Top 25 LLVM Project Interview Questions To Prepare For Your Next Tech Interview

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LLVM is an insanely cool set of low-level technologies (assemblers, compilers, debuggers) that are language-agnostic. They’re used for C, C++, Ruby, C#, Scala, Swift, Haskell, and a ton of other languages.

This article talks about an introduction to compiler design, the ideas behind LLVM, how LLVM was made, and some very useful features it has. We’ll be giving a summary below.

In 2000, interpreters and compilers for open source programming languages were made to be one-time use tools and were packaged as single executables. It would’ve been difficult to reuse the parser from a static compiler to do static analysis or refactoring.

Also, implementations of programming languages usually came with either a traditional static compiler or a runtime compiler in the form of an interpreter or just-in-time (JIT) compiler.

It wasn’t often that a language implementation worked with both, and when it did, there wasn’t much code sharing.

In the old days, you could only run a single application on a single server. Running multiple applications could be dangerous since the two apps might interfere with each other. One app could change the file system in a way that the other app didn’t expect, which would make the second app crash. Or, one app might need a certain version of MySQL while another app needs a different version. In the worst case, one app could have been written by a bad person who wants the other app to fail on purpose.

It gets really pricey if you want to run a cloud computing business with just one app and one server. People who are just starting out won’t be able to use the server’s full power, so you waste a lot of computing power.

Hypervisors solve this issue! A hypervisor is computer software that can create and run a virtual machine. A virtual machine is a copy of a computer system that runs its own operating system on top of the host machine. The hypervisor is run on the host machine to manage the virtual machine. As long as you stay inside the virtual machine, nothing you do will impact the host computer or any other virtual machines that are running on that computer. You basically get your own OS.

A company that does cloud computing can now just put a hypervisor on their powerful servers. Whenever someone wants to run an app on that server, the hypervisor will create a new virtual machine.

You could also use a hypervisor on your home computer to try out a new operating system quickly. It is possible to put VirtualBox on a Windows computer and use Ubuntu in a VirtualBox Virtual Machine while Windows is running.

In terms of our cloud computing business, do we really need to set up a whole virtual machine every time someone wants to use our server? It costs a lot of money to do this, and virtual machines have a lot of security features that aren’t really needed. Each virtual machine is gigabytes in size and takes minutes to start up. Most of the time, users only install and run one app on each virtual machine, so making and packaging a whole new OS is a huge waste.

This is where containers come in. A Container is just a package for code for the application and all the dependencies that are needed. Each container shares the host OS and does not require its own OS. This allows containers to be spun up much faster and each container takes up much less space!.

Now, when you have containers running on a computer, you’ll need some software to manage them. You’ll need a program to spin up new containers, manage their resource sharing, and shut them down. This program is called a container engine. If you’re into analogies… Container Engine is to Container as Hypervisor is to Virtual Machine.

One extremely popular container engine is the Docker Engine. The Docker Engine is open source and is maintained by Docker, Inc!.

The Docker Engine uses a Docker as a blueprint for how to build the container. The Docker is just a static file and is very lightweight. You can download various docker s on Docker Hub. You can download Docker s to run Redis, Node, MongoDB and a bunch of other software.

In order to build Docker s, you need a Dockerfile. What is a dockerfile? It’s just a text file with instructions for how to build the container.

Over the last couple of years, we’ve had a trend from Monolith architectures to Microservices. You won’t run your whole app in a single app; instead, you’ll split it up into separate apps for each part (database, authentication, payment, logging, etc.). ) into different individual services, and expose each individual service with a REST API.

As a general rule, each service runs in its own container, and for scalability, you’ll need to run multiple “copies” of each service.

You might end up with a workflow with thousands of containers! It’s hard to keep track of all of them and make sure they run at the right times.

This is where Kubernetes comes in. Kubernetes is a platform for “automating deployment, scaling, and operations of application containers across clusters of hosts.” It was first made at Google.

This tool can be used with Docker. Docker Engine is used to start up and manage each container individually.

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There is an m-by-n integer grid called that shows an. Each pixel in this grid is represented by [i][j].

To perform a flood fill, start at the starting pixel and change it’s original color to newColor.

Then, change the color of any pixels that are 4-way connected to the first pixel and have the same color as the first pixel.

After that, fill the color of any pixels that are 4-way connected to the pixels that already have the same color.

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First, find the preorder representation of each tree. Then, turn them into strings. Finally, check to see if the subtree’s preorder string is a substring of the other tree’s string.

Finding the preorder representations and converting them to strings takes linear time. The string search (to see if one if a substring of the other) also takes linear time.

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Introduction to Classical Compiler Design

Traditional static compilers most often use a three-phase design, which has the front end, the optimizer, and the back end as its main parts.

The front end parses the source code (checking it for errors) and builds a language-specific Abstract Syntax Tree.

For optimization purposes, the AST may be changed to a new representation. This new representation may be a common code representation, where the code is the same no matter what language the input source code is written in.

The optimizer changes the code in a number of ways to make it run faster, use less memory, take up less space, and so on.

This is more or less independent of the input source code language and the target language

The back end maps the optimized code onto the target instruction set.

It’s responsible for generating good code that takes advantage of the specific features of the supported architecture.

Common parts of the back end include instruction selection, register allocation, and instruction scheduling.

The best thing about this design is how easy it is to make a compiler work with more than one source language or target architecture.

You have to make a new compiler front end for a new source language when you port the compiler to support it, but you can use the same optimizer and back end.

The same applies for adding a new target architecture for the compiler. You just have to implement the back end and you can reuse the front end and optimizer.

Additionally, you can use specific parts of the compiler for other purposes. For example, pieces of the compiler front end could be used for documentation generation and static analysis tools.

The main issue was that this model was rarely realized in practice. Before LLVM, there were open source language implementations for Perl, Python, Ruby, Java, and other languages. shared no code.

GHC and FreeBASIC were made to compile on a number of different CPUs, but their implementations were only compatible with the source language they supported (Haskell for GHC).

Compilers like GCC suffered from layering problems and leaky abstractions. GCC’s front end ASTs are used by the back end to make debug information, and the front end makes back end data structures.

The LLVM project sought to fix this.

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