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Posted to dev@systemml.apache.org by Matthias Boehm <mb...@gmail.com> on 2017/12/09 02:41:46 UTC
[DISCUSS] Roadmap SystemML 1.1 and beyond
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
Re: [DISCUSS] Roadmap SystemML 1.1 and beyond
Posted by Janardhan Pulivarthi <ja...@gmail.com>.
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 <mb...@gmail.com> 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
>