HSA Foundation releases specification, compiler and driver HSA Spec

AMD Announces Heterogeneous C++ AMP Language for Developers here

PyOpenCL 2014.1 here

Fedora 21 supports OpenCl "out of the box" here

OpenCl 2.0 SDK AMD (beta) with Bolt 1.3: SDK 3.0
OpenCl 2.0 SDK Intel: SDK 2014
New version of CodeXL

Some AVX docs
Introduction to AVX
Matrix transpose with AVX
Overview of AVX
Visual Studio 2013 supports AVX2

a muse on vectorized computing SSE/AVX/AVX2/OpenCL e.g. GPU

there has been quite a bit of talk about vectorized computing i.e. the use os GPU and AVX2 for highly vectorized computing etc

i actually did a little bit of experiment, i'm running on haswell i7 4771 (non-k)
asus h87-pro motherboard & 16G 1600ghz ram

i tried openblas
http://www.openblas.net/
https://github.com/xianyi/OpenBLAS

./dlinpack.goto
these are the benchmarks

   SIZE       Residual     Decompose            Solve           Total
   100 :   4.529710e-14      503.14 MFlops    2000.00 MFlops     514.36 MFlops
   500 :   1.103118e-12     8171.54 MFlops    3676.47 MFlops    8112.38 MFlops
  1000 :   5.629275e-12    45060.27 MFlops    2747.25 MFlops   43075.87 MFlops
  5000 :   1.195055e-11   104392.81 MFlops    3275.04 MFlops  102495.20 MFlops
 10000 :   1.529443e-11   129210.71 MFlops    3465.54 MFlops  127819.77 MFlops

ok quite impressive ~128 Gflops on a haswell i7 desktop PC running at only 3.7ghz!
that almost compare to an 'old' supercomputer Numerical wind tunnel in Tokyo
https://en.wikipedia.org/wiki/Numerical_Wind_Tunnel_%28Japan%29

but what become immediately very apparent is also that only very large matrices 10,000 x 10,000 benefits from the vectorized codes (i.e. AVX2) in the *decompose* part. if you have tiny matrices say 100x100 in size that gives a paltry 514.36 Mflops, less than 100 times (or could say 1/200) of that speed of 10,000 x 10,000.

The other apparent thing is the *solve* part of the computation, you could see that while the decompose part which involves a matrix multiplication (e.g. DGEMM) can reach speeds of 128 Ghz, *but* the *solve* part *did not benefit* from all that AVX2 vectorized codes showing little improvements for different matrices sizes!

this has major implications, it means that whether you have a good cpu with AVX2 etc or that you have a large GPU that can process say vectorized / parallel 1000s floating point calcs per clock cycle.

But if your problems are small (e.g. 100x100) or that it cannot benefit from such vectorized codes much of that GPU capacity and even for this instance AVX2 may simply be *unused* and will *not benefit* from all that expensive vectorized hardware (e.g. AVX2 and expensive GPU cards capable of processing possibly thousands of vector computation per cycle, e.g. thousands of gpu simd cores)

i'd guess this reflect in a way Amdahl's law
https://en.wikipedia.org/wiki/Amdahl%27s_law

Gene Amdahl passed away recently & perhaps this could be a little tribute to him for having 'seen so far ahead' from back then.
http://www.latimes.com/local/obituaries/la-me-gene-amdahl-20151116-story.html

This week, during SC15, Kronos Group released OpenCl 2.1 specifications, tools and compilers (with SPIR 1.0). Soon, the release of OpenCl C++ kernel.

CodeXL 1.8 has a lot of new features:

Support for Microsoft Windows® 10
HSA Profiler
Power Profiling discrete GPUs
Power Profiling API
Offline build and analysis of OpenGL shaders
New Static Analyzer enhanced ISA view
GPU Debugger support up to OpenGL 4.5
64-bit offline build for OpenCLâ„¢ kernels
CPU Profiling for the “Carrizo” APU
CPU Profiling on VMWare

New CodeXl is out.
CodeXL 1.9 includes:
Build and Static Analysis of these types of OpenGL® shaders on Microsoft Windows® and Linux®:
Vertex
Tessellation Evaluation
Geometric
Fragment
Compute
Faster processing of CPU profile data;
Support for longer CPU profiling sessions;
API for controlling CPU Profiling data collection from app code;
HSAIL Debugger (beta)
Support for Ubuntu 15.04
Many user-experience enhancements plus more.

Code XL 2.0 is here (and now is open source!!).

Now CodeXl is 2.1 and is on GitHub

CodeXl 2.2 here
Intel CodeBuilder OpenCl for Visual Studio here
Visual Studio 15 preview 4 here
Gcc 6.2 here

Hip is now 1.0, with a lot of new features
PyOpenCl 2016.2 supports Spir-V

Visual Studio 15 preview 4 here

Visual Studio 2017 is now in RC phase

Visual Studio 2017 is here.
And Codeplay releases the first open beta of ComputeCpp, their implementation of SYCL to bring C++ to OpenCl

New KDevelop 5.1 supports OpenCl (and c/c++/c#,java,bash,fortran,pascal,python,etc...) and runs also on Windows and not only on Linux.

Arm opens up Compute Library with OpenCl
Arm Compute Library

I'll consider starting to make some BOINC applications for OpenCL AFTER I find and take an online class in OpenCL that is intended for programming GPUs, not FPGAs. I cannot travel enough to use in-person classes instead.

BOINC now supports use of CUDA and OpenCL; I've seen no information on whether it can also handle the various other ways of programming GPUs that have already been mentioned in this thread. I'd expect library compatibility problems to block use of some of them at least until future versions of BOINC build in replacements for the incompatible sections of the libraries.

OpenCl 2.2 is official.
- Support to OpencCl C++
- Spir 1.2
- SyCl 2.2
and other intersting things

During IWOCL Kronos Group has annunced that they wants to unify OpenCl and Vulkan Api. That's very interesting!

ROCm is now at 1.6 version