Title: "The Code Optimization"
[Opening scene: Jerry's apartment]
Jerry (to George): So, get this. I signed up for this computer class, and guess who's teaching it?
George: Who?
Jerry: Donald Knuth!
George: The Donald Knuth? The Art of Computer Programming Donald Knuth?
Jerry: The one and only!
[Monk's Café]
Jerry, George, Elaine, and Kramer are sitting at their usual booth.
Elaine: So, what's this class about, anyway?
Jerry: Inline assembly and compiler optimization techniques. Real cutting-edge stuff.
Kramer: I've always wanted to learn how to code. Count me in!
[The classroom]
Jerry, George, Elaine, and Kramer are sitting in the classroom. Donald Knuth enters the room and begins the lecture.
Donald Knuth: Welcome, everyone. Today, we'll dive into the world of inline assembly and compiler optimization. We'll start with a simple C++ code sample to illustrate the basics.
Knuth writes the following code on the board:
cpp#include <iostream>
int main() {
int x = 5;
int y = 10;
int sum;
__asm__(
"movl %1, %%eax;"
"addl %2, %%eax;"
"movl %%eax, %0;"
: "=r"(sum)
: "r"(x), "r"(y)
: "%eax"
);
std::cout << "The sum is: " << sum << std::endl;
return 0;
}
Knuth: Now, this code uses inline assembly to add two integers. Can anyone tell me how we can optimize this further?
Kramer raises his hand, and Knuth calls on him.
Kramer: Why not just use the '+' operator?
Knuth smiles: Excellent point, Mr. Kramer. Let's rewrite the code using the '+' operator.
Knuth writes the optimized code on the board:
cpp#include <iostream>
int main() {
int x = 5;
int y = 10;
int sum = x + y;
std::cout << "The sum is: " << sum << std::endl;
return 0;
}
Knuth: Sometimes, the simplest solution is the most optimal one.
[Jerry's apartment]
Jerry, George, Elaine, and Kramer discuss their experience in the class.
Jerry: I thought Knuth was a genius, but Kramer outsmarted him!
Elaine: To be fair, Jerry, you did sign up for a class on compiler optimization.
George: I think I'll stick to my regular job.
Kramer: Well, I'm just getting started. Next, I'll optimize the entire Internet!
[Closing scene]
Jerry does a stand-up comedy bit:
Jerry: So, we learned that sometimes the most complicated solutions are not the best. Just like dating. You can spend hours analyzing someone's text messages or obsessing over every little detail, but sometimes, the best approach is just to ask, "Hey, do you want to grab coffee?"
Title: "The Parallel Predicament"
[Opening scene: Jerry's apartment]
Jerry (to George): You won't believe it! I enrolled in another computer class, and this time, it's Scott Meyers teaching it!
George: The Scott Meyers? The Effective C++ Scott Meyers?
Jerry: That's right!
[Monk's Café]
Jerry, George, Elaine, and Kramer are sitting at their usual booth.
Elaine: So, what's this class about, anyway?
Jerry: Intel TBB and C++17 Parallel Algorithms. It's all about making your code run faster by using multiple cores.
Kramer: Sign me up! I love the need for speed!
[The classroom]
Jerry, George, Elaine, and Kramer are sitting in the classroom. Scott Meyers enters the room and begins the lecture.
Scott Meyers: Welcome, everyone. Today, we'll learn how to harness the power of parallelism with Intel TBB and C++17. Let's start with a simple C++ code sample to demonstrate a parallel for loop using TBB.
Meyers writes the following code on the board:
cpp#include <iostream>
#include <vector>
#include <tbb/tbb.h>
int main() {
std::vector<int> data(1000000);
tbb::parallel_for(tbb::blocked_range<size_t>(0, data.size()),
[&](const tbb::blocked_range<size_t>& r) {
for (size_t i = r.begin(); i != r.end(); ++i) {
data[i] = i * i;
}
});
std::cout << "Data processed in parallel." << std::endl;
return 0;
}
Scott Meyers: Now, this code processes a large vector using TBB's parallel_for. But let's say we want to use C++17's Parallel Algorithms instead. Can anyone tell me how to do that?
Elaine raises her hand, and Meyers calls on her.
Elaine: We can replace the parallel_for loop with a call to std::transform using an execution policy.
Meyers: Excellent, Ms. Benes. Let's rewrite the code using C++17 Parallel Algorithms.
Meyers writes the optimized code on the board:
cpp#include <iostream>
#include <vector>
#include <algorithm>
#include <execution>
int main() {
std::vector<int> data(1000000);
std::transform(std::execution::par_unseq, data.begin(), data.end(), data.begin(),
[](int i) { return i * i; });
std::cout << "Data processed in parallel using C++17." << std::endl;
return 0;
}
Meyers: There you have it. C++17 allows us to use parallelism just as efficiently, but with a cleaner syntax.
[Jerry's apartment]
Jerry, George, Elaine, and Kramer discuss their experience in the class.
Jerry: It's amazing how fast computers have become.
Elaine: I never thought I'd understand parallelism, but it actually makes sense!
George: I'm still confused. Why not just get a faster computer?
Kramer: Well, George, it's like driving a car. You can have the fastest car in the world, but if you're stuck in traffic, you're not going anywhere.
[Closing scene]
Jerry does a stand-up comedy bit:
Jerry: So, we learned that sometimes it's not just about raw speed, but how efficiently you use it. It's like waiting in line at the supermarket. You can have the fastest cashier, but if everyone is using just one line, we're all stuck waiting. That's when you realize