in High Performance Computing Victor Eijkhout CppCon 2024 Introduction This poster reports on ‘D2D’, a benchmark that explores elegance of expression and perfor- mance in the context of a High Performance profiling and discussion of performance. By presentation time: So far, exclusively top-of-the-line high core count processors were used. I will compare to a more ordinary desktop, where bandwidth limitations

Introduction Firsts steps Context Theoretical foundations Outline of an implementation Conclusion High-Performance Numerical Integration in the Age of C++26 Vincent Reverdy Laboratoire d’Annecy de Physique Verner’s methods Explicit adaptive Runge-Kutta schemes Slightly enhanced methods with interpolants High-accurary interpolation inside integration steps Interpolation with extra coefficients βi yn+u = talk of last year for the implementation of this kind of expression templates: Symbolic Calculus for High-Performance Computing from Scratch using C++23, V. Reverdy, CppCon2023 CppCon - Vincent Reverdy -

Nim - the first high performance Nim - the first high performance language with full support for hot code- language with full support for hot code- reloading at runtime reloading at runtime by Viktor Iterators type CustomRange = object low: int high: int iterator items(range: CustomRange): int = var i = range.low while i <= range.high: yield i inc i iterator pairs(range: CustomRange): range: # uses CustomRange.items yield (i, char(i + ord('a'))) for i, c in CustomRange(low: 1, high: 3): echo c # prints: b, c, d 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 7 Variants Variants

Comparison Binding Constraints Architecture Substitution Construction Conclusion Symbolic Calculus for High-Performance Computing from Scratch using C++23 Vincent Reverdy Laboratoire d’Annecy de Physique . What this talk is not about Complicated maths (you are smart people, you can do it yourself) High-performance computing (you all know about it + see the 2019 talk for that) Benchmarks, assembly, mathematical concepts Symbolic calculus (derivatives, integrals) Full blown custom rule-based rewriting High-performance Since formulas have the entire information on the mathematical AST, it’s possible to

Callback argument • Return a placeholder (Future, Promise, Deferred) • Deliver to a queue • Callback registry (e.g. POSIX signals) Regardless of which type of interface is used, asynchronous functions by definition this in native coroutines. – can support integration with other packages including Twisted via a registry of conversion functions. To access this functionality in native coroutines, use tornado.gen.convert_yielded a standalone module in tornado.autoreload. The two can be used in combination to provide extra robustness against syntax errors: set autoreload=True within the app to detect changes while it is running

Callback argument • Return a placeholder (Future, Promise, Deferred) • Deliver to a queue • Callback registry (e.g. POSIX signals) Regardless of which type of interface is used, asynchronous functions by definition this in native coroutines. – can support integration with other packages including Twisted via a registry of conversion functions. To access this functionality in native coroutines, use tornado.gen.convert_yielded a standalone module in tornado.autoreload. The two can be used in combination to provide extra robustness against syntax errors: set autoreload=True within the app to detect changes while it is running

Callback argument • Return a placeholder (Future, Promise, Deferred) • Deliver to a queue • Callback registry (e.g. POSIX signals) Regardless of which type of interface is used, asynchronous functions by definition this in native coroutines. – can support integration with other packages including Twisted via a registry of conversion functions. To access this functionality in native coroutines, use tornado.gen.convert_yielded a standalone module in tornado.autoreload. The two can be used in combination to provide extra robustness against syntax errors: set autoreload=True within the app to detect changes while it is running

Callback argument • Return a placeholder (Future, Promise, Deferred) • Deliver to a queue • Callback registry (e.g. POSIX signals) Regardless of which type of interface is used, asynchronous functions by definition this in native coroutines. – can support integration with other packages including Twisted via a registry of conversion functions. To access this functionality in native coroutines, use tornado.gen.convert_yielded a standalone module in tornado.autoreload. The two can be used in combination to provide extra robustness against syntax errors: set autoreload=True within the app to detect changes while it is running

Callback argument • Return a placeholder (Future, Promise, Deferred) • Deliver to a queue • Callback registry (e.g. POSIX signals) Regardless of which type of interface is used, asynchronous functions by definition this in native coroutines. – can support integration with other packages including Twisted via a registry of conversion functions. To access this functionality in native coroutines, use tornado.gen.convert_yielded a standalone module in tornado.autoreload. The two can be used in combination to provide extra robustness against syntax errors: set autoreload=True within the app to detect changes while it is running