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Revision 351a22f0298a8475400693f9a3e946cd7270d1bc authored by Jyh-Miin Lin on 06 October 2020, 22:25:39 UTC, committed by Jyh-Miin Lin on 06 October 2020, 22:25:39 UTC
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<h1>Version history<a class="headerlink" href="#version-history" title="Permalink to this headline">¶</a></h1>
<p><strong>v2020.1.2</strong></p>
<ul class="simple">
<li><p>new_index: change the khatri_rao_k(), OMEGA_k() in helper.py; col += kindx[index];// + 1</p></li>
<li><p>Add code of conduct of Contributor Covenant v2.0</p></li>
<li><p>Update some documentations</p></li>
</ul>
<p><strong>v2020.1.0</strong></p>
<ul class="simple">
<li><p>add batch mode to nudft_cpu</p></li>
</ul>
<p><strong>v2020.0.0</strong></p>
<ul class="simple">
<li><p>fix batch=1. This can cause error in Riekna fft.</p></li>
</ul>
<p><strong>v2019.2.3</strong></p>
<p>-(experimental) Add the unified NUFFT() class. Now CPU, GPU, and legacy GPU mode are encapsuled in a single class.</p>
<p>-Tested using Intel Neo OpenCL driver (see <a class="reference external" href="https://github.com/intel/compute-runtime">https://github.com/intel/compute-runtime</a>) and IntelPython3.</p>
<p>-The old NUFFT_cpu() and NUFFT_legacy() will be kept in the system for compatibility.</p>
<p><strong>v2019.2.1-2019.2.2</strong></p>
<p>-Remove lsmr as scipy 1.13 has caused unknown error.</p>
<p><strong>v2019.2.0</strong></p>
<ul class="simple">
<li><p>Bump</p></li>
</ul>
<p><strong>v2019.1.2</strong></p>
<ul class="simple">
<li><p>BUGFIX: fix the loss of accuracy in cSpmvh(). Replace the group/local by global memory (the group/local sizes have caused the unknown run-time behaviour on cuda)</p></li>
</ul>
<p><strong>v2019.1.1</strong></p>
<ul class="simple">
<li><p>Refactor the re_subroutine.py</p></li>
<li><p>Adopt tensor form</p></li>
</ul>
<p><strong>v0.4.0.0</strong></p>
<ul class="simple">
<li><p>0.4.0.0 is a beta version.</p></li>
<li><p>Major updates for the NUFFT_hsa class, including memory reduction and split-radix. Multiple NUFFT_hsa() using cuda backend becomes possible, by pushing the context to the top of the stack when a method is called.</p></li>
<li><p>Tested in Windows 10 with PyCUDA 2018.1.1, nvidia-driver 417.35, CUDA 9.2, Visual Studio 2015 Community, and Anaconda Python 3.7 64-bit. PyOpenCL in Windows is yet to be tested.</p></li>
<li><p>Add batch mode.</p></li>
</ul>
<p><strong>v0.3.3.12</strong></p>
<ul class="simple">
<li><p>0.3.3.12 is a bug-fixed version.</p></li>
<li><p>Removal of the keyword async for compatibility reasons because Reikna has changed the keyword to <a href="#id1"><span class="problematic" id="id2">async_</span></a>.</p></li>
</ul>
<p><strong>v0.3.3.8</strong></p>
<ul class="simple">
<li><p>Bugfix: mm = numpy.tile(mm, [numpy.prod(Jd).astype(int), 1]) to fix the wrong type when numpy.prod(Jd) is not cast as int</p></li>
<li><p>Bugfix: fix rcond=None error in Anaconda 3.6.5 and Numpy 1.13.1 (the recommended None in Numpy 1.14 is backward incompatible with 1.13)</p></li>
<li><p>Bugfix: indx1 = indx.copy() is replaced by indx1 = list(indx) for Python2 compatibility</p></li>
</ul>
<p><strong>v0.3.3.7</strong></p>
<ul class="simple">
<li><p>Bugfix in 0.3.3.7 Toeplitz is removed from the NUFFT_cpu and NUFFT_gpu to avoid the MemoryError.</p></li>
</ul>
<p><strong>v0.3.3.6</strong></p>
<ul class="simple">
<li><p>Bugfix: correct the error of import. Now import NUFFT_cpu, NUFFT_hsa at the top level.</p></li>
</ul>
<p><strong>v0.3.3</strong></p>
<ul class="simple">
<li><p>Note: GPU support is superseded by Heterogeneous System Architecture (HSA).</p></li>
<li><p>A variety of nonlinear solvers are provided, including the conjugate gradient method (cg), L1 total-variation ordinary least square (L1TVOLS), and L1 total-variation least absolute deviation (L1TVLAD).</p></li>
<li><p>The CPU version support other nonlinear solvers, lsqr, gmr, cg, bicgstab, bicg, cgs, gmres, lgmres , apart from cg, L1TVOLS and L1TVLAD.</p></li>
<li><p>Support multi-dimensional transform and reconstruction (experimentally).</p></li>
</ul>
<p><strong>v0.3.2.9</strong></p>
<ul class="simple">
<li><p>Experimental support of NVIDIA’s graphic processing unit (GPU).</p></li>
<li><p>The experimental class gpuNUFFT requires pycuda, scikit-cuda, and python-cuda-cffi. scikit-cuda can be installed from standard command.</p></li>
</ul>
<p><strong>v0.3.2.8</strong></p>
<ul class="simple">
<li><p>Tested under Linux and Windows Anaconda3</p></li>
</ul>
<p><strong>v0.3</strong></p>
<ul class="simple">
<li><p>Updated setup.py</p></li>
<li><p>Removal of pyfftw due to segfault under some Linux distributions</p></li>
</ul>
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