## Performance ## Lets dive into Performance issues • Everything in JavaScript defaults to being on the same thread. Too much work on main thread • Android nested layouts • Functions and objects defined

## PERFORMANCE MATTERS Emery Berger College of Information and Computer Sciences UMASS AMHERST (joint work with Charlie Curtsinger, Grinnell College) emeryberger.com, @emeryberger ## A short time ### un.bmp ## Performance used to be easy  Performance improvement in the '80s ## I I ## Performance improvement in [Image](/uploads/documents/6/9/a/5/69a5a7f2064c85b44eb3710c323581ae/p19_1.jpg) loading... ## Performance not easy anymore 

## Performance of Apache Ozone on NVMe Wei-Chiu Chuang (jojochuang) Ritesh Shukla (kerneltime) ## Agenda • Overview of how Ozone and how it scales • Why NVME is important for Ozone for scaling • Benefits Benefits of using NVME • Impala performance results from NVME clusters • Write path improvements results from NVME clusters • Summary • Questions ## Ozone Architecture ![Image](/uploads/documents benefits with all NVME clusters ## Why NVME - Enables destaging of data with minimal impact to performance. • Long tail latency is a small percentage of the overall latency • Vendors increasing shipping

## How GitOps Boosts Business Performance: The Facts  ## I NTRODUCTION As cloud-native applications have become more prevalent competitive advantage with an increase in innovation. This positive effect is not limited to the performance of engineering teams. Technology, in particular cloud native technology like Kubernetes and its together six years of data drawn from over 31,000 technology professionals worldwide. It charts the performance of engineering teams across the world against four key measures: lead time for new features, failure

## 20 24 September 15 - 20 ## +24 ## Performance Engineering Being Friendly to Your Hardware ## I GNAS BAGDONAS ## Being Friendly to Your Hardware Performance Engineering A gentle introduction to hardware ble> From JESD 79-4 DDR4 specification Same capacity, different composition => different performance profile ## Memory • Memory system is in the uncore • Cores act as clients • Remote socket cores • Multiple instructions resulting in fewer operations • ISA restrictions may have impact to performance ## Register renaming ![Image](/uploads/documents/5/f/0/b/5f0b3b420b58b2e5986f32263a8e4e3f/p52_1

China 2022 Python Profiling 原理深入探索与实践 主讲人：羿莉（萧羿）- 阿里云SLS Python  HELLO WORLD ## Python Profiling 大纲 ## ●背景概述 ## ●Python ●Python Profiling的场景与技术原理 ➢场景：即时 VS 持续 ➢技术原理 确定剖析 VS 采样剖析 函数粒度VS 行粒度 物理时间VS CPU 时间 技术全景概览 技术细节探索 ## ●Python Profiling的工具链和解决方案 ➢CPU: cProfile、Profile、Pyinstrument、line-profiler，py-spy ➢Memory : memory-profiler、memray ➢新兴Continuous 方案：Pyroscrope ## ●实践 & 展示 ## 背景概述 ## • 什么是Profiling? - 定义：Profiling是一种以收集程序运行时信息为手段研究动态的程序行为的分析方法。其分析对象是程序的空间或时间复杂度、特定指令的使用情形、函数调用的频率以及执行的时间等等。 • 步骤：数据采集、统计分析、可视化、推理导出

## Using BCC and bpfrace with Performance Co-Pilot eBPF Summit ## M ♦ b ♦ Performance Co-Pilot system performance analysis toolkit BCC eBPF Compiler Collection  and other instrumented applications ## 6000 + ## Metrics Metrics have associated metadata: Semantics: instant, counter

Parallelism in High Performance Computing Victor Eijkhout CppCon 2024 ## I ntroduction This poster reports on ‘D2D’, a benchmark that explores elegance of expression and performance in the context of of a High Performance Computing ‘mini-application’. The same code has been implemented using a number of different approaches to parallelism. Implementations are discussed with performance results. ## Relevance arrays through 'mdspan', it is interesting to explore what C++ can offer for lower level performance critical operations. Scientific computing is an interesting test case since many algorithms are

## +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 past, other languages do far better in terms of everything: functionality, ease of use, and even performance ## This talk The goal is NOT to revolutionize everything or show a library that beats everything k_{i}=f\left(t_{n}+c_{i}h,\ y_{n}+h\sum_{j=1}^{s}a_{i j}k_{j}\right),\quad i=1,\cdots,s $$ ## Performance concerns ☑ The Butcher Tableau can be very sparse ☑ Null coefficients should be optimized away