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content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/1.md

@@ -8,11 +8,11 @@ layout: learningpathall
 
 ## Introduction to Performance Libraries
 
-The C++ Standard Library provides a collection of classes and functions that are essential for everyday programming tasks, such as data structures, algorithms, and input/output operations. It is designed to be versatile and easy to use, ensuring compatibility and portability across different platforms. However as a result of this portability, standard libraries introduces some limitations. Performance sensitive applications may wish to take maximum advantage of the hardware's capabilities. This is where performance libraries come in. 
+The C++ Standard Library provides a collection of classes and functions that are essential for everyday programming tasks, such as data structures, algorithms, and input/output operations. It is designed to be versatile and easy to use, ensuring compatibility and portability across different platforms. However as a result of this portability, standard libraries introduces some limitations. Performance sensitive applications may wish to take maximum advantage of the hardware's capabilities - this is where performance libraries come in. 
 
-Performance libraries like OpenRNG are specialized for high-performance computing tasks and are often tailored to the microarchitecture of a specific processor. These libraries are optimized for speed and efficiency, often leveraging hardware-specific features such as vector units to achieve maximum performance. Performance libraries are crafted through extensive benchmarking and optimization, and can be domain-specific, such as genomics libraries, or produced by Arm for general-purpose computing. For example, OpenRNG focuses on generating random numbers quickly and efficiently, which is crucial for simulations and scientific computations, whereas the C++ Standard Library offers a more general-purpose approach with functions like std::mt19937 for random number generation.
+Performance libraries are specialized for high-performance computing tasks and are often tailored to the microarchitecture of a specific processor. These libraries are optimized for speed and efficiency, often leveraging hardware-specific features such as vector units to achieve maximum performance. Performance libraries are crafted through extensive benchmarking and optimization, and can be domain-specific, such as genomics libraries, or produced by Arm for general-purpose computing. For example, OpenRNG focuses on generating random numbers quickly and efficiently, which is crucial for simulations and scientific computations, whereas the C++ Standard Library offers a more general-purpose approach with functions like std::mt19937 for random number generation.
 
-Performance libraries for Arm CPUs, such as the Arm Performance Libraries (APL), provide highly optimized mathematical functions for scientific computing, similar to how cuBLAS are a set of optimised libaries specifically for NVIDIA GPUs. These libraries can be linked dynamically at runtime or statically during compilation, offering flexibility in deployment. They are designed to support multiple versions of the Arm architecture, including those with NEON and SVE extensions.  Generally, minimal source code changes are required to support these libraries, making them easy to integrate.
+Performance libraries for Arm CPUs, such as the Arm Performance Libraries (APL), provide highly optimized mathematical functions for scientific computing. An analogous library for accelerating routines on GPU is cuBLAS for NVIDIA GPUs. These libraries can be linked dynamically at runtime or statically during compilation, offering flexibility in deployment. They are designed to support multiple versions of the Arm architecture, including those with NEON and SVE extensions.  Generally, minimal source code changes are required to support these libraries, making them simple for porting and optimising. 
 
 ### Choosing the right version of a library
 
@@ -35,13 +35,12 @@ Multiple performance libraries coexist to cater to the diverse needs of differen
 - **Hardware Specialization**  Some libraries are designed to be cross-platform, supporting multiple hardware architectures to provide flexibility and broader usability. For example, the OpenBLAS library supports both Arm and x86 architectures, allowing developers to use the same library across different systems. 
 
 - **Domain-Specific Libraries**: Libraries are often created to handle specific domains or types of computations more efficiently. For instance, libraries like cuDNN are optimized for deep learning tasks, providing specialized functions that significantly speed up neural network training and inference.
-These factors contribute to the existence of multiple performance libraries, each tailored to meet the specific demands of various hardware and applications.
 
 - **Commercial Libraries**: Alternatively, some highly performant libraries require a license to use. This is more common in domain specific libraries such as computations chemistry or fluid dynamics. 
 
+These factors contribute to the existence of multiple performance libraries, each tailored to meet the specific demands of various hardware and applications.
 
-
-Invariably, there will be performance differences between each library and the best way to observe it to use the library within your own program. For more information please read [this blog](https://community.arm.com/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/arm-performance-libraries-24-10).
+Invariably, there will be performance differences between each library and the best way to observe it to use the library within your own program. For more information on performance benchmarking please read [this blog](https://community.arm.com/arm-community-blogs/b/servers-and-cloud-computing-blog/posts/arm-performance-libraries-24-10).
 
 ### What performance libraries are available on Arm?
 

content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/2.md

@@ -8,7 +8,7 @@ layout: learningpathall
 
 ## Setting Up Your Environment
 
-In this initial example we will use an Arm-based AWS `t4g.2xlarge` instance along with the Arm Performance Libraries. For instructions to connect to an AWS instance, please see our [getting started guide](https://learn.arm.com/learning-paths/servers-and-cloud-computing/intro/). 
+In this initial example we will use an Arm-based AWS `t4g.2xlarge` instance running Ubuntu 22.04 LTS along with the Arm Performance Libraries. For instructions to connect to an AWS instance, please see our [getting started guide](https://learn.arm.com/learning-paths/servers-and-cloud-computing/intro/). 
 
 Once connected via `ssh`, install the required packages with the following commands. 
 

content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/3.md

@@ -8,7 +8,7 @@ layout: learningpathall
 
 ## Example using Optimised Math library
 
-The libamath library from Arm is an optimized subset of the standard library math functions for Arm-based CPUs, providing both scalar and vector functions at different levels of precision. It includes vectorized versions (Neon and SVE) of common math functions found in the standard library, such as those in the `<cmath>` header. 
+The `libamath` library from Arm is an optimized subset of the standard library math functions for Arm-based CPUs, providing both scalar and vector functions at different levels of precision. It includes vectorized versions (Neon and SVE) of common math functions found in the standard library, such as those in the `<cmath>` header. 
 
 The trivial snippet below uses the `<cmath>` standard cmath header to calculate the base exponential of a scalar value. Copy and paste the code sample below into a file named `basic_math.cpp`.
 
@@ -65,13 +65,13 @@ int main() {
 }
 ```
 
-Compiling using the following g++ command. Again we can use the `ldd` command to print the shared objects for dynamic linking. Now we can opbserve the `libamath.so` shared object is linked. 
+Compiling using the following g++ command. Again we can use the `ldd` command to print the shared objects for dynamic linking.  
 
 ```bash
 g++ optimised_math.cpp -o optimised_math -lamath -lm
 ldd optimised_math
 ```
-You should see the following output.
+Now we can observe the `libamath.so` shared object is linked.
 
 ```output
 

content/learning-paths/servers-and-cloud-computing/using-and-porting-performance-libs/_index.md

@@ -9,7 +9,8 @@ learning_objectives:
     - Learn how to incorporate optimised libraries
     - Learn how to port a basic application from x86 to AArch64
 prerequisites:
-    - Understanding of C++, Linux and the GCC/G++ compiler
+    - Access to an Arm / x86-based cloud instance
+    - Intermediate understanding of C++, Linux and compilation
 
 author_primary: Kieran Hejmadi