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Dear colleagues,
After nearly 10 years of continuous development, it is our great
pleasure to announce that MCX 1.0 (v2018) has finally arrived! In
the meantime, MMC has also arrived at its v1.0 milestone, after its
initially publication in 2010. Moreover, we also proudly announce
the first stable release of MCX-OpenCL (or MCXCL) - a "clone" of MCX
that can run on nearly all CPUs and GPUs across many vendors.
These releases represent an important milestone for the MCX project
(
http://mcx.space), and signifies that MCX/MMC has grown from unique
research ideas to become mature, robust, full-featured, and general
purpose Monte Carlo simulation platform that empowers thousands of
researchers and students around the world to explore, to teach and
to create. This also marks the start of a wonderful new journey
along which many exciting new ideas, methods, features await ahead.
As of today, our combined registered MCX and MMC user number (with
unique emails) has exceeded 1,500, with people coming from every
corner of the world. The total download number in the past 7 years
has exceeded 22,500 from our Sourceforge site alone. There are over
780 academic publications cited our works, and more than 1,200
questions/replies were received in our mailing lists, subscribed by
over 250 active users. We are proud of these achievements and feel
deeply honored to contribute, even in a small way, to many ongoing
exciting new research, and are committed to continue dedicating our
efforts in maintaining and improving our software. We will continue
working with every one of you, addressing your concerns and new
feature requests, bringing the latest and fastest software to you
with transparency and openness.
Today, we celebrate MCX/MMC 1.0, we thank all the hard-works from
the developers' team, particularly those PhD students who had made
MCX a fast and better software - Fanny Nina-Paravecino, Leiming Yu,
Ruoyang Yao, Yaoshen Yuan, Shijie Yan and Anh Phong Tran, as well as
the continual support and guidance from collaborators, Dr. David
Kaeli and Dr. Xavier Intes. We also thank all the valuable feedback
received from our users, your bug reports and constructive
discussions are crucial for us to improve our software. Last, but
not the least, we thank NIH/NIGMS for funding (R01-GM114365) this
endeavor. It is not possible for us to get where we are today
without this support.
Today, we also kickoff the new development cycle for MCX 2.0! We
will continue accelerating our software by taking advantage of
emerging GPU architectures, new hardware resources and algorithm
optimizations, in the meantime, focusing on usability and broader
dissemination.
As a registered user, you can directly access the new packages at
the below site
https://sourceforge.net/projects/mcx/files/
Several
critical bugs have been fixed in the new MCX codes.
I urge everyone to upgrade from an old release to the latest version
to avoid getting incorrect results. The details of these bugs can be
found here
http://mcx.space/wiki/index.cgi?Doc/ReleaseNotes/v2018#About_this_release
Starting from this release, we provide an
all-in-one package
named MCXStudio - it contains all 7 modules (MCX, MCX-CL, MMC,
MCXLAB, MCXLAB-CL, MMCLAB and mcxstudio GUI) precompiled for
Windows/Linux and Mac. For new users, we suggest you to try the GUI
(mcxstudio) in the all-in-one package as a quick-start.
We also completely
redesigned our wiki to make
information easier to find
http://mcx.space/wiki/
you can find the release notes and detailed video tutorials in the
below links
http://mcx.space/wiki/?Get
http://mcx.space/wiki/?Learn
For those of you who could not run MCX due to the lack of NVIDIA
GPUs, please try our MCX-CL (and MCXLAB-CL) software - they are
highly compatible with MCX (MCXLAB) and
can utilize AMD/Intel
CPUs/GPUs for high-throughput MC simulations. In our paper
published earlier this year, the simulation speed on AMD GPUs is
comparable to those from NVIDIA cards. Please see our paper for
details.
https://doi.org/10.1117/1.JBO.23.1.010504
Please continue using our user forums (
http://mcx.space/wiki/?Help)
to provide your feedback or suggestions.
enjoy the new software and happy modeling!
Qianqian Fang, PhD
Computational Optics & Translational Imaging Lab (http://fanglab.org)
Northeastern University, Boston