I use an AWS c6i.large node (Intel Icelake running at 3,5 GHz, 2 vCPU). I often rely on my Apple macbook, and I build a lot of software using Amazon (AWS) nodes. The project builds an extensive test suite by default. You may also build directly with LLVM: > cmake -B buildfastclang -D CMAKE_LINKER="lld" -D CMAKE_CXX_COMPILER=clang++ -D CMAKE_C_COMPILER=clang -D CMAKE_RC_COMPILER=llvm-rcįor each build directory, I can build in Debug mode ( -config Debug) or in Release mode ( -config Release) with commands such as > cmake -build buildvc -config Debug I do the same for ClangCL: > cmake -B buildclangcl -T ClangCL Hardware-wise, I use a Microsoft Surface Laptop Studio: it has a Tiger Lake Intel processor (i7-11370 3.3 GHz).Īfter grabbing the simdutf library from GitHub, I prepare the build directory for standard Visual Studio: > cmake -B buildvc I will abstain from parallelizing the build: I use default settings. For building the library, we use the latest version of CMake. Let us compare their performance characteristics with a fast string transcoding library ( simdutf). I use an up-to-date Visual Studio (2022) with the latest ClangCL component (based on LLVM 15). Or you can use LLVM as a front-end (ClangCL). If you are programming in C++ using Microsoft tools, you can use the traditional Visual Studio compiler. Evan on Trimming spaces from strings faster with SVE on an Amazon Graviton 3 processor.Daniel Lemire on Trimming spaces from strings faster with SVE on an Amazon Graviton 3 processor.ARM vs Intel on Amazon’s cloud: A URL Parsing Benchmark. Float-parsing benchmark: Regular Visual Studio, ClangCL and Linux GCC.Trimming spaces from strings faster with SVE on an Amazon Graviton 3 processor.Science and Technology links (March 11 2023).However, you can you can sponsor my open-source work on GitHub.
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