## Performance tuning and best practices in a Knative based, large-scale serverless platform with Istio 张龚, Gong Zhang, IBM China Development Lab 庄宇, Yu Zhuang, IBM China Development Lab IstioCon Agenda ☑ Knative and Istio ● How Istio is leveraged in a Knative based platform ● Performance bottleneck analysis and tuning O Istio scalability optimization during Knative Service provisioning Unleash https://github.com/knative/serving) ## Performance bottleneck analysis and tuning ## I stio scalability optimization during Knative Service provisioning • Performance Criteria: the platform has multiple

## 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

## 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

) Table of contents: - Requirements - Quick start using Dockerfile - Build from source - Performance tips - Limitations ## 1.3.1 Requirements - OS: Linux - Instruction set architecture (ISA) requirement: org/simple/wheels/nightly/" VLLM_TARGET_DEVICE=openvino python -m pip install -v . ``` ## 1.3.4 Performance tips vLLM OpenVINO backend uses the following environment variables to control behavior: - V Requirements 2. Quick start using Dockerfile 3. Build from source 4. Intel Extension for PyTorch 5. Performance tips ## 1.4.1 Requirements - OS: Linux - Compiler: gcc/g++>=12.3.0 (optional, recommended)