[Chapter1] Review comments.

chapters/1-Introduction/1-0 Introduction.md

@@ -1,6 +1,6 @@
 # Introduction {#sec:chapter1}
 
-Performance is king: this was true a decade ago, and it certainly is now. According to [@Domo2017], in 2017, the world has been creating 2.5 quintillion[^1] bytes of data every day. [@Statista2024] predicts 400 quintillion bytes per day in 2024. In our increasingly data-centric world, the growth of information exchange requires both faster software and faster hardware.
+Performance is king: this was true a decade ago, and it certainly is now. According to [@Domo2017], in 2017 the world has been creating 2.5 quintillion[^1] bytes of data every day. [@Statista2024] predicts 400 quintillion bytes per day in 2024. In our increasingly data-centric world, the growth of information exchange requires both faster software and faster hardware.
 
 Software programmers have had an "easy ride" for decades, thanks to Moore’s law. Software vendors could rely on new generations of hardware to speed up their software products, even if they did not spend human resources on making improvements in their code. This strategy doesn't work any longer. By looking at Figure @fig:50YearsProcessorTrend, we can see that single-threaded[^2] performance growth is slowing down. From 1990 to 2000, single-threaded performance on SPECint benchmarks increased by a factor of approximately 25 to 30, driven largely by higher CPU frequencies and improved microarchitecture.
 

chapters/1-Introduction/1-2 Why Care about Performance.md

@@ -1,10 +1,10 @@
 ## Why Care about Performance?
 
-In addition to the slowing trend of hardware single-threaded performance growth, there are a couple of other business reasons to care about performance. During the PC era,[^12] the costs of slow software were paid by the users, as inefficient software was running on user computers. With the advent of SaaS (software as a service) and cloud computing, the costs of slow software are put back on the software providers, not their users. If you're a SaaS company like Meta or Netflix,[^4] it doesn't matter if you run your service on-premise hardware or you use the public cloud, you pay for the electricity your servers consume. Inefficient software cuts right into your margins and market evaluation. According to Synergy Research Group,[^5] worldwide spending on cloud services topped $100 billion in 2020, and according to Gartner,[^6] it will surpass $675 billion in 2024.
+In addition to the slowing growth of hardware single-threaded performance, there are a couple of other business reasons to care about performance. During the PC era,[^12] the costs of slow software were paid by the users, as inefficient software was running on user computers. With the advent of SaaS (software as a service) and cloud computing, the costs of slow software are put back on the software providers, not their users. If you're a SaaS company like Meta or Netflix,[^4] it doesn't matter if you run your service on-premise hardware or you use the public cloud, you pay for the electricity your servers consume. Inefficient software cuts right into your margins and market valuation. According to Synergy Research Group,[^5] worldwide spending on cloud services topped $100 billion in 2020, and according to Gartner,[^6] it will surpass $675 billion in 2024.
 
 For many years performance engineering was a nerdy niche, but now it's becoming mainstream. Many companies have already realized the importance of performance engineering and are willing to pay well for this work.
 
-It is fairly easy to reach performance level 4 in Table @tbl:PlentyOfRoom. In fact, you don't need this book to get there. Write your program in one of the native programming languages, distribute work among multiple threads, pick a good optimizing compiler and you'll get there. Unfortunately, this will only put you halfway to the desired efficiency target.
+It is fairly easy to reach performance level 4 in Table @tbl:PlentyOfRoom. In fact, you don't need this book to get there. Write your program in one of the native programming languages, distribute work among multiple threads, pick a good optimizing compiler and you'll get there. Unfortunately, the performance of your program will be about 200 times slower than the optimal target.
 
 The methodologies in this book focus on squeezing out the last bit of performance from your application. Such transformations can be attributed along rows 6 and 7 in Table @tbl:PlentyOfRoom. The types of improvements that will be discussed are usually not big and often do not exceed 10%. However, do not underestimate the importance of a 10% speedup. SQLite is commonplace today not because its developers one day made it 50% faster, but because they meticulously made hundreds of 0.1% improvements over the years. The cumulative effect of these small improvements is what makes the difference.
 

chapters/1-Introduction/1-6 What is in the book.md

@@ -9,7 +9,7 @@ This book is written to help developers better understand the performance of the
 * What performance analysis tools are available on my platform? 
 * What are techniques to reduce the number of cache misses and branch mispredictions?
 
-Hopefully, by the end of this book, you will be able to answer those questions.
+I hope that by the end of this book, you will be able to answer those questions.
 
 The book is split into two parts. The first part (chapters 2--7) teaches you how to find performance problems, and the second part (chapters 8--13) teaches you how to fix them.