Merge pull request #2359 from jasonrandrews/review

spelling and tag updates

Author: jasonrandrews

.wordlist.txt

@@ -4812,4 +4812,141 @@ learnt
 lof
 BalenaOS
 balenaCloud
-
+MX
+ARMFp
+AndroidDemo
+ApacheBench
+ArmHalideAndroidDemo
+Autoscheduler
+BGR
+BVM
+BenchmarkBubbleSort
+BenchmarkQuickSort
+Botspot
+BoundaryConditions
+BubbleSort
+ByteBuffer
+DGGML
+DNQZJ
+DTLB
+EPYC
+ETag
+EVEX
+Esc
+FuseAll
+FuseBlurAndThreshold
+GGG
+GOPATH
+GOROOT
+GTK
+GetByteArrayElements
+Golang
+Golang’s
+HWC
+Halide
+Halide’s
+ImageParam
+Istio
+KEDA
+Kedify
+Kedify’s
+LLC
+LLE
+MPix
+NIC’s
+Netty
+NoRuntime
+OpenBMC’s
+Parallelization
+QCOW
+QuickSort
+RDom
+RGBRGBRGB
+RRR
+RamFB
+Recomputation
+ReleaseByteArrayElements
+Remmina
+Roubalik
+SAXPY
+ScaledObject
+Scaler
+SetByteArrayRegion
+SoL
+Sor
+Sysoev
+TinyRPS
+UFW
+VLA
+VTOR
+VirtualService
+WindowsOnArm
+XMM
+YMM
+YUV
+ZMM
+Zbynek
+adaptively
+allocs
+apiKey
+armhalideandroiddemo
+autounattend
+autowiring
+benchmarkHttpResponse
+benchmem
+blurThresholdImage
+bvm
+clusterName
+coroutine
+createBitmapFromGrayBytes
+cv
+extractGrayScaleBytes
+fallbacks
+firstlogin
+golang
+gosort
+goweb
+halide
+httpd
+inBytes
+inlines
+inputBuffer
+insturction
+jbyteArray
+keda
+kedify
+keypress
+kts
+llmexport
+loadImageFromAssets
+microarchitectures
+minikube
+oOer
+orgId
+outputArray
+outputBuffer
+parallelization
+parallelize
+parallelized
+parallelizes
+preallocation
+precomputing
+qcow
+recomputation
+reconfig
+reconversion
+refetching
+req
+scaler
+scalers
+sprintf
+stdev
+thresholded
+underperformed
+underperforms
+unvectorized
+uop
+walkthrough
+warmups
+xo
+yi

content/learning-paths/automotive/_index.md

@@ -12,21 +12,22 @@ title: Automotive
 weight: 4
 subjects_filter:
 - Containers and Virtualization: 3
-- Performance and Architecture: 5
+- Performance and Architecture: 6
 operatingsystems_filter:
 - Baremetal: 1
-- Linux: 7
+- Linux: 8
 - macOS: 1
 - RTOS: 1
 tools_software_languages_filter:
 - Arm Development Studio: 1
 - Arm Zena CSS: 1
 - C: 2
 - C++: 1
-- Clang: 2
+- Clang: 3
 - DDS: 1
 - Docker: 2
-- GCC: 2
+- FVP: 1
+- GCC: 3
 - Python: 2
 - Raspberry Pi: 1
 - ROS 2: 3

content/learning-paths/embedded-and-microcontrollers/_index.md

@@ -45,11 +45,12 @@ tools_software_languages_filter:
 - CMSIS-DSP: 1
 - CMSIS-Toolbox: 3
 - CNN: 1
+- Computer Vision: 1
 - Containerd: 1
 - DetectNet: 1
 - Docker: 10
 - DSTREAM: 2
-- Edge AI: 1
+- Edge AI: 2
 - Edge Impulse: 1
 - ExecuTorch: 3
 - FastAPI: 1

content/learning-paths/laptops-and-desktops/_index.md

@@ -9,13 +9,13 @@ maintopic: true
 operatingsystems_filter:
 - Android: 2
 - ChromeOS: 2
-- Linux: 33
+- Linux: 34
 - macOS: 9
-- Windows: 44
+- Windows: 45
 subjects_filter:
 - CI-CD: 5
 - Containers and Virtualization: 7
-- Migration to Arm: 28
+- Migration to Arm: 29
 - ML: 2
 - Performance and Architecture: 27
 subtitle: Create and migrate apps for power efficient performance
@@ -28,6 +28,7 @@ tools_software_languages_filter:
 - Arm Performance Libraries: 2
 - Arm64EC: 1
 - Assembly: 1
+- Bash: 1
 - C: 8
 - C#: 6
 - C++: 11
@@ -48,6 +49,7 @@ tools_software_languages_filter:
 - Intrinsics: 1
 - JavaScript: 2
 - Kubernetes: 1
+- KVM: 1
 - Linux: 1
 - LLM: 1
 - LLVM: 2
@@ -61,7 +63,9 @@ tools_software_languages_filter:
 - OpenCV: 1
 - perf: 4
 - Python: 6
+- QEMU: 1
 - Qt: 2
+- RDP: 1
 - Remote.It: 1
 - RME: 1
 - Runbook: 18

content/learning-paths/mobile-graphics-and-gaming/_index.md

@@ -9,15 +9,15 @@ key_ip:
 - Mali
 maintopic: true
 operatingsystems_filter:
-- Android: 31
+- Android: 32
 - Linux: 30
 - macOS: 14
 - Windows: 14
 subjects_filter:
 - Gaming: 6
 - Graphics: 6
 - ML: 12
-- Performance and Architecture: 34
+- Performance and Architecture: 35
 subtitle: Optimize Android apps and build faster games using cutting-edge Arm tech
 title: Mobile, Graphics, and Gaming
 tools_software_languages_filter:
@@ -26,7 +26,7 @@ tools_software_languages_filter:
 - Android: 4
 - Android NDK: 2
 - Android SDK: 1
-- Android Studio: 10
+- Android Studio: 11
 - Arm Development Studio: 1
 - Arm Mobile Studio: 1
 - Arm Performance Studio: 3
@@ -38,6 +38,7 @@ tools_software_languages_filter:
 - CCA: 1
 - Clang: 12
 - CMake: 1
+- Coding: 1
 - Docker: 1
 - ExecuTorch: 1
 - Frame Advisor: 1

content/learning-paths/servers-and-cloud-computing/_index.md

@@ -8,7 +8,7 @@ key_ip:
 maintopic: true
 operatingsystems_filter:
 - Android: 3
-- Linux: 175
+- Linux: 177
 - macOS: 13
 - Windows: 14
 pinned_modules:
@@ -19,11 +19,11 @@ pinned_modules:
     - migration
 subjects_filter:
 - CI-CD: 7
-- Containers and Virtualization: 31
+- Containers and Virtualization: 32
 - Databases: 17
 - Libraries: 9
 - ML: 31
-- Performance and Architecture: 71
+- Performance and Architecture: 72
 - Storage: 1
 - Web: 12
 subtitle: Optimize cloud native apps on Arm for performance and cost
@@ -72,7 +72,7 @@ tools_software_languages_filter:
 - Capstone: 1
 - CCA: 8
 - Clair: 1
-- Clang: 12
+- Clang: 13
 - ClickBench: 1
 - ClickHouse: 1
 - CMake: 1
@@ -89,7 +89,7 @@ tools_software_languages_filter:
 - Fortran: 1
 - FunASR: 1
 - FVP: 7
-- GCC: 24
+- GCC: 25
 - gdb: 1
 - Geekbench: 1
 - Generative AI: 12
@@ -106,21 +106,27 @@ tools_software_languages_filter:
 - Google Cloud: 2
 - Google Test: 1
 - HammerDB: 1
+- Helm: 1
 - Herd7: 1
 - Hugging Face: 11
 - InnoDB: 1
 - Intrinsics: 1
 - iPerf3: 1
+- ipmitool: 1
 - Java: 4
 - JAX: 1
 - JMH: 1
 - Kafka: 1
+- KEDA: 1
+- Kedify: 1
 - Keras: 1
-- Kubernetes: 10
+- KleidiAI: 1
+- Kubernetes: 11
 - Libamath: 1
 - libbpf: 1
 - Linaro Forge: 1
 - Litmus7: 1
+- llama.cpp: 1
 - Llama.cpp: 2
 - LLM: 10
 - llvm-mca: 1
@@ -135,19 +141,22 @@ tools_software_languages_filter:
 - mpi: 1
 - MySQL: 9
 - NEON: 7
+- Neoverse: 1
 - Networking: 1
 - Nexmark: 1
 - NGINX: 4
 - Node.js: 3
 - Ollama: 1
 - ONNX Runtime: 1
 - OpenBLAS: 1
+- OpenBMC: 1
 - OpenJDK 21: 2
 - OpenShift: 1
 - Orchard Core: 1
 - PAPI: 1
 - perf: 6
 - PostgreSQL: 4
+- Profiling: 1
 - Python: 31
 - PyTorch: 9
 - QEMU: 1
@@ -188,6 +197,7 @@ tools_software_languages_filter:
 - wrk2: 2
 - x265: 1
 - YCSB: 1
+- Yocto/BitBake: 1
 - zlib: 1
 - ZooKeeper: 1
 weight: 1

content/learning-paths/servers-and-cloud-computing/irq-tuning-guide/checking.md

@@ -69,4 +69,4 @@ done
 
 ### Saving these changes
 
-Any changes you make to IRQs will be reset at reboot. You will need to change your systems settings to make your changes permanant.
+Any changes you make to IRQs will be reset at reboot. You will need to change your systems settings to make your changes permanent.

content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/3_llama.cpp_annotation.md

@@ -26,7 +26,7 @@ For installation guidance, refer to the [Streamline installation guide](https://
 
 Clone the gator repository that matches your Streamline version and build the `Annotation support library`.
 
-The installation step is depends on your developement machine.
+The installation step is depends on your development machine.
 
 For Arm native build, you can use following insturction to install the packages.
 For other machine, you need to set up the cross compiler environment by install [aarch64 gcc compiler toolchain](https://developer.arm.com/downloads/-/arm-gnu-toolchain-downloads).
@@ -121,7 +121,7 @@ Finally, add an annotation marker inside the main loop:
                 }
 ```
 
-A string is added to the Annotation Marker to record the position of input tokens and numbr of tokens to be processed.
+A string is added to the Annotation Marker to record the position of input tokens and number of tokens to be processed.
 
 ### Step 3: Build llama-cli
 

content/learning-paths/servers-and-cloud-computing/llama_cpp_streamline/4_analyze_token_prefill_decode.md

@@ -113,7 +113,7 @@ By monitoring other PMU events, Backend Stall Cycles and Backend Stall Cycles du
 We can see that at Prefill stage, Backend Stall Cycles due to Memory stall are only about 10% of total Backend Stall Cycles. However, at Decode stage, Backend Stall Cycles due to Memory stall are around 50% of total Backend Stall Cycles.
 All those PMU event counters indicate that it is compute-bound at Prefill stage and memory-bound at Decode stage.
 
-Now, let us further profile the code execution with Streamline. In the ‘Call Paths’ view of Streamline, we can see the percentage of running time of functions that are orginized in form of call stack.
+Now, let us further profile the code execution with Streamline. In the ‘Call Paths’ view of Streamline, we can see the percentage of running time of functions that are organized in form of call stack.
 ![text#center](images/annotation_prefill_call_stack.png "Figure 12. Call stack")
 
 In the ‘Functions’ view of Streamline, we can see the overall percentage of running time of functions.