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 (joint work with Charlie Curtsinger, Grinnell College) emeryberger.com, @emeryberger Emery Berger College of Information and Computer Sciences UMASS AMHERSTA short time ago : un.bmp Ogle is too slow! OGLE’84 is too slow!Transistors (millions) Clock Speed (MHz) Performance used to be easy 0.001 0.01 0.1 1 10 100 1,000 10,000 1970 1975 1980 1985 1990 1995 gle loading… No mojitos for me… Back to the present…Transistors (millions) Clock Speed (MHz) Performance not easy anymore 0.001 0.01 0.1 1 10 100 1,000 10,000 1970 1975 1980 1985 1990 1995

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 Why does Ozone Scale? Separation of Active - Standby Protocol Support Hadoop / S3 API Hadoop API Small objects are welcome Max performance reached at object size around 10-20 MB Hardware trends • Cloudera recommends Ozone’s metadata

Being Friendly to Your Hardware Performance Engineering A gentle introduction to hardware for software engineers 2Where does C++ run? 3On an abstract C++ machine 4On an abstract C++ machine? In most practical cases at boot time only Same capacity, different composition => different performance profile From JESD 79-4 DDR4 specificationMemory • Memory system is in the uncore • Cores act Multiple instructions resulting in fewer operations • ISA restrictions may have impact to performance Imaginary ARM mov r20, 0x123456789abcdef0Register renaming 52 Branching Fetch Decode Queue

WHITEPAPER How GitOps Boosts Business Performance: The Facts How GitOps Boosts Business Performance: The Facts 2 INTRODUCTION As cloud-native applications have become more prevalent, the concept 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

Stream processing optimizations ??? Vasiliki Kalavri | Boston University 2020 2 • Costs of streaming operator execution • state, parallelism, selectivity • Dataflow optimizations • plan translation translation alternatives • Runtime optimizations • load management, scheduling, state management • Optimization semantics, correctness, profitability Topics covered in this lecture ??? Vasiliki Kalavri placement decisions • different algorithms, e.g. hash-based vs. broadcast join • What does performance depend on? • input data, intermediate data • operator properties • How can we estimate the

Using BCC and bpftrace with Performance Co-Pilot Andreas Gerstmayr October 28, 2020 Source: https://pcp.io https://github.com/iovisor/bcc https://bpftrace.org 2 eBPF Compiler Collection bpftrace for eBPF Performance Co-Pilot system performance analysis toolkit Performance Co-Pilot 3 Toolkit for collecting, analyzing, visualizing and responding to the status and performance of servers servers, applications and networks. 92 6000+ Agents Performance Metrics Domain Agents export performance data from the kernel, services (e.g. PostgreSQL) and other instrumented applications Metrics

Poster submission: Modern C++ for Parallelism in High Performance Computing Victor Eijkhout CppCon 2024 Introduction This poster reports on ‘D2D’, a benchmark that explores elegance of expression and context 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 multi-dimensional arrays through ‘mdspan’, it is interesting to explore what C++ can offer for lower level performance critical operations. Scientific computing is an interesting test cases since many algorithms are

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 des 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 yn+1 = yn + h s � i=1 biki ki = f � tn + cih, yn + h s � j=1 aijkj � , i = 1, . . . , s Performance concerns The Butcher Tableau can be very sparse Null coefficients should be optimized away Compilers