[Chapter 13] Small addition to the intro

chapters/13-Optimizing-Multithreaded-Applications/13-0 Optimizing Multithreaded Applications.md

@@ -13,6 +13,6 @@ Some production applications scale well with the number of cores. However, many
 * **Massively parallel applications**. This type of application usually scales well with the number of cores. They are designed to process a large number of independent tasks. Massively parallel programs often use the divide-and-conquer technique to split the work into smaller tasks (also called *worker threads*) and process them in parallel. Examples of such applications are scientific computations, video rendering, data analytics, AI, and many others. The main obstacle for this type of application is the saturation of a shared resource, such as memory bandwidth, that can effectively stall all the worker threads in the process.
 * **Applications that require synchronization**. This type of application has workers share resources to complete their tasks. Worker threads depend on each other, which creates periods when some threads are stalled. Examples of such applications are databases, web servers, and other server applications. The main challenge in this type of application is to minimize required synchronization and to avoid contention on shared resources.
 
-In this chapter, we will explore how to analyze the performance of both types of applications. 
+In this chapter, we will explore how to analyze the performance of both types of applications. Since this is a book about low-level performance, we will not discuss algorithm-level optimizations such as lockless data structures, which are well covered in other books.
 
 [^5]: Producer-consumer pattern - [https://en.wikipedia.org/wiki/Producer-consumer_problem](https://en.wikipedia.org/wiki/Producer-consumer_problem)