Added coding exercise to chapter 10

chapters/10-Optimizing-Branch-Prediction/10-3 Questions-Exercises.md

@@ -4,4 +4,10 @@
 - `perf-ninja::lookup_tables_1`
 - `perf-ninja::virtual_call_mispredict`
 - `perf-ninja::conditional_store_1`
-2. Run the application that you're working with on a daily basis. Collect the TMA breakdown and check the `BadSpeculation` metric. Look at the code that is attributed with the most number of branch mispredictions. Is there a way to avoid branches using techniques we discussed in this chapter?
+2. Run the application that you're working with on a daily basis. Collect the TMA breakdown and check the `BadSpeculation` metric. Look at the code that is attributed with the most number of branch mispredictions. Is there a way to avoid branches using techniques we discussed in this chapter?
+
+**Coding exercise**: write a microbenchmark that will experience 50% misprediction rate or get as close as possible. Your goal is to write a code in which half of all branch instructions are mispredicted. That is not as simple as you may think. Some hints and ideas:
+- Branch misprediction rate is measured as `BR_MISP_RETIRED.ALL_BRANCHES / BR_INST_RETIRED.ALL_BRANCHES`.
+- If you're coding in C++, you can use 1) google benchmark similar to perf-ninja, or 2) write a regular console program and collect CPU counters with Linux `perf`, or 3) integrate libpfm into the microbenchmark (see [@sec:MarkerAPI]).
+- There is no need to invent some complicated algorithm. A simple approach would be to generate a pseudo-random number in the range [0;100) and check if it is less than 50. Random numbers can be pregenerated ahead of time.
+- Keep in mind that modern CPUs can remember long (but limited) sequences of branch outcomes.

chapters/5-Performance-Analysis-Approaches/5-4 Marker APIs.md

@@ -1,4 +1,4 @@
-### Using Marker APIs
+### Using Marker APIs {#sec:MarkerAPI}
 
 In certain scenarios, we might be interested in analyzing performance of a specific code region, not an entire application. This can be a situation when you're developing a new piece of code and want to focus just on that code. Naturally, you would like to track optimization progress and capture additional performance data that will help you along the way. Most performance analysis tools provide specific *marker APIs* that let you do that. Here are a few examples: