Thanks Zach!
On Thu, Apr 28, 2022 at 10:37 AM Zach Pearson zjp@cgl.ucsf.edu wrote:
Hi Alexis,
The Intel version of ChimeraX currently available on our website appears to work well on Apple Silicon, and we are very close to a full M1 build. We anticipate releasing an M1 version of ChimeraX 1.4.
— Zach
On Apr 28, 2022, at 10:34, Alexis Rohou via ChimeraX-users < chimerax-users@cgl.ucsf.edu> wrote:
Hi Tom, ChimeraX team,
What is the status of ChimeraX on Apple M1/Silicon machines? Does it work? Well? Natively?
Cheers, Alexis
On Mon, Mar 22, 2021 at 11:11 AM Jeffrey D. Hartgerink jdh@rice.edu wrote:
Hi Tom,
Thank you for this detailed response! It illustrated the challenges well and was very useful. In particular, I was unaware of the all-or-nothing nature of Rosetta.
I’ve recently acquired an arm based Mac mini and would be happy to test any early builds if that would be useful.
-Jeff
Jeffrey D. Hartgerink, Ph.D. Prof. of Chemistry and Bioengineering Rice University, MS 602 6100 Main Street Houston, TX 77005 jdh@rice.edu / 713-348-4142 hartgerink.rice.edu
On Mar 22, 2021, at 12:58 PM, Tom Goddard goddard@sonic.net wrote:
One more detail. Our lab is getting an Apple Silicon machine so we can attempt to make a native ChimeraX version.
Tom
On Mar 22, 2021, at 10:57 AM, Tom Goddard goddard@sonic.net wrote:
In the future we would like to provide a native Apple Silicon (ARM CPU) version of ChimeraX. We have not tried it yet. It would be easy to compile the C++ code we develop for the new CPU. But the trouble is that ChimeraX depends on over 50 third party Python modules, some of them including compiled code. I believe all of those compiled python modules will have to be available compiled for ARM in order for us to make a native ChimeraX app. According to Apple's developer documentation you cannot mix ARM compiled code with Intel compiled dynamically loaded modules in the same process:
From https://developer.apple.com/documentation/apple-silicon/about-the-rosetta-tr... Important The system prevents you from mixing arm64 code and x86_64 code in the same process. Rosetta translation applies to an entire process, including all code modules that the process loads dynamically.
Major packages we use like the Qt window toolkit and numpy array module provide native ARM versions. But less well maintained modules like the PyOpenGL-accelerate module ChimeraX uses for OpenGL graphics has not been updated for more than a year (Jan 2020) and there is no telling when an ARM version will become available.
The following GitHub repository (focused on neuroimaging) describes some of the availability problems from scientific packages on ARM CPUs.
https://github.com/neurolabusc/AppleSiliconForNeuroimaging
Tom
On Mar 21, 2021, at 7:40 PM, Jeffrey D. Hartgerink jdh@rice.edu wrote:
Are there any plans for an Apple Silicon (M1 chip) native version of ChimeraX? While things work ok via the Rosetta emulator, it would be great to have the performance from a native app.
Jeffrey D. Hartgerink, Ph.D. Prof. of Chemistry and Bioengineering Rice University, MS 602 6100 Main Street Houston, TX 77005 jdh@rice.edu / 713-348-4142 hartgerink.rice.edu
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