1Custom Views for the Rest of Us — Jacob Rice jacob.rice.cpp@gmail.com 2🎉🎆🎊 using namespace std::views; for (int i : iota(2) | filter(even) | transform(square)) std::cout << i << ' '; 1 GravitationalBody = requires(T const t) { { t.GetMass() } -> std::floating_point; { t.GetLocation() } -> std::same_as; { t.GetVelocity() } -> std::same_as; }; template concept concept GravitationalRange = GravitationalBody<std::ranges::range_value_t>; 1 2 3 4 5 6 7 8 9 10 78910def GetForceOnBody(this_body, all_bodies): all_forces = list() for other_body in all_bodies:

Limitations and Problems in std::function and similar constructs Amandeep Chawla | Sr. Computer Scientist II amandeep@adobe.com | adchawla@gmail.com Image by Bruno Tornielli© 2024 Adobe. All Rights whose type we can control. ▪ std::function© 2024 Adobe. All Rights Reserved. Adobe Confidential. Task Based Mechanism© 2024 Adobe. All Rights Reserved. Adobe Confidential. std::function ▪ Is a class template template ▪ It is a general-purpose polymorphic function wrapper ▪ Instances of std::function can store, copy, and invoke any CopyConstructible Callable target ▪ Uses type-erasure under the hood to gain

https://github.com/kokkos/mdspangithub.com/griswaldbrooks/spanny2 goals ● deeper understanding of std::mdspan layouts and accessors ● how to write custom layouts and accessors ● dispel common misconceptions memory accessor ● improving memory access using asynchronicity 4 ● motivations for std::mdspan ● review std::mdspan declarationgithub.com/griswaldbrooks/spanny2 how ● layouts and their requirements memory accessor ● improving memory access using asynchronicity 5 ● motivations for std::mdspan ● review std::mdspan declarationgithub.com/griswaldbrooks/spanny2 how ● layouts and their requirements

Slice me jsc: :chunk_evenl Mateusz Zych, lvo Kabadshow Julich Supercomputing Centre, Research Centre Julich, Germany (0) How to chunk a sequence? > Different range adaptors dividing a range into into subranges available: std: :Views: :chunk(chunk_size) std: :Views: :chunk_by(Predicate) std::Views::SpPlLit(dqelimiter) ve [LS Le LT LS LIALBIC index 3456789140 1 12 chunk size =5 1121s1aslsl7lsls work across taskSs chunk count = 5 @xe9 昌 xxe9 回回 回回日回 chunk size =5 remainder 回回 chunk count = 5 由remainder 5 quotient chunk size quotient GD chunk by size [0.5][67]

Other names and brands may be claimed as the property of others.Human Readable Disclaimer - The views in this talk are mine, not necessarily those of my employer. - This is a speculative, academic-style auto z = views::zip(x, y) | views::transform([](auto element) { auto [a, b] = element; return a * b; }); return reduce(par_unseq, z, 0, std::plus()); Distributed Data Structures) - Distributed Ranges (Concepts) - Implementation (Algorithms and views) - Complex Data Structures (Dense and sparse matrices) - Lessons learnedOutline - Background

elements std::sort, std::stable_sort, ranges::sort, etc std::sort(begin, end); std::ranges::sort(cont); 4REMINDERS Sorting is the ordering of elements std::sort, std::stable_sort std::stable_sort, ranges::sort, etc std::sort(begin, end, comp); std::ranges::sort(cont, comp); 5QUICKSORT 6QUICKSORT Quick sort 6.1QUICKSORT Quick sort Take any element 6.2QUICKSORT __quick_sort_loop(first, last); 4 } 5 9Quick sort (STL version) 10STANDARD LIBRARY EVOLVED FROM STL 11STD::SORT 12STD::SORT 12.1Quicksort's worst case is Just pick the worst element 13GCC libstdc++ template

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Allocation // cpu std::vector x(N); std::vector y(N); // … // gpu // ??? // ??? // … 15 |• Added in C++17: • std::pmr::memory_resource • std::pmr::polymorphic_allocator • std::pmr::vector std::pmr::vector • std::pmr::monotonic_buffer_resource • … std::pmr 16 |// gpu unified_memory_resource mem; std::pmr::vector x(N, &mem); std::pmr::vector y(N, &mem); // … Memory Allocation 17 |struct rce : std::pmr::memory_resource { void* do_allocate(std::size_t, std::size_t); void do_deallocate( void* p, std::size_t, std::size_t); bool do_is_equal( const std::pmr::memory_resource&

Center (DLR) Institute of Software Methods for Product Virtualization VECTORIZING A CFD CODE WITH STD::SIMD SUPPLEMENTED BY (ALMOST) TRANSPARENT LOADING AND STORINGMotivation: The Origin of the Talk deduction to load and store std::simd and scalar variables ▪ syntactically equalize scalar and vectorized code The talk: ▪ share experience with vectorization using std::simd ▪ introduce the SIMD_ACCESS once → Single Instruction Multiple Data (SIMD) For more details Matthias Kretz‘ Cppcon talk about std::simd: https://youtu.be/LAJ_hywLtMA 4 Olaf Krzikalla, DLR SP, 2024-09-17 𝑥1 𝑥2 𝑥3 𝑥4 + −∗

Is std::mdspan a Zero-overhead Abstraction? Oleksandr Bacherikov Snap IncWhat is std::mdspan? It’s a view over a multi-dimensional array. It’s designed primarily to be used as a function parameter to C++ If the matrices are contiguous in memory, then we can treat them as ranges and simply use std::transform {-5, 5, -3, 1, 4, 0} + { 3, 2, 1, 3, -5, 4} = {-2, 7, -2, 4, -1, 4} 4*r8], xmm0 15Vectorization is great for performance, not great for slideware.clang16.0.4 -O1 -std=c++20 push rbx test rdi, rdi je .LBB0_6 mov rax, qword ptr [rsp + 24]

Can’t we just synthesize std::tuple_element from get? Jonathan Müller — @foonathanAdding structured bindings support to a custom tuple-like type template struct my_tuple { … }; Jonathan just synthesize std::tuple_element from get? CppCon 2024-09-18 1Adding structured bindings support to a custom tuple-like type template struct my_tuple { … }; template <std::size_t I, typename Tuple> requires instance_ofstd::remove_cvref_t> auto get(Tuple&& t) -> decltype(auto) { … } Jonathan Müller — @foonathan Can’t we just synthesize std::tuple_element from get? CppCon 2024-09-18