# SystemDS 2.1.0 API

SystemDS Architecture

See: Description

Packages
org.apache.sysds.api
org.apache.sysds.api.jmlc
org.apache.sysds.api.mlcontext
org.apache.sysds.common
org.apache.sysds.conf
org.apache.sysds.hops
org.apache.sysds.hops.codegen
org.apache.sysds.hops.codegen.cplan
org.apache.sysds.hops.codegen.cplan.cuda
org.apache.sysds.hops.codegen.cplan.java
org.apache.sysds.hops.codegen.opt
org.apache.sysds.hops.codegen.template
org.apache.sysds.hops.cost
org.apache.sysds.hops.estim
org.apache.sysds.hops.ipa
org.apache.sysds.hops.recompile
org.apache.sysds.hops.rewrite
org.apache.sysds.lops
org.apache.sysds.lops.compile
org.apache.sysds.parser
org.apache.sysds.parser.dml
org.apache.sysds.runtime
org.apache.sysds.runtime.codegen
org.apache.sysds.runtime.compress
org.apache.sysds.runtime.compress.cocode
org.apache.sysds.runtime.compress.colgroup
org.apache.sysds.runtime.compress.colgroup.dictionary
org.apache.sysds.runtime.compress.colgroup.insertionsort
org.apache.sysds.runtime.compress.colgroup.mapping
org.apache.sysds.runtime.compress.colgroup.offset
org.apache.sysds.runtime.compress.estim
org.apache.sysds.runtime.compress.estim.sample
org.apache.sysds.runtime.compress.lib
org.apache.sysds.runtime.compress.readers
org.apache.sysds.runtime.compress.utils
org.apache.sysds.runtime.compress.workload
org.apache.sysds.runtime.controlprogram
org.apache.sysds.runtime.controlprogram.caching
org.apache.sysds.runtime.controlprogram.context
org.apache.sysds.runtime.controlprogram.federated
org.apache.sysds.runtime.controlprogram.paramserv
org.apache.sysds.runtime.controlprogram.paramserv.dp
org.apache.sysds.runtime.controlprogram.paramserv.rpc
org.apache.sysds.runtime.controlprogram.parfor
org.apache.sysds.runtime.controlprogram.parfor.opt
org.apache.sysds.runtime.controlprogram.parfor.stat
org.apache.sysds.runtime.controlprogram.parfor.util
org.apache.sysds.runtime.data
org.apache.sysds.runtime.functionobjects
org.apache.sysds.runtime.instructions
org.apache.sysds.runtime.instructions.cp
org.apache.sysds.runtime.instructions.cpfile
org.apache.sysds.runtime.instructions.fed
org.apache.sysds.runtime.instructions.gpu
org.apache.sysds.runtime.instructions.gpu.context
org.apache.sysds.runtime.instructions.spark
org.apache.sysds.runtime.instructions.spark.data
org.apache.sysds.runtime.instructions.spark.functions
org.apache.sysds.runtime.instructions.spark.utils
org.apache.sysds.runtime.io
org.apache.sysds.runtime.io.hdf5
org.apache.sysds.runtime.io.hdf5.message
org.apache.sysds.runtime.lineage
org.apache.sysds.runtime.matrix.data
org.apache.sysds.runtime.matrix.operators
org.apache.sysds.runtime.meta
org.apache.sysds.runtime.privacy
org.apache.sysds.runtime.privacy.finegrained
org.apache.sysds.runtime.privacy.propagation
org.apache.sysds.runtime.transform
org.apache.sysds.runtime.transform.decode
org.apache.sysds.runtime.transform.encode
org.apache.sysds.runtime.transform.meta
org.apache.sysds.runtime.transform.tokenize
org.apache.sysds.runtime.util
org.apache.sysds.utils

# SystemDS Architecture

Algorithms in Apache SystemDS are written in a high-level R-like language called Declarative Machine learning Language (DML) or a high-level Python-like language called PyDML. SystemDS compiles and optimizes these algorithms into hybrid runtime plans of multi-threaded, in-memory operations on a single node (scale-up) and distributed Spark operations on a cluster of nodes (scale-out). SystemDS's high-level architecture consists of the following components:

## Language

DML (with either R- or Python-like syntax) provides linear algebra primitives, a rich set of statistical functions and matrix manipulations, as well as user-defined and external functions, control structures including parfor loops, and recursion. The user provides the DML script through one of the following APIs:

- Command-line interface ( `DMLScript` )
- Convenient programmatic interface for Spark users ( `MLContext` )
- Java Machine Learning Connector API ( `Connection` )

`ParserWrapper` performs syntactic validation and parses the input DML script using ANTLR into a a hierarchy of `StatementBlock` and `Statement` as defined by control structures. Another important class of the language component is `DMLTranslator` which performs live variable analysis and semantic validation. During that process we also retrieve input data characteristics -- i.e., format, number of rows, columns, and non-zero values -- as well as infrastructure characteristics, which are used for subsequent optimizations. Finally, we construct directed acyclic graphs (DAGs) of high-level operators ( `Hop` ) per statement block.

## Optimizer

The SystemDS optimizer works over programs of HOP DAGs, where HOPs are operators on matrices or scalars, and are categorized according to their access patterns. Examples are matrix multiplications, unary aggregates like rowSums(), binary operations like cell-wise matrix additions, reorganization operations like transpose or sort, and more specific operations. We perform various optimizations on these HOP DAGs, including algebraic simplification rewrites ( `ProgramRewriter` ), intra-/`InterProceduralAnalysis` for statistics propagation into functions and over entire programs, and operator ordering of matrix multiplication chains. We compute memory estimates for all HOPs, reflecting the memory requirements of in-memory single-node operations and intermediates. Each HOP DAG is compiled to a DAG of low-level operators ( `Lop` ) such as grouping and aggregate, which are backend-specific physical operators. Operator selection picks the best physical operators for a given HOP based on memory estimates, data, and cluster characteristics. Individual LOPs have corresponding runtime implementations, called instructions, and the optimizer generates an executable runtime program of instructions.

## Runtime

We execute the generated runtime program locally in CP (control program), i.e., within a driver process. This driver handles recompilation, runs in-memory single-node `CPInstruction` (some of which are multi-threaded ), maintains an in-memory buffer pool, and launches Spark jobs if the runtime plan contains distributed computations in the form of Spark instructions ( `SPInstruction` ). For the Spark backend, we rely on Spark's lazy evaluation and stage construction. CP instructions may also be backed by GPU kernels ( `GPUInstruction` ). The multi-level buffer pool caches local matrices in-memory, evicts them if necessary, and handles data exchange between local and distributed runtime backends. The core of SystemDS's runtime instructions is an adaptive matrix block library, which is sparsity-aware and operates on the entire matrix in CP, or blocks of a matrix in a distributed setting. Further key features include parallel for-loops for task-parallel computations, and dynamic recompilation for runtime plan adaptation addressing initial unknowns.