The Apache SystemML 1.1.0 release was approved on March 28, 2018. The release includes enhancements, features, and additions as listed below.

New Builtin Functions/Operations

• ifelse
• assert
• Deep learning builtin functions: avg_pool, avg_pool_backward
• Second-order eval
• Accumulator operator +=
• Bitwise operators not, and, or, xor, & LShift, Rshift
• Logical operator support over matrices AND/OR/NOT/XOR

Additional Layers in the NN library

• Average pooling
• Upsampling
• Low-rank fully connected

New Capabilities/Features

• Dense matrix blocks >16GB, and operations
• Enable additional ParFor result aggregation operations
• UDFs callable in expressions
• Zero rows/columns matrices and updated operations such as removeEmpty
• Matrix-matrix multiplication over compressed matrices
• Extended Caffe2DML and Keras2DML APIs

Compiler & Runtime

• Use common thread pool
• Single-precision support for native conv2d and mm operations
• Improved nnz maintenance, runtime propogation and memory management
• Robustness for matrices with larger than int dimensions
• [Experimental] Codegen extensions: operation support, extended optimizer, see SYSTEMML-2065

Performance Improvements

• sparse left indexing, sparse reshape, ultra-sparse transpose, ultra-sparse rand, binary in-place operations, sparse relu backward, maxpooling, sparse im2col, ultra-sparse conv2d, read of short-wide sparse matrices, avoid unnecessary evictions, lock-free statistics maintenance, spark cpmm, spark aggregates, spark reshape, spark binary ops, etc.

Bug Fixes

• in APIs, performance, optimizer, runtime, GPU backend, Spark backend

Deprecate

• Support for Spark 2.1 / 2.2 (make switch to newer ANTLR version)

JIRA release notes