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.wordlist.txt

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veraison
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verifier
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vllm
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observables
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observables
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APL
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ARchive
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AllowUSBDebugging
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CalcThreadProc
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DWORD
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Daytona
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Fraunhofer
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HIWORD
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IEC
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ITU
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Kibana
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Koleini
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LOWORD
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LPVOID
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Masoud
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OpenMP
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VVC
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VVenC
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ViT
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WINAPI
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Willen
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applyRotation
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boto
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cblas
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daytona
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dgemm
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dotfiles
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dumpbin
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fraunhoferhhi
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gh
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gif
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grafana
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installable
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kibana
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pointStride
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prometheus
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refx
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refy
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refz
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rotMatrix
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sbt
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scala
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spherePoints
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startPoint
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terrafom
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threadCount
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threadNum
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useAPL
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vvenc
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workspaces

content/install-guides/rust.md

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Use the `apt` command to install the required software packages on any Debian-based Linux distribution, including Ubuntu.
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```bash { target="ubuntu:latest" }
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sudo apt update -y
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sudo apt update
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sudo apt install -y curl gcc
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```
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content/install-guides/sbt.md

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{{% notice Note %}}
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When the project was created, it was called *Simple Build Tool*, but quickly evolved to *sbt*. Some have incorrectly redefined it to *Scala Build Tool*, which does not reflect the fact that sbt works with Java-only projects.
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It is now called *sbt* in all lowercase letters, which emphasises the fact that it is not an acronym.{{% /notice %}}
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It is now called *sbt* in all lowercase letters, which emphasizes the fact that it is not an acronym.{{% /notice %}}
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## What should I consider before installing sbt on Arm?
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content/learning-paths/cross-platform/daytona/intro.md

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## Daytona terminology
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Taking time to learn some the basic Daytona defintions will enable you to get started easily. You can find some of these terms described below.
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Taking time to learn some the basic Daytona definitions will enable you to get started easily. You can find some of these terms described below.
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#### Git Providers
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content/learning-paths/cross-platform/windowsperf_sampling_cpython_spe/overview.md

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---
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layout: learningpathall
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title: Overview of Arm Statistical Profiling Extenstion
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title: Overview of Arm Statistical Profiling Extension
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weight: 2
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---
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content/learning-paths/servers-and-cloud-computing/mysql_tune/kernel_comp_lib.md

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## Compiler Considerations
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The easiest way to gain performance is to use the latest version of GCC. Aside from that, the flags `-mcpu` and `-flto` can be used to potentially gain additional performance. Usage of these flags is explained in the [Migrating C/C++ applications](/learning-paths/servers-and-cloud-computing/migration/c-c++) section of the [Migrating applications to Arm servers](/learning-paths/servers-and-cloud-computing/migration/) learning path.
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The easiest way to gain performance is to use the latest version of GCC. Aside from that, the flags `-mcpu` and `-flto` can be used to potentially gain additional performance. Usage of these flags is explained in the [Migrating C/C++ applications](/learning-paths/servers-and-cloud-computing/migration/c/) section of the [Migrating applications to Arm servers](/learning-paths/servers-and-cloud-computing/migration/) learning path.
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## OpenSSL Considerations
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content/learning-paths/servers-and-cloud-computing/nginx_tune/kernel_comp_lib.md

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## Compiler Considerations
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The easiest way to gain performance is to use the latest version of GCC. Aside from that, the flag `-mcpu` can be used to potentially gain additional performance. Usage of this flag is explained in the [Migrating C/C++ applications](/learning-paths/servers-and-cloud-computing/migration/c-c++) section of the [Migrating applications to Arm servers](/learning-paths/servers-and-cloud-computing/migration/) learning path.
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The easiest way to gain performance is to use the latest version of GCC. Aside from that, the flag `-mcpu` can be used to potentially gain additional performance. Usage of this flag is explained in the [Migrating C/C++ applications](/learning-paths/servers-and-cloud-computing/migration/c/) section of the [Migrating applications to Arm servers](/learning-paths/servers-and-cloud-computing/migration/) learning path.
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If you need to understand how to configure a build of Nginx. Please review the [build Nginx from source](/learning-paths/servers-and-cloud-computing/nginx/build_from_source) section of the [Learn to deploy Nginx learning path](/learning-paths/servers-and-cloud-computing/nginx/).
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content/learning-paths/servers-and-cloud-computing/postgresql_tune/kernel_comp_lib.md

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## Compiler Considerations
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The easiest way to gain performance is to use the latest version of GCC. Aside from that, the flags `-mcpu` and `-flto` can be used to potentially gain additional performance. Usage of these flags is explained in the [Migrating C/C++ applications](/learning-paths/servers-and-cloud-computing/migration/c-c++) section of the [Migrating applications to Arm servers](/learning-paths/servers-and-cloud-computing/migration/) learning path.
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The easiest way to gain performance is to use the latest version of GCC. Aside from that, the flags `-mcpu` and `-flto` can be used to potentially gain additional performance. Usage of these flags is explained in the [Migrating C/C++ applications](/learning-paths/servers-and-cloud-computing/migration/c/) section of the [Migrating applications to Arm servers](/learning-paths/servers-and-cloud-computing/migration/) learning path.
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## OpenSSL Considerations
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content/learning-paths/servers-and-cloud-computing/redis_tune/kernel_comp_lib.md

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## Compiler Considerations
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The easiest way to gain performance is to use the latest version of GCC. Aside from that, the flag `-mcpu` and `-flto` can be used to potentially gain additional performance. Usage of these flags is explained in the [Migrating C/C++ applications](/learning-paths/servers-and-cloud-computing/migration/c-c++) section of the [Migrating applications to Arm servers](/learning-paths/servers-and-cloud-computing/migration/) learning path.
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The easiest way to gain performance is to use the latest version of GCC. Aside from that, the flag `-mcpu` and `-flto` can be used to potentially gain additional performance. Usage of these flags is explained in the [Migrating C/C++ applications](/learning-paths/servers-and-cloud-computing/migration/c/) section of the [Migrating applications to Arm servers](/learning-paths/servers-and-cloud-computing/migration/) learning path.
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If you need to understand how to configure a build of Redis. Please review the [build Redis from source](https://redis.io/docs/getting-started/installation/install-redis-from-source/).
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content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/_index.md

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title: Perform Sentiment Analysis on X on Arm-based EKS clusters
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minutes_to_complete: 60
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who_is_this_for: This Learning Path is for software developers who want to build an end-to-end ML sentiment analysis solution on an Arm-based Amazon EKS cluster to analyze live posts on X .

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