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[DOCS] Create release notes for 3.2.0 (#140)
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j143 committed Mar 23, 2024
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title: SystemDS 3.2.0 Release Notes
description: Project Release Notes
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<h1>{{ site.data.project.name }} 3.2.0 Release Notes</h1>
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## Release notes for SystemDS 3.2.0

SystemDS 3.2 is a minor release. Release 3.2 contains new features and major improvements to existing features.

### New Features

- Reuse, recycling, and memory management of GPU
- New operator placement strategy to maximize parallelism across backends (CPU, Spark, GPU)
- Improved compiler integration of multi-backend reuse: delayed caching, eviction placement, checkpoint placement, and asynchronous operators
- Frame compression and lossy compression with binning
- Improved compression support for transform encode
- Ultra sparse matrix multiplication optimization
- Improved parallel writing to the local file system
- Extended sparsity propagation in federated backend
- Deduplicated matrix block for word embedding
- Double compressed sparse row block
- Time-series Alignment library (Python API)


### New Builtins

- Linearized image transformations
- Anomaly detection via IsolationForest
- Better support for DNN models
- Performance improvement of many existing builtins


### Other Optimizations

- Various new compiler rewrites
- Extended constant folding
- Improved handling of ultra-sparse matrices
- Rewrites for GPU operators
- Robustness for decision tree and random forest
- Improved performance of transform encode/apply
- Various bug fixes in memory estimates, Spark checkpointing, frame operations, IO, and other areas


### Software Build

- Upgrade spark 3.5 and corresponding dependencies and fix imports
- Slf4j and log4j update to the latest version
- GitHub actions improvements for Windows, upgrade the actions dependencies to the latest. Fix all the flagged dependencies
- Fix GitHub actions failure related to federated tests
- Code coverage and actions artifact lifecycle


### Documentation

- Fix federated tutorial commands.
- Builtin functions, Random Forest and decision tree algorithms

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