Hi all, my 0.02$ I am working on one by one. Please add to the above list..
0. Algorithms * Factorization machines, with regression & classification capabities with the help of nn layers.[ 1437] * A test suite for the nn optimization, with well known optimization test functions. [1974] 1. Deep Learning * I am working on model selection + hyperparameter optimization, a basic implementation will be possible by January. [SYSTEMML-1973] - some components of it are in testing phase, now. * I think distributed DL is a great idea, & it may be necessary now. 2. GPU backends * Support for sparse operations - [SYSTEMML-2041] Implementation of block sparse kernel enables us to model LSTM with 10,000 hidden units, instead current state-of-the-art 1000 hidden units 6. Misc. compiler * support for single-output UDFs in expressions. * SPOOF compiler improvement * Rewrites 8. Builtin functions * Well known distribution functions - weibull, gamma etc. * Generalization of operations, such as xor, and, other operations. 9. Documentation improvement. Thanks, Janardhan On Sat, Dec 9, 2017 at 8:11 AM, Matthias Boehm <[email protected]> wrote: > 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 >
