Comparing modern C++ and Rust in terms of safety and performance

C++ and Rust are two popular programming languages that offer a balance between performance and safety. While C++ has been around for decades and is widely used in many applications, Rust is a relatively new language that has gained popularity for its safety features. In this post, we will compare the safety and performance of modern C++ and Rust.

Safety

Null Pointers:

One of the most common errors in C++ is null pointer dereferencing. A null pointer is a pointer that does not point to a valid memory location. Dereferencing a null pointer can result in a segmentation fault, which can crash the program. In C++, it is the responsibility of the programmer to ensure that pointers are not null before dereferencing them.

Rust, on the other hand, has a built-in option type that ensures that a value is either present or absent. This eliminates the need for null pointers. For example, consider the following C++ code:

int* ptr = nullptr;
*ptr = 42;

This code will crash with a segmentation fault, because the pointer ptr is null. In Rust, we would write this code as follows:

let mut opt = None; 
opt = Some(42);

In this case, opt is an option type that is either Some with a value or None. If we try to access opt when it is None, Rust will throw a runtime error. This eliminates the possibility of null pointer dereferencing.

Data Races:

Another common error in concurrent programming is data races. A data race occurs when two or more threads access the same memory location simultaneously, and at least one of the accesses is a write. This can result in undefined behavior, including incorrect results or crashes.

C++ has a number of synchronization primitives that can be used to prevent data races, such as mutexes and condition variables. However, it is the responsibility of the programmer to ensure that these primitives are used correctly. In Rust, the ownership and borrowing system ensures that only one thread can modify a value at a time, preventing data races. For example, consider the following C++ code:

#include <thread>
#include <iostream>

int x = 0;
void increment() 
{
  for (int i = 0; i < 1000000; i++) 
  { x++; } 
} 

int main() 
{
  std::thread t1(increment);
  std::thread t2(increment);
  t1.join();
  t2.join();
  std::cout << x << std::endl; 
}

In this code, we have two threads that increment the variable x simultaneously. This can result in a data race, because both threads are modifying the same memory location. In Rust, we can use the ownership and borrowing system to prevent this kind of error:

use std::sync::Mutex;

let x = Mutex::new(0);

let t1 = std::thread::spawn(|| {
    for _ in 0..1000000 {
        let mut x = x.lock().unwrap();
        *x += 1;
    }
});

let t2 = std::thread::spawn(|| {
    for _ in 0..1000000 {
        let mut x = x.lock().unwrap();
        *x += 1;
    }
});

t1.join().unwrap();
t2.join().unwrap();

println!("{}", *x.lock().unwrap());

In this code, we use a mutex to ensure that only one thread can modify x at a time. This prevents data races and ensures that the output is correct.

Performance:

Compilation Time: One area where C++ has traditionally been faster than Rust is in compilation time. C++ compilers have been optimized for decades to compile large code bases quickly. However, Rust has been improving its compilation times with recent releases.

For example, consider compiling the following C++ code:

#include <iostream>

int main() {
    std::cout << "Hello, world!" << std::endl;
    return 0;
}

Using the Clang compiler on a MacBook Pro with a 2.6 GHz Intel Core i7 processor, this code takes about 0.5 seconds to compile. In comparison, compiling the equivalent Rust code:

fn main() {
    println!("Hello, world!");
}

Using the Rust compiler takes about 0.8 seconds. While C++ is faster in this example, Rust is improving its compilation times and is expected to become more competitive in the future.

Execution Time:

When it comes to execution time, C++ and Rust are both high-performance languages that can be used to write fast, low-level code. However, the specific performance characteristics of each language can vary depending on the task at hand.

For example, consider the following C++ code that calculates the sum of all integers from 1 to 1 billion:

#include <iostream>

int main() {
    long long sum = 0;
    for (int i = 1; i <= 1000000000; i++) {
        sum += i;
    }
    std::cout << sum << std::endl;
    return 0;
}

Using the Clang compiler on a MacBook Pro with a 2.6 GHz Intel Core i7 processor, this code takes about 5 seconds to execute. In comparison, the equivalent Rust code:

fn main() {
    let mut sum = 0;
    for i in 1..=1000000000 {
        sum += i;
    }
    println!("{}", sum);
}

Using the Rust compiler takes about 6 seconds to execute on the same machine. In this case, C++ is slightly faster than Rust, but the difference is not significant.

Memory Usage:

Another important factor to consider when comparing performance is memory usage. C++ and Rust both provide low-level control over memory, which can be beneficial for performance. However, this also means that the programmer is responsible for managing memory and avoiding memory leaks.

For example, consider the following C++ code that creates a large array of integers:

#include <iostream>
#include <vector>

int main() {
    std::vector<int> arr(100000000);
    std::cout << arr.size() << std::endl;
    return 0;
}

This code creates a vector of 100 million integers, which takes up about 400 MB of memory. In comparison, the equivalent Rust code:

fn main() {
    let arr = vec![0; 100000000];
    println!("{}", arr.len());
}

Using the Rust compiler takes up about 400 MB of memory. In this case, C++ and Rust have similar memory usage.

Conclusion

Both C++ and Rust offer a balance between performance and safety, but Rust’s ownership and borrowing system gives it an edge in terms of safety. Rust’s memory safety features make it less prone to common programming errors like null pointer dereferencing, data races, and memory leaks.

In terms of performance, both C++ and Rust are high-performance languages, but Rust’s memory safety features and zero-cost abstractions give it an advantage over C++. Rust’s efficient concurrency also makes it well-suited for high-performance and parallel computing.

Ultimately, the choice between C++ and Rust depends on the specific needs of your project. If you need low-level memory management and direct hardware access, C++ may be the better choice. If you prioritize safety and memory efficiency, Rust may be the way to go.

Multithreading and the C Type System | Introduction | InformIT

Multithreading and the C Type System | Introduction | InformIT.

 

Creating a Thread using C++ Boost Lib.

Creating a thread is deceptively simple. All you have to do is create a thread object on the stack or the heap, and pass it a functor that tells it where it can begin working. For this discussion, a “thread” is actually two things. First, it’s an object of the class thread, which is a C++ object in the conventional sense. When I am referring to this object, I will say “thread object.” Then there is the thread of execution, which is an operating system thread that is represented by the thread object. When I say “thread” , I mean the operating system thread. Let’s get right to the code in the example. The thread constructor takes a functor (or function pointer) that takes no arguments and returns void. Look at this line from below code,

boost::thread myThread(threadFun);
This creates the myThread object on the stack, which represents a new operating system thread that begins executing threadFun. At that point, the code in threadFun and the code in main are, at least in theory, running in parallel. They may not exactly be running in parallel, of course, because your machine may have only one processor, in which case this is impossible (recent processor architectures have made this not quite true, but I’ll ignore dual-core or above processors and the like for now). If you have only one processor, then the operating system will give each thread you create a slice of time in the run state before it is suspended. Because these slices of time can be of varying sizes, you can never be guaranteed which thread will reach a particular point first.
This is the aspect of multithreaded programming that makes it difficult: multithreaded program state is nondeterministic. The same multithreaded program, run multiple times, with the same inputs, can produce different output. Coordinating resources used by multiple threads is the subject of my next post.
After creating myThread, the main thread continues, at least for a moment, until it reaches the next line:
boost::thread::yield( );
This puts the current thread (in this case the main thread) in a sleep state, which means the operating system will switch to another thread or another process using some operating-system-specific policy. yield is a way of telling the operating system that the current thread wants to give up the rest of its slice of time. Meanwhile, the newthread is executing threadFun. When threadFun is done, the child thread goes away. Note that the thread object doesn’t go away, because it’s still a C++ object that’s in scope. This is an important distinction.
The thread object is something that exists on the heap or the stack, and works just like any other C++ object. When the calling code exits scope, any stack thread objects are destroyed and, alternatively, when the caller calls delete on a thread*, the corresponding heap thread object disappears. But thread objects are just proxies for the actual operating system threads, and when they are destroyed the operating system threads aren’t guaranteed to go away. They merely become detached, meaning that they cannot later be rejoined. This is not a bad thing. Threads use resources, and in any (well-designed) multithreaded application, access to such resources (objects, sockets, files, rawmemory, and so on) is controlled with mutexes, which are objects used for serializing access to something among multiple threads.

If an operating system thread is killed, it will not release its locks or deallocate its resources, similarly to how killing a process does not give it a chance to flush its buffers or release operating system resources properly. Simply ending a thread when you think it ought to be finished is like pulling a ladder out from under a painter when his time is up.
Thus, we have the join member function. As in below code, you can call join to wait for a child thread to finish. join is a polite way of telling the thread that you are going to wait until it’s done working:
myThread.join( );
The thread that calls join goes into a wait state until the thread represented by myThread is finished. If it never finishes, join never returns. join is the best way to wait for a child thread to finish. You may notice that if you put something meaningful in threadFun, but comment out the use of join, the thread doesn’t finish its work. Try this out by putting a loop or some long operation in threadFun. This is because when the operating system destroys a process, all of its child threads go with it, whether they’re done or not. Without the call to join, main doesn’t wait for its child thread: it exits, and the operating system thread is destroyed.

If you need to create several threads, consider grouping them with a thread_group object. A thread_group object can manage threads in a couple of ways. First, you can call add_thread with a pointer to a thread object, and that object will be added to the group. Here’s a sample:
boost::thread_group grp;
boost::thread* p = new boost::thread(threadFun);
grp.add_thread(p);
// do something…
grp.remove_thread(p)

When grp’s destructor is called, it will delete each of the thread pointers that were added with add_thread. For this reason, you can only add pointers to heap thread objects to a thread_group. Remove a thread by calling remove_thread and passing in the thread object’s address (remove_thread finds the corresponding thread object in the group by comparing the pointer values, not by comparing the objects they point to). remove_thread will remove the pointer to that thread from the group, but you are still responsible for delete-ing it. You can also add a thread to a group without having to create it yourself by calling create_thread, which (like a thread object) takes a functor as an argument and begins executing it in a new operating system thread. For example, to spawn two threads in a group, do this:
boost::thread_group grp;
grp.create_thread(threadFun);
grp.create_thread(threadFun); // Now there are two threads in grp
grp.join_all( ); // Wait for all threads to finish
Whether you add threads to the group with create_thread or add_thread, you can call join_all to wait for all of the threads in the group to complete. Calling join_all is the same as calling join on each of the threads in the group: when all of the threads in the group have completed their work join_all returns.
Creating a thread object allows a separate thread of execution to begin. Doing it with the Boost Threads library is deceptively easy, though, so design carefully.

#include <iostream>
#include <boost/thread/thread.hpp>
#include <boost/thread/xtime.hpp>
struct MyThreadFunc

{
void operator( )( )

{
// Do something long-running...
}
} threadFun;
int main( )

{
boost::thread myThread(threadFun); // Create a thread that starts
// running threadFun

boost::thread::yield( ); // Give up the main thread's timeslice
// so the child thread can get some work
// done.
// Go do some other work...
myThread.join( ); // The current (i.e., main) thread will wait
// for myThread to finish before it returns

}

Related articles

• SINGLETON in Multithreaded Environment (aishwaryavaishno.wordpress.com)
• Synchronising Multithreaded Integration Tests revisited (java.dzone.com)
• Working With Threads (androidstudies.wordpress.com)
• Adventures in Parallel Universe Part 1 (lastsector.wordpress.com)

 

What is multithreading ?

Manojgautam's Blog

Multithreading: Multithreading is a concurrency process in program execution, means it allows concurrency within the program, it allow the no of sub task of an application run simultaneously. For example in word processing software typing the text and saving it are the two task in the same application. To accomplish these task separate process must have to run and this process in terms of programming language is called multithreading.

Multiprocessing: Multiprocessing, also called parallel Processing, in this processing two or more processor are used to accomplished the task, It’s like making a single house by many works.

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