## +24 ## High-Performance Numerical Integration in the Age of C++26 VINCENT REVERDY ## High-Performance Numerical Integration in the Age of C++26 Vincent Reverdy Laboratoire d'Annecy de Physique 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<

## +24 ## High-Performance Cross-Platform Architecture: C++20 Innovations ## NOAH STEIN ## About Me • 35-year career in video games and embedded software • Started using C++ in 1995 • First cross-platform

## +23 ## Symbolic Calculus for High-performance Computing From Scratch Using C++23 VINCENT REVERDY ## Symbolic Calculus for High-Performance Computing from Scratch using C++23 Vincent Reverdy Laboratoire 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, and optimization ■ 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

## +24 ## Using Modern C++ to Build XOffsetDatastructure: A Zero-Encoding and Zero-Decoding High-Performance Serialization Library in the Game Industry FANCHEN SU 20 24 September 15 - 20 ## About Me Title: • Using Modern C++ to Build XOffsetDatastructure: A Zero-Encoding and Zero-Decoding High-Performance Serialization Library in the Game Industry • What • XOffsetDatastructure; Serialization Library; Title: • Using Modern C++ to Build XOffsetDatastructure: A Zero-Encoding and Zero-Decoding High-Performance Serialization Library in the Game Industry • What • XOffsetDatastructure; Serialization Library;

automatically switches I/Os to other available paths in case of a link failure, ensuring uninterrupted high-performance and high-reliability services. ## ▶ Challenges With the emergence of diverse application scenarios links cannot be automatically switched over. ## ▶ Project Introduction eNFS: Building an E2E High-Performance and Reliable Distributed File System for Production Services  eNFS provides high-performance data sharing capabilities beyond

FX!32 achieves its goal by transparently running those applications at speeds comparable to high-performance x86 platforms. ## FX!32 A PROFILE-DIRECTED BINARY TRANSLATOR —1998 (read) Because Digital’s FX!32 achieves its goal by transparently running those applications at speeds comparable to high-performance x86 platforms. Digital FX!32 is a software utility that enables x86 Win32 applications to be FX!32 achieves its goal by transparently running those applications at speeds comparable to high-performance x86 platforms. Digital FX!32 is a software utility that enables x86 Win32 applications to be

hybrid deployment of online and offline services. • Support Service Mesh. • Large-scale and high-performance autoscaling capabilities. • Multi-tenant and quota management. etc. ## TKEx Architecture [Image](/uploads/documents/e/f/c/5/efc5dc960bf5aac902a1035ed3658334/p14_1.jpg) Large-scale and high-performance Autoscaling ## V ertical Workload AutoScaler (VWA) ## Motivation: ➢ Reduce container OOM from without breaking offline tasks. ## Key Features: In-Place Update without restarting container ➢ High-performance ➢ Safe autoscaling decisions Personalized configuration of VWA objects ➢ Cooperate with HPA

in Expert Parallelism 15 3.2 Flexible and Efficient Kernel Development with TileLang 16 3.3 High-Performance Batch-Invariant and Deterministic Kernel Libraries 18 3.4 FP4 Quantization-Aware Training 19 higher speed. 3.3. High-Performance Batch-Invariant and Deterministic Kernel Libraries To enable efficient training and inference, we develop a comprehensive set of high-performance computational kernels Diverse cache policies, such as those used in Sliding Window Attention. Constraints imposed by high-performance attention kernels, including alignment requirements. Figure 6 | Illustration of the KV cache

adds tables to compute engines including Spark, Trino, PrestoDB, Flink, Hive and Impala using a high-performance table format that works just like a SQL table. Tip: This article assumes that you have mastered adds tables to compute engines including Spark, Trino, PrestoDB, Flink, Hive and Impala using a high-performance table format that works just like a SQL table. Tip: This article assumes that you have mastered adds tables to compute engines including Spark, Trino, PrestoDB, Flink, Hive and Impala using a high-performance table format that works just like a SQL table. Tip: This article assumes that you have mastered

adds tables to compute engines including Spark, Trino, PrestoDB, Flink, Hive and Impala using a high-performance table format that works just like a SQL table. Tip: This article assumes that you have mastered adds tables to compute engines including Spark, Trino, PrestoDB, Flink, Hive and Impala using a high-performance table format that works just like a SQL table. Tip: This article assumes that you have mastered adds tables to compute engines including Spark, Trino, PrestoDB, Flink, Hive and Impala using a high-performance table format that works just like a SQL table. Tip: This article assumes that you have mastered