. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.1 Evaluation Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2.2 Evaluation Results . . . . human preference and produce DeepSeek-V2 Chat (RL). We evaluate DeepSeek-V2 on a wide range of benchmarks in English and Chinese, and compare it with representative open-source models. Evaluation results models at an economical cost through the sparse architecture. DeepSeek-V2 Chat (RL) on open-ended benchmarks. Notably, DeepSeek-V2 Chat (RL) achieves 38.9 length-controlled win rate on AlpacaEval 2.0 (Dubois

i7 (10510U) - gcc 13.2.1 Benchmarks - centred (a)Benchmarks - centred (b) teju x dragonbox wins ties losses 99.5% 0.0% 0.5% Intel i7 (10510U) - gcc 13.2.1Benchmarks - centred (b) teju x dragonbox 5% 0.0% 0.5% teju x ryu wins ties losses 100.0% 0.0% 0.0% Intel i7 (10510U) - gcc 13.2.1Benchmarks - centred (b) teju x dragonbox wins ties losses 99.5% 0.0% 0.5% teju x ryu wins ties (10510U) - gcc 13.2.1 Benchmarks - uncentred (b)Benchmarks - uncentred (a) teju x dragonbox wins ties losses 38.2% 0.0% 61.8% Intel i7 (10510U) - gcc 13.2.1Benchmarks - uncentred (a) teju x

md 48 / 58Benchmarks - Benchmarks - https://qlibs.github.io/mp https://qlibs.github.io/mp 49 / 5850 / 5851 / 5852 / 5853 / 5854 / 5855 / 58Benchmarks Benchmarks 56 / 58Benchmarks Benchmarks Circle-lang to compile all around Circle-lang meta model is the fastest to compile all around 56 / 58Benchmarks Benchmarks Circle-lang meta model is the fastest to compile all around Circle-lang meta model is the recursive template instantiations ( ) boost.mp11 boost.mp11 std::tuple std::tuple 56 / 58Benchmarks Benchmarks Circle-lang meta model is the fastest to compile all around Circle-lang meta model is the

feasibility  #2 Micro Benchmarks  Measure specific aspects in controlled and understandable scope  Bottom-up approach  #3 Benchmarks  Evaluate on community/own benchmarks  Examples: TPC-C, TPC-H ML: ImageNet, Mnist, CIFAR, KDD, Criteo  Common Datasets in DM: Census, Taxi, Airlines, DBLP, benchmarks etc Experiments and Result Presentation Representative of real data distributions? Representative Boehm, Graz University of Technology, WS 2021/22 Benchmarks  Overview  Community- and organization-driven creation of agreed benchmarks  Benchmarks can define a field and foster innovation  #1 Data

candidate designs, refining as we go, and then presents interesting implications of the approach, benchmarks, and lessons learned • I implemented the data structure mostly for the fun of it, as well as to Finance L.P. All rights reserved. Benchmarks! 56© 2024 Bloomberg Finance L.P. All rights reserved. Benchmarks! 57© 2024 Bloomberg Finance L.P. All rights reserved. Benchmarks! 58© 2024 Bloomberg Finance Finance L.P. All rights reserved. Benchmarks! 59© 2024 Bloomberg Finance L.P. All rights reserved. Benchmarks! 60© 2024 Bloomberg Finance L.P. All rights reserved. Benchmarks! 61© 2024 Bloomberg Finance

implications • An example of an elegant wait-free algorithm and wait-free design • Some simple benchmarks Some assumed knowledge • You know what std::atomic does and what it is used for • You’ve heard performance. Never guess. The rest of this talk: • How to guess about performance • We’ll do some benchmarks too I promise10 Progress guarantees • Progress guarantees are a way to theoretically categorize this step Solution: One and only one decrement must “take credit” for zeroing the counter29 Benchmarks • My atomic implementation using the wait-free counter versus the lock-free counter

Controller that implements similar functionality. 3.2.3 Configurable Adherence to CIS Security Benchmarks • SUSE Rancher: 4 • OpenShift: 3 • Tanzu: 2 • Anthos: 2 3.2.3.1 SUSE Rancher SUSE Rancher maintains a hardening guide and self-assessment that references CIS benchmarks with specific user actions to satisfy the requirements. SUSE Rancher supports CIS scans on any Kubernetes guide for OpenShift and for RHCOS. 3.2.3.3 Tanzu Security scanning for adherence to the CIS Benchmarks for Kubernetes is available through Tanzu Mission Control (TMC) or by using the Compliance Scanner

10 / 14BENCHMARKS - BENCHMARKS - 10'000 TESTS, 20'000 ASSERTS, 100 CPP FILES 10'000 TESTS, 20'000 ASSERTS, 100 CPP FILES SUITE+ASSERT+STL SUITE+ASSERT+STL 11 / 14BENCHMARKS - BENCHMARKS -

to store everything else (e.g. code) ▶ The OS is Ubuntu 18.04 LTS ▶ The application and micro-benchmarks were compiled using gcc 7.4 with the -O3 optimization flag and C++14 standard flags A Persistent Persistent Transactional Data Structures Live Demonstration References Experimental Setup Micro-benchmarks ▶ Operation ratio for write-dominated workload ▶ Lists: 40% Insert, 40% Delete, 20% Find ▶ Map: Persistent Transactional Data Structures Live Demonstration References Demonstration Settings Micro-benchmarks ▶ Operation ratio: 33% Insert, 33% Delete, 34% Find ▶ Number of Transactions: 10K ▶ Key Range:

across the whole benchmark suite evaluation of LLVM’s optimizations with STABILIZER first, build benchmarks with STABILIZERBuild programs with STABILIZER > szc main.c> szc main.c Build programs with Build programs with STABILIZER now run the benchmarks0% 10% 20% 30% 40% 85.0 87.5 90.0 92.5 95.0 Time (s) Percent of Observed Runtimes Run benchmarks as usual A′ A ×30 ×30 drop the results