Yusuke MIYAKE / Pepabo R&D Institute, GMO Pepabo, Inc. 2019.07.25 GopherCon 2019 Optimization for number of goroutines using feedback control Principal engineer Yusuke MIYAKE @monochromegane Pepabo

downloads. The process of building an open source OS relies on supply chain aggregation and optimization. A reliable open source software supply chain is fundamental to a large-scale commercial OS an HPC deployment tuning assistant that significantly reduces deployment costs and improves optimization efficiency. Project Introduction HPCRunner is composed of two parts: HPC dependency management Scheduler offers several features, including topology discovery and export, scheduling support and optimization, and user-mode topology support. • The Linux ACPI and topology driver can enumerate and create

+ ? ≥ 1 Negative class: ?!? + ? ≤ −1 ? = 1 ? Figure 2: Hard-margin SVM. aim of the above optimization problem is to find a hyperplane (parameterized by ω and b) with margin γ = 1/∥ω∥ maximized, while training set. 2.2 Preliminary Knowledge of Convex Optimization 2.2.1 Optimization Problems and Lagrangian Duality We now consider the following optimization problem min ω f(ω) (9) s.t. gi(ω) ≤ 0, i = 1 gk(ω) and the equality constraints h1(ω), · · · , hl(ω). We construct the Lagrangian of the above optimization problem as L(ω, α, β ) = f(ω) + k � i=1 αigi(ω) + l � j=1 β jhj(ω) (12) In fact, L(ω, α

reserved. © 2024 Bloomberg Finance L.P. All rights reserved. Can you RVO? Using Return Value Optimization for Performance in Bloomberg’s C++ Codebases CppCon 2024 September 16, 2024 Michelle D’Souza RVO? How many people here have heard about “Return Value Optimization”? 2 How many people here are experts on “Return Value Optimization”?© 2018 Bloomberg Finance L.P. All rights reserved. © 2024 Finance L.P. All rights reserved. © 2024 Bloomberg Finance L.P. All rights reserved. Return Value Optimization (RVO) 5© 2018 Bloomberg Finance L.P. All rights reserved. Agenda Questions at the end Source:

Outline 1 SVM: A Primal Form 2 Convex Optimization Review 3 The Lagrange Dual Problem of SVM 4 SVM with Kernels 5 Soft-Margin SVM 6 Sequential Minimal Optimization (SMO) Algorithm Feng Li (SDU) SVM December 28, 2021 15 / 82 Convex Optimization Review Optimization Problem Lagrangian Duality KKT Conditions Convex Optimization S. Boyd and L. Vandenberghe, 2004. Convex Optimization. Cambridge university press press. Feng Li (SDU) SVM December 28, 2021 16 / 82 Optimization Problems Considering the following optimization problem min ω f (ω) s.t. gi(ω) ≤ 0, i = 1, · · · , k hj(ω) = 0, j = 1, · · · , l with

 However, an optimizer might cause this code to fail. 17 Overly-Aggressive Optimization  To see why optimization might be a problem, consider this variation on the code, which sends a '\r' and Volatile Means (and Doesn't Mean) Copyright © 2024 by Ben Saks and Dan Saks 0-8 Overly-Aggressive Optimization  Although they’re mapped to memory locations, device registers aren’t ordinary memory.  effects.  Eliminating those side effects might cause device drivers to fail. 19 Overly-Aggressive Optimization  Unfortunately, to the compiler, USTAT0looks like an ordinary object.  Its state should change

33 1.2.57 $OBJECTCHECKS : Check Object . . . . . . . . . . . . . . . . . . . . . . 34 1.2.58 $OPTIMIZATION : Enable Optimizations . . . . . . . . . . . . . . . . . . 34 1.2.59 $PACKENUM or $Z : Minimum 2.2 Motorola 680x0 specific . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 11.3 Optimization switches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 11.4 Tips to get code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 11.6 Whole Program Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 11.6.1 Overview . . . . . .

handling errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 C.12 Whole program optimization messages . . . . . . . . . . . . . . . . . . . . . . . . . 198 C.13 Package loading messages in this directory as well: fpc -Fu"units/i386-linux" -Fu"units/i386-linux/*" Note that (for optimization) the compiler will drop any non-existing paths from the search path, i.e. the existence of the the same effect as -Fi. -FWxxx store generated Whole Program Optimization information in file xxx. -Fwxxx Read Whole Program Optimization information (as saved using -FWxxx) from file xxx. 5.1.4 Options

in a Knative based platform ● Performance bottleneck analysis and tuning ○ Istio scalability optimization during Knative Service provisioning ○ Unleash maximum scalability by fully leveraging Istio features MEM Knative Version Knative 0.16, 0.17, 0.18 Istio Version 1.5, 1.6, 1.7 Istio scalability optimization during Knative Service provisioning • Benchmark: Kperf (https://github.com/knative-sandbox/kperf) resolved this issue. o Istiod MEM bumped with large numbers of Knative Services (#25532) Mem usage optimization of pilot resolved this issue. • Tune CPU/MEM to ensure enough capacity Leveraged Metrics to

BENCHMARK ● 03a, b – branch is not predicted, optimization works ● 03c, d – branch is well-predicted, no optimizationBranchless Computing 51 ADVANCED OPTIMIZATION – ALWAYS MEASURE ● Sometimes the compiler – Often using “conditional move” instructions (they are not branches) ● Compiler’s branchless optimization is usually better than yours ● In particular, this is almost always branchless in reality: return Branchless Computing 52 BENCHMARK ● 04c, d – optimization does not work with GCC ● with perf – no branchBranchless Computing 53 ADVANCED OPTIMIZATION – ALWAYS MEASURE ● Sometimes the compiler will