In the spirit of other Apache projects, we should publish a Roadmap page.
The page should be clear on the immediate timeline, point to some future
projects, and also summarize the past to demonstrate continuum. It is not
a replacement for jiras or release notes, but just a single place for
people to go to and see what happened in the past and what will happen
going forward.
SystemML Roadmap
SystemML Release Timeline
=========================
Oct. 2016: SystemML 0.11.0 on Spark 1.x
Nov. 2016: SystemML 0.11.1 on Spark 1.x/2.x
Dec. 2016: SystemML 1.0 on Spark 1.x/2.x
Next SystemML 0.11.x
--------------------
- Features
-- SystemML frames
-- New MLContext API
-- Transform functions based on SystemML frames
- Bug fixes
- Experimental Features / algorithms
-- New built-in functions for deep learning (convolution and pooling)
-- Deep learning library (DML bodied functions)
-- Python DSL integration
-- GPU support
-- Compressed Linear Algebra
-- New Algorithms
--- Lasso
--- kNN
--- Lanczos
--- PPCA
--- Deep Learning: CNN (Lenet), RBM
Planned for future SystemML 1.0
-------------------------------
- Rigorous performance and scalability testing (bug fixes)
- Remove deprecated APIs
- Remove deprecated functions
Planned for future Releases
---------------------------
- Completion of prior experimental features
- New algorithms: Non-linear SVMs, solvers, decomposition, inversion, etc.
- DSLs (e.g. Scala, Python) and common DSL architecture
- R interfaces: R DSL and R wrappers
- Native Zeppelin Notebook support
- Code generation
- Sum product optimizations
- Tree-based data structures
- Global dataflow optimizations
Prior Releases
==============
SystemML 0.10.0-incubating (released in June, 2016) (link to release notes
(
https://github.com/apache/incubator-systemml-website/blob/master/0.10.0-incubating/release_notes.md
))
--------------------------
- Different types of Spark Matrix Blocks: MCSR, CSR, COO
- SystemML Frame support in JMLC/CP
- Initial Deep Learning support
- API/Scripts: parser error handling, SystemML configuration handling,
include algorithms in SystemML jar, print matrix
- New fused operator: wdivmm with variations
- Performance Features: cache-conscious operations, more multithreaded
operations, new simplications rewrites
- New Algorithms: kNN
- Documentation: javadocs, Jupyter/Zeppeling notebook examples
SystemML 0.9.0-incubating (released in Jan. 2016) (link to release notes (
https://github.com/apache/incubator-systemml-website/blob/master/0.9.0-incubating/release_notes.md
))
-------------------------
- Improvements to MLContext and MLPipeline wrappers
- New converter utilities for RDDs and DataFrames)
- New Optimizations for Spark Backend, e.g. eager RDD caching and
repartitioning, RDD checkpointing, on-demand creation of SparkContext
- New Runtime Operators for mmult, multihreaded readers and operators.
- New Algoriths: ALS, Cubic Splines
- Online documentation
Regards,
Berthold Reinwald
IBM Almaden Research Center
office: (408) 927 2208; T/L: 457 2208
e-mail: reinwald@us.ibm.com
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