Hi all, We have just NEP 54, "SIMD infrastructure evolution: adopting Google Highway when moving to C++?", with Draft status after a long review at https://github.com/numpy/numpy/pull/24138. It looks like it wasn't sent to this list before.
Please see https://numpy.org/neps/nep-0054-simd-cpp-highway.html for the rendered version (complete text below). This is a complex topic, and the NEP captures more a discussion on the pros and cons of moving to Highway, and in what form. Most folks active in working on SIMD code in NumPy have weighed in in one of several calls, in the community meeting and the 3-weekly meeting of the recently formed NumPy Optimization Team. I think we can summarize the current status as follows: - Google Highway is now included in the main repo as a git submodule - We are +1 on using Highway for high-level operations where possible given accuracy constraints, and are already doing so for sorting functionality. - We are -1 on using Highway's dynamic dispatch, we prefer to stay with the current dynamic dispatch support via build system support, which has worked well for us for ~4 years now. - We are +0 to +0.5 on using Highway's form of 'universal intrinsics', in preference of moving our own universal intrinsics from C to C++. Both would be a major improvement on the current state of our C implementation. - For that latter decision, there isn't complete consensus on it, and also Highway is missing a few things that NumPy does have that we'd like to see it gain. In particular, a way to prototype and test new SIMD intrinsics from Python (see https://numpy.org/neps/nep-0054-simd-cpp-highway.html#the-simd-unit-testing-module ). Cheers, Ralf full text of the NEP: =================================================================================== NEP 54 — SIMD infrastructure evolution: adopting Google Highway when moving to C++? =================================================================================== :Author: Sayed Adel, Jan Wassenberg, Matti Picus, Ralf Gommers, Chris Sidebottom :Status: Draft :Type: Standards Track :Created: 2023-07-06 :Resolution: TODO Abstract -------- We are moving the SIMD intrinsic framework, Universal Intrinsics, from C to C++. We have also moved to Meson as the build system. The Google Highway intrinsics project is proposing we use Highway instead of our Universal Intrinsics as described in `NEP 38`_. This is a complex and multi-faceted decision - this NEP is an attempt to describe the trade-offs involved and what would need to be done. Motivation and Scope -------------------- We want to refactor the C-based Universal Intrinsics (see :ref:`NEP 38 <NEP38>`) to C++. This work was ongoing for some time, and Google's Highway was suggested as an alternative, which was already written in C++ and had support for scalable SVE and other reusable components (such as VQSort). The move from C to C++ is motivated by (a) code readability and ease of development, (b) the need to add support for sizeless SIMD instructions (e.g., ARM's SVE, RISC-V's RVV). As an example of the readability improvement, here is a typical line of C code from our current C universal intrinsics framework: .. code:: // The @name@ is the numpy-specific templating in .c.src files npyv_@sfx@ a5 = npyv_load_@sfx@(src1 + npyv_nlanes_@sfx@ * 4); This will change (as implemented in PR `gh-21057`_) to: .. code:: C++ auto a5 = Load(src1 + nlanes * 4); If the above C++ code were to use Highway under the hood it would look quite similar, it uses similarly understandable names as ``Load`` for individual portable intrinsics. The ``@sfx`` in the C version above is the template variable for type identifiers, e.g.: ``#sfx = u8, s8, u16, s16, u32, s32, u64, s64, f32, f64#``. Explicit use of bitsize-encoded types like this won't work for sizeless SIMD instruction sets. With C++ this is easier to handle; PR `gh-21057`_ shows how and contains more complete examples of what the C++ code will look like. The scope of this NEP includes discussing most relevant aspects of adopting Google Highway to replace our current Universal Intrinsics framework, including but not limited to: - Maintainability, domain expertise availability, ease of onboarding new contributor, and other social aspects, - Key technical differences and constraints that may impact NumPy's internal design or performance, - Build system related aspects, - Release timing related aspects. Out of scope (at least for now) is revisiting other aspects of our current SIMD support strategy: - accuracy vs. performance trade-offs when adding SIMD support to a function - use of SVML and x86-simd-sort (and possibly its equivalents for aarch64) - pulling in individual bits or algorithms of Highway (as in `gh-24018`_) or SLEEF (as discussed in that same PR) Usage and Impact ---------------- N/A - there will be no significant user-visible changes. Backward compatibility ---------------------- There will be no changes in user-facing Python or C APIs: all the methods to control compilation and runtime CPU feature selection should remain, although there may be some changes due to moving to C++ without regards to the Highway/Universal Intrinsics choice. The naming of the CPU features in Highway is different from that of the Universal Intrinsics (see "Supported features/targets" below) On Windows, MSVC may have to be avoided, as a result of Highway's use of pragmas which are less well supported by MSVC. This means that we likely have to build our wheels with clang-cl or Mingw-w64. Both of those should work - we merged clang-cl support a while back (see `gh-20866`_), and SciPy builds with Mingw-w64. It may however impact other redistributors or end users who build from source on Windows. In response to the earlier dicussions around this NEP, Highway is now dual-licensed as Apache 2 / BSD-3. High-level considerations ------------------------- .. note:: Currently this section attempts to cover each topic separately, and comparing the future use of a NumPy-specific C++ implementation vs. use of Google Highway with our own numerical routines on top of that. It does not (yet) assume a decision or proposed decision is made. Hence this NEP is not "this is proposed" with another option in the Alternatives section, but rather a side-by-side comparison. Development effort and long-term maintainability ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Moving to Highway is likely to be a significant development effort. Longer-term, this will hopefully be offset by Highway itself having more maintainer bandwidth to deal with ongoing issues in compiler support and adding new platforms. Highway being used by other projects, like Chromium and `JPEG XL`_ (see `this more complete list < https://google.github.io/highway/en/master/README.html#examples>`__ in the Highway documentation), does imply that there is likely to be a benefit of a wider range of testing and bug reporting/fixing. One concern is that new instructions may have to be added, and that that is often best done as part of the process of developing the numerical kernel that needs the instruction. This will be a little more clumsy if the instruction lives in Highway which is a git submodule inside the NumPy repo - there will be a need to implement a temporary/generic version first, and then update the submodule after upstreaming the new intrinsic. Documentation-wise, Highway would be a clear win. NumPy's `CPU/SIMD Optimizations`_ docs are fairly sparse compared to `the Highway docs`_. Migration strategy - can it be gradual? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This is a story of two halves. Moving to Highway's statically dispatched intrinsics could be done gradually, as already seen in PR `gh-24018`_. However, adopting Highway's way of performing runtime dispatching has to be done in one go - we can't (or shouldn't) have two ways of doing that. Highway policies for compiler and platform support ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When adding new instructions, Highway has a policy that they must be implemented in a way that fairly balances across CPU architectures. Regarding the support status and whether all currently-supported architectures will remain supported, Jan stated that Highway can commit to the following: 1. If it cross-compiles with Clang and can be tested via standard QEMU, it can go into Highway's CI. 2. If it cross-compiles via clang/gcc and can be tested with a new QEMU (possibly with extra flags), then it can be support via manual testing before each Highway release. 3. Existing targets will remain supported as long as they compile/run in QEMU. Highway is not subject to Google's "no longer supported" strategy (or, as written in its README, *This is not an officially supported Google product*). That is not a bad thing; it means that it is less likely to go unsupported due to a Google business decision about the project. Quite a few well-known open source projects under the ``google`` GitHub org state this, e.g. `JAX`_ and `tcmalloc`_. Supported features/targets ~~~~~~~~~~~~~~~~~~~~~~~~~~ Both frameworks support a large set of platforms and SIMD instruction sets, as well as generic scalar/fallback versions. The main differences right now are: - NumPy supports IBM Z-system (s390x, VX/VXE/VXE2) while Highway supports Z14, Z15. - Highway supports ARM SVE/SVE2 and RISC-V RVV (sizeless instructions), while NumPy does not. - The groundwork for sizeless SIMD support in NumPy has been done in `gh-21057`_, however SVE/SVE2 and RISC-V are not yet implemented there. There is also a difference in the granularity of instruction set groups: NumPy supports a more granular set of architectures than Highway. See the list of targets for Highway `here <https://github.com/google/highway/#targets>`__ (it's roughly per CPU family) and for NumPy `here < https://numpy.org/doc/1.25/reference/simd/build-options.html#supported-features >`__ (roughly per SIMD instruction set). Hence with Highway we'd lose some granularity - but that is probably fine, we don't really need this level of granularity, and there isn't much evidence that users explicitly play with this to squeeze out the last bit of performance for their own CPU. Compilation strategy for multiple targets and runtime dispatching ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Highway compiles once while using preprocessing tricks to generate multiple stanzas for each CPU feature within the same compilation unit (see the ``foreach_target.h`` usage and dynamic dispatch docs for how that is done). Universal Intrinsics generate multiple compilation units, one for each CPU feature group, and compiles multiple times, linking them all together (with different names) for runtime dispatch. The Highway technique may not work reliably on MSVC, the Universal Intrinsic technique does work on MSVC. Which one is more robust? The experts disagree. Jan thinks that the Highway approach is more robust and in particular avoids the linker pulling in functions with too-new instructions into the final binary. Sayed thinks that the current NumPy approach (also used by OpenCV) is more robust, and in particular is less likely to run into compiler-specific bugs or catch them earlier. Both agree the meson build system allows specifying object link order, which produces more consistent builds. However that does tie NumPy to meson. Matti and Ralf think the current build strategy is working well for NumPy and the advantages of changing the build and runtime dispatch, with possible unknown instabilities outweighs the advantages that adopting Highway's dynamic dispatch may bring. Our experience of the past four years says that bugs with "invalid instruction" type crashes are invariably due to issues with feature detection - most often because users are running under emulation, and sometimes because there are actual issues with our CPU feature detection code. There is little evidence we're aware of of the linker pulling in a function which is compiled multiple times for different architectures and picking the one with unsupported instructions. To ensure to avoid the issue, it's advisable to keep numerical kernels inside the source code and refrain from defining non-inlined functions within cache-able objects. C++ refactoring considerations ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ We want to move from C to C++, which will naturally involve a significant amount of refactoring, for two main reasons: - get rid of the NumPy-specific templating language for more expressive C++ - this would make using sizeless intrinsics (like for SVE) easier. In addition, we see the following considerations: - If we use Highway, we would need to switch the C++ wrappers from universal intrinsics to Highway. On the other hand, the work to move to C++ is not complete. - If we use Highway, we'd need to rewrite existing kernels using Highway intrinsics. But again, moving to C++ requires touching all those kernels anyway. - One concern regarding Highway was whether it is possible to obtain a function pointer for an architecture-specific function instead of calling that function directly. This so that we can be sure that calling 1-D inner loop many times for a single Python API invocation does not incur the dispatching overhead many times. This was investigated: this can be done with Highway too. - A second concern was whether it's possible with Highway to allow the user at runtime to select or disable dispatching to certain instruction sets. This is possible. - Use of tags in Highway's C++ implementation reduces code duplication but the added templating makes C-level testing and tracing more complicated. The ``_simd`` unit testing module ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Rewriting the ``_simd testing`` module to use C++ was done very recently in PR `gh-24069`_. It depends on the main PR for the move to C++, `gh-21057`_. It allows one to access the C++ intrinsics with almost the same signature, but from Python. This is a great way not only for testing, but also for designing new SIMD kernels. It may be possible to add a similar testing and prototyping feature to Highway (which uses plain ``googletest``), however currently the NumPy way is quite a bit nicer. Math routines ~~~~~~~~~~~~~ Math or numerical routines are written at a higher level of abstraction than the universal intrinsics that are the main focus of this NEP. Highway has only a limited number of math routines, and they are not precise enough for NumPy's needs. So either way, NumPy's existing routines (which use universal intrinsics) will stay, and if we go the Highway route they'll simply have to use Highway primitives internally. We could still use Highway sorting routines. If we do accept lower-precision routines (via a user-supplied choice, i.e. extending ``errstate`` to allow a precision option), we could use Highway-native routines. There may be other libraries that have numerical routines that can be reused in NumPy (e.g., from SLEEF, or perhaps from JPEG XL or some other Highway-using libraries). There may be a small benefit here, but likely it doesn't matter too much. Supported and missing intrinsics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Some specific intrinsics that NumPy needs may be missing from Highway. Similarly, some intrinsics that NumPy needs to implement routines are already implemented in Highway and are missing from NumPy. Highway has more instructions that NumPy's universal intrinsics, so it's possible that some future needs for NumPy kernels may already be met there. Either way, we will always have to implement intrinsics in either solution. Related Work ------------ - `Google Highway`_ - `Xsimd`_ - OpenCV's SIMD framework (`API reference < https://docs.opencv.org/4.x/df/d91/group__core__hal__intrin.html>`__, `docs <https://github.com/opencv/opencv/wiki/CPU-optimizations-build-options>`__) - `std::experimental::simd < https://en.cppreference.com/w/cpp/experimental/simd/simd>`__ - See the Related Work section in :ref:`NEP38` for more related work (as of 2019) Implementation -------------- TODO Alternatives ------------ Use Google Highway for dynamic dispatch. Other alternatives include: do nothing and stay with C universal intrinsics, use `Xsimd`_ as the SIMD framework (less comprehensive than Highway - no SVE or PowerPC support for example), or use/vendor `SLEEF`_ (a good library, but inconsistently maintained). Neither of these alternatives seems appealing. Discussion ---------- References and Footnotes ------------------------ .. [1] Each NEP must either be explicitly labeled as placed in the public domain (see this NEP as an example) or licensed under the `Open Publication License`_. .. _Open Publication License: https://www.opencontent.org/openpub/ .. _`NEP 38`: https://numpy.org/neps/nep-0038-SIMD-optimizations.html .. _`gh-20866`: https://github.com/numpy/numpy/pull/20866 .. _`gh-21057`: https://github.com/numpy/numpy/pull/21057 .. _`gh-23096`: https://github.com/numpy/numpy/pull/23096 .. _`gh-24018`: https://github.com/numpy/numpy/pull/24018 .. _`gh-24069`: https://github.com/numpy/numpy/pull/24069 .. _JPEG XL: https://github.com/libjxl/libjxl .. _CPU/SIMD Optimizations: https://numpy.org/doc/1.25/reference/simd/ .. _the Highway docs: https://google.github.io/highway/ .. _Google Highway: https://github.com/google/highway/ .. _Xsimd: https://github.com/xtensor-stack/xsimd .. _SLEEF: https://sleef.org/ .. _tcmalloc: https://github.com/google/tcmalloc .. _JAX: https://github.com/google/jax Copyright --------- This document has been placed in the public domain. [1]_
_______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
