Release Notes

v3.4.2

Deprecation Announcement

This release supports CUDA 6.5 and higher. The next ArrayFire relase will support CUDA 7.0 and higher, dropping support for CUDA 6.5. Reasons for no longer supporting CUDA 6.5 include:

  • CUDA 7.0 NVCC supports the C++11 standard (whereas CUDA 6.5 does not), which is used by ArrayFire's CPU and OpenCL backends.
  • Very few ArrayFire users still use CUDA 6.5.

As a result, the older Jetson TK1 / Tegra K1 will no longer be supported in the next ArrayFire release. The newer Jetson TX1 / Tegra X1 will continue to have full capability with ArrayFire.

Docker

Improvements

  • Implemented sparse storage format conversions between AF_STORAGE_CSR and AF_STORAGE_COO. 1
  • Added cast support for sparse arrays. 1
    • Casting only changes the values array and the type. The row and column index arrays are not changed.
  • Reintroduced automated computation of chart axes limits for graphics functions. 1
    • The axes limits will always be the minimum/maximum of the current and new limit.
    • The user can still set limits from API calls. If the user sets a limit from the API call, then the automatic limit setting will be disabled.
  • Using boost::scoped_array instead of boost::scoped_ptr when managing array resources. 1
  • Internal performance improvements to getInfo() by using const references to avoid unnecessary copying of ArrayInfo objects. 1
  • Added support for scalar af::array inputs for af::convolve() and set functions. 1 2 3
  • Performance fixes in af::fftConvolve() kernels. 1 2

Build

  • Support for Visual Studio 2015 compilation. 1 2
  • Fixed FindCBLAS.cmake when PkgConfig is used. 1

Bug fixes

Installers

  • Major OS X installer fixes. 1
    • Fixed installation scripts.
    • Fixed installation symlinks for libraries.
  • Windows installer now ships with more pre-built examples.

Examples

Documentation

CUDA 8 on OSX

Known Issues

  • Known failures with CUDA 6.5. These include all functions that use sorting. As a result, sparse storage format conversion between AF_STORAGE_COO and AF_STORAGE_CSR has been disabled for CUDA 6.5.

v3.4.1

Installers

  • Installers for Linux, OS X and Windows
    • CUDA backend now uses CUDA 8.0.
    • Uses Intel MKL 2017.
    • 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 (previously 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 (containing Linux for Tegra r24.2 for TX1).
    • CUDA backend now uses CUDA 8.0 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 support to af::eval(). 1
  • Add OpenCL-CPU fallback support for sparse 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. 1
  • OpenCL FFT now also supports prime factors 7, 11 and 13. 1 2

Bug Fixes

  • Allow CUDA libdevice to be detected from custom directory.
  • Fix aarch64 detection on Jetson TX1 64-bit OS. 1
  • Add missing definition of af_set_fft_plan_cache_size in unified backend. 1
  • Fix intial values for af::min() and af::max() operations. 1 2
  • Fix distance calculation in af::nearestNeighbour for CUDA and OpenCL backend. 1 2
  • Fix OpenCL bug where scalars where are passed incorrectly to compile options. 1
  • Fix bug in af::Window::surface() with respect to dimensions and ranges. 1
  • Fix possible double free corruption in af_assign_seq(). 1
  • Add missing eval for key in af::scanByKey in CPU backend. 1
  • Fixed creation of sparse values array using AF_STORAGE_COO. 1 1

Examples

CUDA Backend

  • When using CUDA 8.0, compute 2.x are no longer in default compute list.
    • This follows CUDA 8.0 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

  • 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. 1
    • Since the OS X Installer libraries uses MKL rather than Accelerate Framework, this issue does not affect those libraries.

v3.4.0

Major Updates

Features

Bug Fixes

  • Fixes to edge-cases in Morphological Operations. 1
  • Makes JIT tree size consistent between devices. 1
  • Delegate higher-dimension in Convolutions to correct dimensions. 1
  • Indexing fixes with C++11. 1 2
  • Handle empty arrays as inputs in various functions. 1
  • Fix bug when single element input to af::median. 1
  • Fix bug in calculation of time from af::timeit(). 1
  • Fix bug in floating point numbers in af::seq. 1
  • Fixes for OpenCL graphics interop on NVIDIA devices. 1
  • Fix bug when compiling large kernels for AMD devices. 1
  • Fix bug in af::bilateral when shared memory is over the limit. 1
  • Fix bug in kernel header compilation tool bin2cpp. 1
  • Fix inital values for Morphological Operations functions. 1
  • Fix bugs in af::homography() CPU and OpenCL kernels. 1
  • Fix bug in CPU TNJ. 1

Improvements

  • CUDA 8 and compute 6.x(Pascal) support, current installer ships with CUDA 7.5. 1 2 3
  • User controlled FFT plan caching. 1
  • CUDA performance improvements for wrap, unwrap and Interpolation and approximation. 1
  • Fallback for CUDA-OpenGL interop when no devices does not support OpenGL. 1
  • Additional forms of batching with the transform functions. New behavior defined here. 1
  • Update to OpenCL2 headers. 1
  • Support for integration with external OpenCL contexts. 1
  • Performance improvements to interal copy in CPU Backend. 1
  • Performance improvements to af::select and af::replace CUDA kernels. 1
  • Enable OpenCL-CPU offload by default for devices with Unified Host Memory. 1
    • To disable, use the environment variable AF_OPENCL_CPU_OFFLOAD=0.

Build

  • Compilation speedups. 1
  • Build fixes with MKL. 1
  • Error message when CMake CUDA Compute Detection fails. 1
  • Several CMake build issues with Xcode generator fixed. 1 2
  • Fix multiple OpenCL definitions at link time. 1
  • Fix lapacke detection in CMake. 1
  • Update build tags of
  • Fix builds with GCC 6.1.1 and GCC 5.3.0. 1

Installers

  • All installers now ship with ArrayFire libraries build with MKL 2016.
  • All installers now ship with Forge development files and examples included.
  • CUDA Compute 2.0 has been removed from the installers. Please contact us directly if you have a special need.

Examples

Documentation & Licensing

Deprecations

The following functions have been deprecated and may be modified or removed permanently from future versions of ArrayFire.

Known Issues

Certain CUDA functions are known to be broken on Tegra K1. The following ArrayFire tests are currently failing:

  • assign_cuda
  • harris_cuda
  • homography_cuda
  • median_cuda
  • orb_cudasort_cuda
  • sort_by_key_cuda
  • sort_index_cuda

v3.3.2

Improvements

Bug Fixes

Build

Documentation

Installer

  • Linux x64 installers will now be compiled with GCC 4.9.2.
  • OSX installer gives better error messages on brew failures and now includes link to Fixing OS X Installer Failures for brew installation failures.

v3.3.1

Bug Fixes

Build

  • CMake compiles files in alphabetical order.
  • CMake fixes for BLAS and LAPACK on some Linux distributions.

Improvements

Documentation

  • Reorganized, cleaner README file.
  • Replaced non-free lena image in assets with free-to-distribute lena image.

v3.3.0

Major Updates

  • CPU backend supports aysnchronous execution.
  • Performance improvements to OpenCL BLAS and FFT functions.
  • Improved performance of memory manager.
  • Improvements to visualization functions.
  • Improved sorted order for OpenCL devices.
  • Integration with external OpenCL projects.

Features

Bug Fixes

Improvements

  • Optionally offload BLAS and LAPACK functions to CPU implementations to improve performance.
  • Performance improvements to the memory manager.
  • Error messages are now more detailed.
  • Improved sorted order for OpenCL devices.
  • JIT heuristics can now be tweaked using environment variables. See Environment Variables tutorial.
  • Add BUILD_<BACKEND> options to examples and tests to toggle backends when compiling independently.

Examples

Build

  • Support for Intel icc compiler
  • Support to compile with Intel MKL as a BLAS and LAPACK provider
  • Tests are now available for building as standalone (like examples)
  • Tests can now be built as a single file for each backend
  • Better handling of NONFREE build options
  • Searching for GLEW in CMake default paths
  • Fixes for compiling with MKL on OSX.

Installers

  • Improvements to OSX Installer
    • CMake config files are now installed with libraries
    • Independent options for installing examples and documentation components

Deprecations

  • af_lock_device_arr is now deprecated to be removed in v4.0.0. Use af_lock_array() instead.
  • af_unlock_device_arr is now deprecated to be removed in v4.0.0. use af_unlock_array() instead.

Documentation

  • Fixes to documentation for matchTemplate().
  • Improved documentation for deviceInfo.
  • Fixes to documentation for exp().

Known Issues

v3.2.2

Bug Fixes

Build

  • Added a MIN_BUILD_TIME option to build with minimum optimization compiler flags resulting in faster compile times
  • Fixed issue in CBLAS detection by CMake
  • Fixed tests failing for builds without optional components FreeImage and LAPACK
  • Added a test for unified backend
  • Only info and backend tests are now built for unified backend
  • Sort tests execution alphabetically
  • Fixed compilation flags and errors in tests and examples
  • Moved AF_REVISION and AF_COMPILER_STR into src/backend. This is because as revision is updated with every commit, entire ArrayFire would have to be rebuilt in the old code.
  • Clean up examples
    • Remove getchar for Windows (this will be handled by the installer)
    • Other miscellaneous code cleanup
    • Fixed bug in plot3.cpp example
  • Rename clBLAS/clFFT external project suffix from external -> ext
  • Add OpenBLAS as a lapack/lapacke alternative

Improvements

  • Added AF_MEM_INFO macro to print memory info from ArrayFire's memory manager (cross issue)
  • Added additional paths for searching for libaf* for Unified backend on unix-style OS.
    • Note: This still requires dependencies such as forge, CUDA, NVVM etc to be in LD_LIBRARY_PATH as described in Unified Backend
  • Create streams for devices only when required in CUDA Backend

Documentation

v3.2.1

Bug Fixes

Build

  • Tests can now be used as a standalone project
    • Tests can now be built using pre-compiled libraries
    • Similar to how the examples are built
  • The install target now installs the examples source irrespective of the BUILD_EXAMPLES value
    • Examples are not built if BUILD_EXAMPLES is off

Documentation

  • HTML documentation is now built and installed in docs/html
  • Added documentation for af::seq class
  • Updated Matrix Manipulation tutorial
  • Examples list is now generated by CMake
    • Examples are now listed as dir/example.cpp
  • Removed dummy groups used for indexing documentation (affcted doxygen < 1.8.9)

v3.2.0

Major Updates

  • Added Unified backend
  • Support for 16-bit integers (s16 and u16)
    • All functions that support 32-bit interger types (s32, u32), now also support 16-bit interger types

Function Additions

Other Improvements

Build Improvements

  • Submodules update is now automatically called if not cloned recursively
  • Fixes for compilation on Visual Studio 2015
  • Option to use fallback to CPU LAPACK for linear algebra functions in case of CUDA 6.5 or older versions.

Bug Fixes

Documentation Updates

  • Improved tutorials documentation
  • Added return type information for functions that return different type arrays

New Examples

Installers

  • All installers now include the Unified backend and corresponding CMake files
  • Visual Studio projects include Unified in the Platform Configurations
  • Added installer for Jetson TX1
  • SIFT and GLOH do not ship with the installers as SIFT is protected by patents that do not allow commercial distribution without licensing.

v3.1.3

Bug Fixes

  • Fixed bugs in various OpenCL kernels without offset additions
  • Remove ARCH_32 and ARCH_64 flags
  • Fix missing symbols when freeimage is not found
  • Use CUDA driver version for Windows
  • Improvements to SIFT
  • Fixed memory leak in median
  • Fixes for Windows compilation when not using MKL #1047
  • Fixed for building without LAPACK

Other

  • Documentation: Fixed documentation for select and replace
  • Documentation: Fixed documentation for af_isnan

v3.1.2

Bug Fixes

  • Fixed bug in assign that was causing test to fail
  • Fixed bug in convolve. Frequency condition now depends on kernel size only
  • Fixed bug in indexed reductions for complex type in OpenCL backend
  • Fixed bug in kernel name generation in ireduce for OpenCL backend
  • Fixed non-linear to linear indices in ireduce
  • Fixed bug in reductions for small arrays
  • Fixed bug in histogram for indexed arrays
  • Fixed compiler error CPUID for non-compliant devices
  • Fixed failing tests on i386 platforms
  • Add missing AFAPI

Other

  • Documentation: Added missing examples and other corrections
  • Documentation: Fixed warnings in documentation building
  • Installers: Send error messages to log file in OSX Installer

v3.1.1

Installers

  • CUDA backend now depends on CUDA 7.5 toolkit
  • OpenCL backend now require OpenCL 1.2 or greater

Bug Fixes

  • Fixed bug in reductions after indexing
  • Fixed bug in indexing when using reverse indices

Build

  • cmake now includes PKG_CONFIG in the search path for CBLAS and LAPACKE libraries
  • heston_model.cpp example now builds with the default ArrayFire cmake files after installation

Other

v3.1.0

Function Additions

  • Computer Vision Functions
    • nearestNeighbour() - Nearest Neighbour with SAD, SSD and SHD distances
    • harris() - Harris Corner Detector
    • susan() - Susan Corner Detector
    • sift() - Scale Invariant Feature Transform (SIFT)
      • Method and apparatus for identifying scale invariant features" "in an image and use of same for locating an object in an image," David" "G. Lowe, US Patent 6,711,293 (March 23, 2004). Provisional application" "filed March 8, 1999. Asignee: The University of British Columbia. For" "further details, contact David Lowe (lowe@.nosp@m.cs.u.nosp@m.bc.ca) or the" "University-Industry Liaison Office of the University of British" "Columbia.")
      • SIFT is available for compiling but does not ship with ArrayFire hosted installers/pre-built libraries
    • dog() - Difference of Gaussians
  • Image Processing Functions
  • Array & Data Handling
    • copy() - Copy
    • array::lock() and array::unlock() - Lock and Unlock
    • select() and replace() - Select and Replace
    • Get array reference count (af_get_data_ref_count)
  • Signal Processing
  • Linear Algebra
  • Other operations
    • sigmoid() - Sigmoid
    • Sum (with option to replace NaN values)
    • Product (with option to replace NaN values)
  • Graphics
    • Window::setSize() - Window resizing using Forge API
  • Utility
    • Allow users to set print precision (print, af_print_array_gen)
    • saveArray() and readArray() - Stream arrays to binary files
    • toString() - toString function returns the array and data as a string
  • CUDA specific functionality
    • getStream() - Returns default CUDA stream ArrayFire uses for the current device
    • getNativeId() - Returns native id of the CUDA device

Improvements

  • dot
    • Allow complex inputs with conjugate option
  • AF_INTERP_LOWER interpolation
    • For resize, rotate and transform based functions
  • 64-bit integer support
    • For reductions, random, iota, range, diff1, diff2, accum, join, shift and tile
  • convolve
    • Support for non-overlapping batched convolutions
  • Complex Arrays
    • Fix binary ops on complex inputs of mixed types
    • Complex type support for exp
  • tile
    • Performance improvements by using JIT when possible.
  • Add AF_API_VERSION macro
    • Allows disabling of API to maintain consistency with previous versions
  • Other Performance Improvements
    • Use reference counting to reduce unnecessary copies
  • CPU Backend
    • Device properties for CPU
    • Improved performance when all buffers are indexed linearly
  • CUDA Backend
    • Use streams in CUDA (no longer using default stream)
    • Using async cudaMem ops
    • Add 64-bit integer support for JIT functions
    • Performance improvements for CUDA JIT for non-linear 3D and 4D arrays
  • OpenCL Backend
    • Improve compilation times for OpenCL backend
    • Performance improvements for non-linear JIT kernels on OpenCL
    • Improved shared memory load/store in many OpenCL kernels (PR 933)
    • Using cl.hpp v1.2.7

Bug Fixes

  • Common
    • Fix compatibility of c32/c64 arrays when operating with scalars
    • Fix median for all values of an array
    • Fix double free issue when indexing (30cbbc7)
    • Fix bug in rank
    • Fix default values for scale throwing exception
    • Fix conjg raising exception on real input
    • Fix bug when using conjugate transpose for vector input
    • Fix issue with const input for array_proxy::get()
  • CPU Backend
    • Fix randn generating same sequence for multiple calls
    • Fix setSeed for randu
    • Fix casting to and from complex
    • Check NULL values when allocating memory
    • Fix offset issue for CPU element-wise operations

New Examples

  • Match Template
  • Susan
  • Heston Model (contributed by Michael Nowotny)

Installer

  • Fixed bug in automatic detection of ArrayFire when using with CMake in Windows
  • The Linux libraries are now compiled with static version of FreeImage

Known Issues

  • OpenBlas can cause issues with QR factorization in CPU backend
  • FreeImage older than 3.10 can cause issues with loadImageMem and saveImageMem
  • OpenCL backend issues on OSX
    • AMD GPUs not supported because of driver issues
    • Intel CPUs not supported
    • Linear algebra functions do not work on Intel GPUs.
  • Stability and correctness issues with open source OpenCL implementations such as Beignet, GalliumCompute.

v3.0.2

Bug Fixes

Documentation

  • Added missing documentation for constant()
  • Added missing documentation for array::scalar()
  • Added supported input types for functions in arith.h

v3.0.1

Bug Fixes

  • Fixed header to work in Visual Studio 2015
  • Fixed a bug in batched mode for FFT based convolutions
  • Fixed graphics issues on OSX
  • Fixed various bugs in visualization functions

Other improvements

  • Improved fractal example
  • New OSX installer
  • Improved Windows installer
    • Default install path has been changed
  • Fixed bug in machine learning examples


v3.0.0

Major Updates

  • ArrayFire is now open source
  • Major changes to the visualization library
  • Introducing handle based C API
  • New backend: CPU fallback available for systems without GPUs
  • Dense linear algebra functions available for all backends
  • Support for 64 bit integers

Function Additions

  • Data generation functions
  • Computer Vision Algorithms
    • features()
      • A data structure to hold features
    • fast()
      • FAST feature detector
    • orb()
      • ORB A feature descriptor extractor
  • Image Processing
  • Linear Algebra
  • Other functions
    • lookup() - lookup indices from a table
    • batchFunc() - helper function to perform batch operations
  • Visualization functions
    • Support for multiple windows
    • window.hist()
      • Visualize the output of the histogram
  • C API
    • Removed old pointer based C API
    • Introducing handle base C API
    • Just In Time compilation available in C API
    • C API has feature parity with C++ API
    • bessel functions removed
    • cross product functions removed
    • Kronecker product functions removed

Performance Improvements

  • Improvements across the board for OpenCL backend

API Changes

  • print is now af_print()
  • seq(): The step parameter is now the third input
    • seq(start, step, end) changed to seq(start, end, step)
  • gfor(): The iterator now needs to be seq()

Deprecated Function APIs

Deprecated APIs are in af/compatible.h

Known Issues

  • OpenCL backend issues on OSX
    • AMD GPUs not supported because of driver issues
    • Intel CPUs not supported
    • Linear algebra functions do not work on Intel GPUs.
  • Stability and correctness issues with open source OpenCL implementations such as Beignet, GalliumCompute.