ch1section0 edits

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

@@ -1,18 +1,18 @@
 # Introduction {#sec:chapter1}
 
-They say, "Performance is king". It 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, and as predicted in [@Statista2024], it will reach 400 quintillion bytes per day in 2024. In our increasingly data-centric world, the growth of information exchange fuels the need for 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. It used to be the case that some software vendors preferred to wait for a new generation of hardware to speed up their software products and did not spend human resources on making improvements in their code. 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 grew by a factor of approximately 25 to 30 times based on SPECint benchmarks. The increase in CPU frequency was the key factor driving performance growth.
+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.
 
 ![50 Years of Microprocessor Trend Data. *© Image by K. Rupp via karlrupp.net*. Original data up to the year 2010 was collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, and C. Batten. New plot and data collected for 2010-2021 by K. Rupp.](../../img/intro/50-years-processor-trend.png){#fig:50YearsProcessorTrend width=100%}
 
-However, from 2000 to 2010, single-threaded CPU performance growth was more modest compared to the previous decade (approximately 4 to 5 times). Clock speed stagnated due to a combination of power consumption, heat dissipation challenges, limitations in voltage scaling (Dennard Scaling[^3]), and other fundamental problems. Despite slower clock speed improvements, architectural advancements continued, including better branch prediction, deeper pipelines, larger caches, and more efficient execution units.
+Single-threaded CPU performance growth was more modest from 2000 to 2010 (a factor between four and five). Clock speeds topped out around 4GHz due to power consumption, heat dissipation challenges, limitations in voltage scaling (Dennard Scaling[^3]), and other fundamental problems. Despite clock speed stagnation, architectural advancements continued: better branch prediction, deeper pipelines, larger caches, and more efficient execution units.
 
-From 2010 to 2020, single-threaded performance grew only by about 2 to 3 times. During this period, CPU manufacturers began to focus more on multi-core processors and parallelism rather than solely increasing single-threaded performance. 
+From 2010 to 2020, single-threaded performance grew only by a factor between two and three. During this period, CPU manufacturers began to focus more on multi-core processors and parallelism rather than solely increasing single-threaded performance.
 
-The original interpretation of Moore's law is still standing, as transistor count in modern processors maintains its trajectory. For instance, the number of transistors in Apple chips grew from 16 billion in M1 to 20 billion in M2, to 25 billion in M3, to 28 billion in M4 in a span of roughly four years. The growth in transistor count enables manufacturers to add more cores to a processor. As of 2024, you can buy a high-end server processor that will have more than 100 logical cores on a single CPU socket. This is very impressive, unfortunately, it doesn't always translate into better performance. Very often, application performance doesn't scale with extra CPU cores.
+Transistor counts continue to increase in modern processors. For instance, the number of transistors in Apple chips grew from 16 billion in M1 to 20 billion in M2, to 25 billion in M3, to 28 billion in M4 in a span of roughly four years. The growth in transistor count enables manufacturers to add more cores to a processor. As of 2024, you can buy a high-end server processor that will have more than 100 logical cores on a single CPU socket. This is very impressive. Unfortunately, it doesn't always translate into better performance. Very often, application performance doesn't scale with extra CPU cores.
 
-When it's no longer the case that each hardware generation provides a significant performance boost, we must start paying more attention to how fast our code runs. When seeking ways to improve performance, developers should not rely on hardware. Instead, they should start optimizing the code of their applications.
+As it's no longer the case that each hardware generation provides a significant performance boost, we must start paying more attention to how fast our code runs. When seeking ways to improve performance, developers should not rely on hardware. Instead, they should start optimizing the code of their applications.
 
 > “Software today is massively inefficient; it’s become prime time again for software programmers to get really good at optimization.” - Marc Andreessen, the US entrepreneur and investor (a16z Podcast)