Official Release for RandBLAS 0.2.0

[rileyjmurray]

The work that lead to this release of RandBLAS was funded by the U.S. National Science Foundation and the U.S. Department of Energy, and was conducted at the International Computer Science Institute, the University of California at Berkeley, the University of Tennessee at Knoxville, Lawrence Berkeley National Laboratory, and Sandia National Laboratories.

The content that follows is adapted from the release notes on the RandBLAS ReadTheDocs website.

Release contents

Today marks the first formal release of RandBLAS. We've been working on it for over three years, so we couldn't possibly describe all of its capabilities in just this changelog. Instead, we'll repurpose some text that's featured prominently in our documentation at the time of this release.

A quote from the README, describing the aims of this project:

RandBLAS supports high-level randomized linear algebra algorithms (like randomized low-rank SVD) that might be implemented in other libraries. Our goal is for RandBLAS to become a standard like the BLAS, in that hardware vendors might release their own optimized implementations of algorithms which confirm to the RandBLAS API.

A quote from the website, describing our current capabilities:

RandBLAS is efficient, flexible, and reliable. It uses CPU-based OpenMP acceleration to apply its sketching operators to dense or sparse data matrices stored in main memory. All sketches produced by RandBLAS are dense. As such, dense data matrices can be sketched with dense or sparse operators, while sparse data matrices can only be sketched with dense operators. RandBLAS can be used in distributed environments through its ability to (reproducibly) compute products with submatrices of sketching operators.

There's a ton of documentation besides those snippets. In fact, we have three separate categories of documentation!

1. Traditional source code comments.
2. Web documentation (https://randblas.readthedocs.io/en/latest/)
3. Developer notes; one for RandBLAS as a whole, another for our sparse matrix functionality, and a third for our website.

Contributors and Acknowledgements

Since this is our first release, many acknowledgements in order. We'll start with contributors to the RandBLAS codebase as indicated by the repository commit history.

• Riley Murray (@rileyjmurray)
• Burlen Loring (@burlen)
• Kaiwen He (@kaiwenhe7)
• Maksim Melnichenko (@TeachRaccooon)
• Tianyu Liang (@Tianyu-Liang)
• Vivek Bharadwaj (@vbharadwaj-bk)

In addition to code contributors, we had the benefit of supervision and input from the following established principal investigators

James Demmel, Michael Mahoney, Jack Dongarra, Piotr Luszczek, Mark Gates, and Julien Langou.

We would also like to thank Weslley da Silva Pereira, who gave valuable feedback at the earliest stages of this project, and all of the individuals who gave feedback on our RandNLA monograph.

What happened to version 0.1?

We tagged a commit on the RandBLAS repository with version 0.1.0 almost two years ago. However, we hadn't maintained version numbers or a dedicated changelog since then. RandBLAS 0.2.0 is our first formal release. We opted not to release under version 0.1.0 since that could ambiguously refer to anything from the now-very-old 0.1.0 tag up to the present.

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This discussion was created from the release Official Release for RandBLAS 0.2.0.

[rileyjmurray]

Well, with releases comes the need to document known bugs. It looks like the sparse QRCP example qrcp_matrixmarket.cc had a one-line bug that led to excessive memory usage. I didn't catch this before the release because I only tested the examples on smaller matrices (order 10,000 by 10,000). The specific issue occurs here:

RandBLAS/examples/sparse-low-rank-approx/qrcp_matrixmarket.cc

Line 247 in 60d06fb

int64_t sketch_dim = (int64_t) (1.25*m + 1);

It's wildly off-base to be using m in that function for the sketch dimension. The correct/intended value is k. This has been resolved in 4bc2934.