Hi,
Matrix computation is critical for algorithm efficiency like least square,
Kalman filter and so on.
For now, the mllib module offers limited linear algebra on matrix, especially
for distributed matrix.
We have been working on establishing distributed matrix computation APIs based
on data
There's been some work at the AMPLab on a distributed matrix library on top
of Spark; see here [1]. In particular, the repo contains a couple
factorization algorithms.
[1] https://github.com/amplab/ml-matrix
Zongheng
On Mon Nov 17 2014 at 7:34:17 PM liaoyuxi liaoy...@huawei.com wrote:
Hi,
Hey Yuxi,
We also have implemented a distributed matrix multiplication library in
PasaLab. The repo is host on here https://github.com/PasaLab/marlin . We
implemented three distributed matrix multiplication algorithms on Spark. As
we see, communication-optimal does not always means the
.
发件人: Zongheng Yang [mailto:zonghen...@gmail.com]
发送时间: 2014年11月18日 11:37
收件人: liaoyuxi; d...@spark.incubator.apache.org
抄送: Shivaram Venkataraman
主题: Re: matrix computation in spark
There's been some work at the AMPLab on a distributed matrix library on top of
Spark; see here [1]. In particular
Hi Yuxi,
We are integrating the ml-matrix from the AMPlab repo into MLlib, tracked
by this JIRA: https://issues.apache.org/jira/browse/SPARK-3434
We already have matrix multiply, but are missing LU decomposition. Could
you please track that JIRA, once the initial design is in, we can sync on
how
