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From Matthias Boehm <mboe...@gmail.com>
Subject [DISCUSS] Roadmap SystemML 1.1 and beyond
Date Sat, 09 Dec 2017 02:41:46 GMT
Hi all,

with our SystemML 1.0 release around the corner, I think we should start
the discussion on the roadmap for SystemML 1.1 and beyond. Below is an
initial list as a starting point, but please help to add relevant items,
especially for algorithms and APIs, which are barely covered so far.

1) Deep Learning
 * Full compiler integration GPU backend
 * Extended sparse operations on CPU/GPU
 * Extended single-precision support CPU
 * Distributed DL operations?

2) GPU Backend
 * Full support for sparse operations
 * Automatic decisions on CPU vs GPU operations
 * Graduate GPU backends (enable by default)

3) Code generation
 * Graduate code generation (enable by default)
 * Support for deep learning operations
 * Code generation for the heterogeneous HW, incl GPUs

4) Compressed Linear Algebra
 * Support for matrix-matrix multiplications
 * Support for deep learning operations
 * Improvements for ultra-sparse datasets

5) Misc Runtime
 * Large dense matrix blocks > 16GB
 * NUMA-awareness (thread pools, matrix partitioning)
 * Unified memory management (ops, bufferpool, RDDs/broadcasts)
 * Support feather format for matrices and frames
 * Parfor support for broadcasts
 * Extended support for multi-threaded operations
 * Boolean matrices

6) Misc Compiler
 * Support single-output UDFs in expressions
 * Consolidate replicated compilation chain (e.g., diff APIs)
 * Holistic sum-product optimization and operator fusion
 * Extended sparsity estimators
 * Rewrites and compiler improvements for mini-batching
 * Parfor optimizer support for shared reads

7) APIs
 * Python Binding for JMLC API
 * Consistency Python/Java APIs


Regards,
Matthias

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