News: SystemML Release 0.8.0 - Distributed and Declarative Machine Learning

The Spark Technology Center team has just released SystemML 0.8.0.

SystemML 0.8.0 is the first binary release of SystemML since its initial migration to GitHub on August 16, 2015. This release represents 320+ patches from 14 contributors since that date. SystemML became publicly available on GitHub on August 27, 2015.

Extensive updates have been made to the project in several areas. These include APIs, data ingestion, optimizations, language and runtime operators, new algorithms, testing, and online documentation.


Improvements to MLContext and to MLPipeline wrappers

Data Ingestion

Data conversion utilities (from RDDs and DataFrames)
Data transformations on raw data sets


Extensions to compilation chain, including IPA
Improvements to parfor
Improved execution of concurrent Apache Spark jobs
New rewrites, including eager RDD caching and repartitioning
Improvements to buffer pool caching
Partitioning-preserving operations
On-demand creation of SparkContext
Efficient use of RDD checkpointing

Language and Runtime Operators

New matrix multiplication operators (e.g., ZipMM)
New multi-threaded readers and operators
Extended aggregation-outer operations for different relational operators
Sample capability

New Algorithms

Alternating Least Squares (Conjugate Gradient)
Cubic Splines (Conjugate Gradient and Direct Solve)


PyDML algorithm tests
Test suite refactoring
Improvements to performance tests

Online Documentation

Quick Start Guide
DML and PyDML Programming Guide
MLContext Programming Guide
Algorithms Reference
DML Language Reference
Debugger Guide
Documentation site available at


You Might Also Enjoy

Kevin Bates
Kevin Bates
10 months ago

Limit Notebook Resource Consumption by Culling Kernels

There’s no denying that data analytics is the next frontier on the computational landscape. Companies are scrambling to establish teams of data scientists to better understand their clientele and how best to evolve product solutions to the ebb and flow of today’s business ecosystem. With Apache Hadoop and Apache Spark entrenched as the analytic engine and coupled with a trial-and-error model to... Read More

Gidon Gershinsky
Gidon Gershinsky
a year ago

How Alluxio is Accelerating Apache Spark Workloads

Alluxio is fast virtual storage for Big Data. Formerly known as Tachyon, it’s an open-source memory-centric virtual distributed storage system (yes, all that!), offering data access at memory speed and persistence to a reliable storage. This technology accelerates analytic workloads in certain scenarios, but doesn’t offer any performance benefits in other scenarios. The purpose of this blog is to... Read More