# Intel Fortran on Snapdragon chips **URL:** \ **Category:** Uncategorized\ **Created:** [July 18, 2024, 3:17pm UTC](https://fortran-lang.discourse.group/t/intel-fortran-on-snapdragon-chips/8362 "2024-07-18T15:17:00Z")\ **Posts on this page:** 6\ **Page:** 1
**Author:** ![NC1](https://avatars.discourse-cdn.com/v4/letter/n/ee7513/32.png) [@NC1](https://fortran-lang.discourse.group/u/NC1)\ **Post date:** [July 18, 2024, 3:17pm UTC](https://fortran-lang.discourse.group/t/intel-fortran-on-snapdragon-chips/8362/1 "2024-07-18T15:17:00Z")
Hi all, I mainly use intel fortran (I have a bunch of legacy code that works fine with that compiler and it seems a massive risk to use another compiler). I mainly run my codes on a cluster (with mainly Xeon type processors) and use my laptop (intel chip too) to sometimes run some smaller codes and debug code (using VS community + Intel OneAPI). Now I am planning to buy a new laptop. And I have my eye on the new surface laptops: [https://www.microsoft.com/en-us/surface/devices/surface-laptop-7th-edition](https://www.microsoft.com/en-us/surface/devices/surface-laptop-7th-edition) Does anyone know whether the intel fortran compilers run well on snapdragon chips? In particular for OpenMP type codes. Thank you. ---
**Author:** ![sblionel](https://yyz2.discourse-cdn.com/free1/user_avatar/fortran-lang.discourse.group/sblionel/32/853_2.png) [@sblionel](https://fortran-lang.discourse.group/u/sblionel)\ **Post date:** [July 19, 2024, 12:00am UTC](https://fortran-lang.discourse.group/t/intel-fortran-on-snapdragon-chips/8362/2 "2024-07-19T00:00:53Z")
They do not run at all, and don’t generate ARM code. If you want to use the Intel compiler, buy a laptop with an Intel (or AMD) processor. ---
**Author:** ![ivanpribec](https://yyz2.discourse-cdn.com/free1/user_avatar/fortran-lang.discourse.group/ivanpribec/32/3290_2.png) [@ivanpribec](https://fortran-lang.discourse.group/u/ivanpribec)\ **Post date:** [July 19, 2024, 8:10am UTC](https://fortran-lang.discourse.group/t/intel-fortran-on-snapdragon-chips/8362/3 "2024-07-19T08:10:46Z")
I think laptops with similar AI features and Intel CPUs will arrive on the market later this year → [Intel’s Lunar Lake Processors Arriving Q3 2024](https://www.intel.com/content/www/us/en/newsroom/news/intels-lunar-lake-processors-arriving-q3-2024.html#gs.bu22nt). There are also laptops with [Intel Arc GPUs](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/mobile/a-series/overview.html#tab-blade-1-0) these days, which can be used for OpenMP target offloading (although limited to 32-bit real precision; I’ve read that software-emulated 64-bit is also available, but I don’t know how useful that is). A few more news pages: - [This is Lunar Lake — Intel’s utterly overhauled AI laptop chip that ditches memory sticks - The Verge](https://www.theverge.com/2024/6/3/24169115/intel-lunar-lake-architecture-platform-feature-reveal) - [Intel details new Lunar Lake CPUs that will go up against AMD, Qualcomm, and Apple | Ars Technica](https://arstechnica.com/gadgets/2024/06/intels-new-lunar-lake-chips-promise-big-gpu-and-npu-upgrades-minor-cpu-boosts/) - [‘Lunar Lake’ Explained: How Intel’s Moonshot Mobile CPUs Will Escalate the AI Wars | PCMag](https://www.pcmag.com/news/lunar-lake-explained-how-intels-moonshot-mobile-cpus-will-escalate-the) ---
**Author:** ![NC1](https://avatars.discourse-cdn.com/v4/letter/n/ee7513/32.png) [@NC1](https://fortran-lang.discourse.group/u/NC1)\ **Post date:** [July 19, 2024, 10:23pm UTC](https://fortran-lang.discourse.group/t/intel-fortran-on-snapdragon-chips/8362/4 "2024-07-19T22:23:13Z")
Thank you. This is useful to know. ---
**Author:** ![NC1](https://avatars.discourse-cdn.com/v4/letter/n/ee7513/32.png) [@NC1](https://fortran-lang.discourse.group/u/NC1)\ **Post date:** [July 19, 2024, 10:23pm UTC](https://fortran-lang.discourse.group/t/intel-fortran-on-snapdragon-chips/8362/5 "2024-07-19T22:23:53Z")
It seems that I need to study target offloading then. My open mp loops are very long loops … so unsure if they will be fit for GPUs. ---
**Author:** ![ivanpribec](https://yyz2.discourse-cdn.com/free1/user_avatar/fortran-lang.discourse.group/ivanpribec/32/3290_2.png) [@ivanpribec](https://fortran-lang.discourse.group/u/ivanpribec)\ **Post date:** [July 22, 2024, 8:03pm UTC](https://fortran-lang.discourse.group/t/intel-fortran-on-snapdragon-chips/8362/6 "2024-07-22T20:03:56Z")
As mentioned earlier, I’m not sure how well-suited the consumer-grade GPUs are for scientific computation with 64-bit floats. Vendors like to introduce stratification into their product lines for various purposes. However, I’ve seen some compelling examples of the Intel Arc series using OpenCL: [Intel Arc A750 does all these fluid simulations in real-time](https://www.reddit.com/r/IntelArc/comments/1b6g7ei/intel_arc_a750_does_all_these_fluid_simulations/) If by long you mean nested and with lots independent data items, then they’d be a good fit. There was an example like this in a recent blog post from Intel: [https://www.intel.com/content/www/us/en/developer/articles/technical/phasta-gpu-acceleration.html](https://www.intel.com/content/www/us/en/developer/articles/technical/phasta-gpu-acceleration.html) [This Discourse thread](https://fortran-lang.discourse.group/t/parallelization-on-gpu-with-intel-compiler/7804) also contains some useful information on the topic of OpenMP offloading. The Nvidia compilers also support a [subset of OpenMP](https://docs.nvidia.com/hpc-sdk/compilers/hpc-compilers-user-guide/index.html#openmp-use). Hopefully they’ll improve coverage of OpenMP standards in the future.