Apache SystemML Roadmap

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 0.11.0-incubating (released in November, 2016)
    • SystemML frames
    • New MLContext API
    • Transform functions based on SystemML frames
  • 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 Algorithms
    • CNN (Lenet)
    • RBM
  • SystemML 0.10.0-incubating (released in June, 2016)
    • 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)
    • 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