | | > +--------------+----------------+-----------------+-------------+
Matrix pruning and compression functionalities
Activation functions, bias vector, and output scaling
Batched computation (multiple matrices in a single run)
GEMM Split-K mode
Auto-tuning functionality (see cusparseLtMatmulSearch())
NVTX ranging and Logging functionalities

Input A/B	Input C	Output D	Compute
FP32	FP32	FP32	FP32
FP16	FP16	FP16	FP32

Support

Supported SM Architectures: SM 8.0, SM 8.6, SM 8.9, SM 9.0
Supported CPU architectures and operating systems:

OS	CPU archs
Windows	x86_64
Linux	x86_64, Arm64

Documentation

Please refer to for the cuSPARSELt documentation.

Installation

The cuSPARSELt wheel can be installed as follows:

pip install nvidia-cusparselt-cuXX

where XX is the CUDA major version (currently CUDA 12 only is supported).

FAQs

What is nvidia-cusparselt-cu12?

NVIDIA cuSPARSELt. Visit Snyk Advisor to see a full health score report for nvidia-cusparselt-cu12, including popularity, security, maintenance & community analysis.

Is nvidia-cusparselt-cu12 popular?

We’re still processing downloads data for the python package nvidia-cusparselt-cu12. Visit the popularity section on Snyk Advisor to see the full popularity analysis.

Is nvidia-cusparselt-cu12 well maintained?

We found indications that nvidia-cusparselt-cu12 maintenance is sustainable demonstrating some project activity. We saw a total of 30 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is nvidia-cusparselt-cu12 safe to use?

The python package nvidia-cusparselt-cu12 was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 25 October-2024, at 05:26 (UTC).

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NVIDIA cuSPARSELt For more information about how to use this package see README

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Readme

cuSPARSELt: A High-Performance CUDA Library for Sparse Matrix-Matrix Multiplication

Key Features

Support

Documentation

Installation

FAQs

What is nvidia-cusparselt-cu12?

Is nvidia-cusparselt-cu12 popular?

Is nvidia-cusparselt-cu12 well maintained?

Is nvidia-cusparselt-cu12 safe to use?

Build a secure application checklist

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Package Health Score

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Direct Usage Popularity

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Commit Frequency

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Readme

cuSPARSELt: A High-Performance CUDA Library for Sparse Matrix-Matrix Multiplication

Key Features

Support

Documentation

Installation

FAQs

What is nvidia-cusparselt-cu12?

Is nvidia-cusparselt-cu12 popular?

Is nvidia-cusparselt-cu12 well maintained?

Is nvidia-cusparselt-cu12 safe to use?

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Source Code

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