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Increment version to 3.4.1 and Release Notes for v3.4.1 #1615

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Merged
umar456 merged 3 commits into from
Oct 13, 2016
Merged

Increment version to 3.4.1 and Release Notes for v3.4.1 #1615

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4497241
Increment version to 3.4.1
shehzan10 Oct 11, 2016
d0cb8d5
Remove Tegra K1 build status from README.md
shehzan10 Oct 13, 2016
9476a9e
Updated release notes for v3.4.1
shehzan10 Oct 13, 2016
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2 changes: 1 addition & 1 deletion CMakeModules/Version.cmake
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Expand Up @@ -10,7 +10,7 @@ ENDIF()

SET(AF_VERSION_MAJOR "3")
SET(AF_VERSION_MINOR "4")
SET(AF_VERSION_PATCH "0")
SET(AF_VERSION_PATCH "1")

SET(AF_VERSION "${AF_VERSION_MAJOR}.${AF_VERSION_MINOR}.${AF_VERSION_PATCH}")
SET(AF_API_VERSION_CURRENT ${AF_VERSION_MAJOR}${AF_VERSION_MINOR})
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32 changes: 16 additions & 16 deletions README.md
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@@ -1,35 +1,35 @@
<a href="http://arrayfire.com/"><img src="http://arrayfire.com/logos/arrayfire_logo_whitebkgnd.png" width="300"></a>

ArrayFire is a general-purpose library that simplifies the process of developing
software that targets parallel and massively-parallel architectures including
ArrayFire is a general-purpose library that simplifies the process of developing
software that targets parallel and massively-parallel architectures including
CPUs, GPUs, and other hardware acceleration devices.

To achieve this goal, ArrayFire provides software developers with a high-level
abstraction of data which resides on the accelerator, the `af::array` object
To achieve this goal, ArrayFire provides software developers with a high-level
abstraction of data which resides on the accelerator, the `af::array` object
(or C-style struct).
Developers write code which performs operations on ArrayFire arrays which, in turn,
are automatically translated into near-optimal kernels that execute on the computational
device.
ArrayFire is successfully used on devices ranging from low-power mobile phones to
high-power GPU-enabled supercomputers including CPUs from all major vendors (Intel, AMD, Arm),
GPUs from the dominant manufacturers (NVIDIA, AMD, and Qualcomm), as well as a variety
device.
ArrayFire is successfully used on devices ranging from low-power mobile phones to
high-power GPU-enabled supercomputers including CPUs from all major vendors (Intel, AMD, Arm),
GPUs from the dominant manufacturers (NVIDIA, AMD, and Qualcomm), as well as a variety
of other accelerator devices on Windows, Mac, and Linux.

Several of ArrayFire's benefits include:

* [Easy to use](http://arrayfire.org/docs/gettingstarted.htm), stable,
* [Easy to use](http://arrayfire.org/docs/gettingstarted.htm), stable,
[well-documented](http://arrayfire.org/docs) API.
* Rigorously Tested for Performance and Accuracy
* Rigorously Tested for Performance and Accuracy
* Commercially Friendly Open-Source Licensing
* Commercial support from [ArrayFire](http://arrayfire.com)
* [Read about more benefits on Arrayfire.com](http://arrayfire.com/the-arrayfire-library/)

### Build and Test Status

| | Linux x86_64 | Linux armv7l | Linux aarch64 | Windows | OSX |
|:-------:|:------------:|:------------:|:-------------:|:-------:|:---:|
| Build | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build/branch/devel/) |
| Test | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrak1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrak1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test/branch/devel/) |
| | Linux x86_64 | Linux aarch64 | Windows | OSX |
|:-------:|:------------:|:-------------:|:-------:|:---:|
| Build | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/build/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/build/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/build/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/build/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/build/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/build/branch/devel/) |
| Test | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-linux/test/devel)](http://ci.arrayfire.org/job/arrayfire-linux/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-tegrax1/test/devel)](http://ci.arrayfire.org/job/arrayfire-tegrax1/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-windows/test/devel)](http://ci.arrayfire.org/job/arrayfire-windows/job/test/branch/devel/) | [![Build Status](http://ci.arrayfire.org/buildStatus/icon?job=arrayfire-osx/test/devel)](http://ci.arrayfire.org/job/arrayfire-osx/job/test/branch/devel/) |

### Installation

Expand Down Expand Up @@ -143,7 +143,7 @@ details.

### Trademark Policy

The literal mark “ArrayFire” and ArrayFire logos are trademarks of
The literal mark “ArrayFire” and ArrayFire logos are trademarks of
AccelerEyes LLC DBA ArrayFire.
If you wish to use either of these marks in your own project, please consult
[ArrayFire's Trademark Policy](http://arrayfire.com/trademark-policy/)
84 changes: 84 additions & 0 deletions docs/pages/release_notes.md
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Release Notes {#releasenotes}
==============

v3.4.1
==============

Installers
----------
* Installers for Linux, OS X and Windows
* CUDA backend now uses [CUDA 8.0](https://developer.nvidia.com/cuda-toolkit).
* Uses [Intel MKL 2017](https://software.intel.com/en-us/intel-mkl).
* CUDA Compute 2.x (Fermi) is no longer compiled into the library.
* Installer for OS X
* The libraries shipping in the OS X Installer are now compiled with Apple
Clang v7.3.1 (previouly v6.1.0).
* The OS X version used is 10.11.6 (previously 10.10.5).
* Installer for Jetson TX1 / Tegra X1
* Requires [JetPack for L4T 2.3](https://developer.nvidia.com/embedded/jetpack)
(containing Linux for Tegra r24.2 for TX1).
* CUDA backend now uses [CUDA 8.0](https://developer.nvidia.com/cuda-toolkit) 64-bit.
* Using CUDA's cusolver instead of CPU fallback.
* Uses OpenBLAS for CPU BLAS.
* All ArrayFire libraries are now 64-bit.

Improvements
------------
* Add [sparse array](\ref sparse_func) support to \ref af::eval().
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1598)</sup>
* Add OpenCL-CPU fallback support for sparse \ref af::matmul() when running on
a unified memory device. Uses MKL Sparse BLAS.
* When using CUDA libdevice, pick the correct compute version based on device.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1612)</sup>
* OpenCL FFT now also supports prime factors 7, 11 and 13.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1383)</sup>
<sup>[2](https://github.com/arrayfire/arrayfire/pull/1619)</sup>

Bug Fixes
---------
* Allow CUDA libdevice to be detected from custom directory.
* Fix `aarch64` detection on Jetson TX1 64-bit OS.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1593)</sup>
* Add missing definition of `af_set_fft_plan_cache_size` in unified backend.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1591)</sup>
* Fix intial values for \ref af::min() and \ref af::max() operations.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1594)</sup>
<sup>[2](https://github.com/arrayfire/arrayfire/pull/1595)</sup>
* Fix distance calculation in \ref af::nearestNeighbour for CUDA and OpenCL backend.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1596)</sup>
<sup>[2](https://github.com/arrayfire/arrayfire/pull/1595)</sup>
* Fix OpenCL bug where scalars where are passed incorrectly to compile options.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1595)</sup>
* Fix bug in \ref af::Window::surface() with respect to dimensions and ranges.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1604)</sup>
* Fix possible double free corruption in \ref af_assign_seq().
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1605)</sup>
* Add missing eval for key in \ref af::scanByKey in CPU backend.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1605)</sup>
* Fixed creation of sparse values array using \ref AF_STORAGE_COO.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1620)</sup>
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1621)</sup>

Examples
--------
* Add a [Conjugate Gradient solver example](\ref benchmarks/cg.cpp)
to demonstrate sparse and dense matrix operations.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1599)</sup>

CUDA Backend
------------
* When using [CUDA 8.0](https://developer.nvidia.com/cuda-toolkit),
compute 2.x are no longer in default compute list.
* This follows [CUDA 8.0](https://developer.nvidia.com/cuda-toolkit)
deprecating computes 2.x.
* Default computes for CUDA 8.0 will be 30, 50, 60.
* When using CUDA pre-8.0, the default selection remains 20, 30, 50.
* CUDA backend now uses `-arch=sm_30` for PTX compilation as default.
* Unless compute 2.0 is enabled.

Known Issues
------------
* \ref af::lu() on CPU is known to give incorrect results when built run on
OS X 10.11 or 10.12 and compiled with Accelerate Framework.
<sup>[1](https://github.com/arrayfire/arrayfire/pull/1617)</sup>
* Since the OS X Installer libraries uses MKL rather than Accelerate
Framework, this issue does not affect those libraries.


v3.4.0
==============

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