If anyone's wondering, its response to the GP comment is 100% correct as far as I can tell. Only problem is that it's missing technical details and has a hard time giving specific examples.
---
There are several reasons why programming languages might choose to rely on LLVM or other external compilers and optimization frameworks rather than implementing optimizations on their own:
Focus on language design: Many programming languages prioritize language design and functionality over compiler optimization. Implementing optimizations can be a complex and time-consuming process, and language designers may prefer to focus on other aspects of the language.
Difficulty of optimization: While some optimizations may seem simple in theory, they can be difficult to implement in practice, especially for complex programs. Additionally, different optimizations may interact with each other in unpredictable ways, making it challenging to optimize code effectively.
Targeting multiple platforms: Many programming languages are designed to be portable and run on multiple platforms. Relying on external compilers and optimization frameworks like LLVM can help ensure that code is optimized for each platform without requiring extensive platform-specific optimizations.
Access to expertise: Compiler optimization is a specialized field that requires a deep understanding of computer architecture, algorithms, and programming languages. By relying on external frameworks like LLVM, programming languages can leverage the expertise of experienced optimization engineers and researchers.
As for the implementation of the top 20 optimizations, the specific details will depend on the optimization and the programming language being used. However, many optimizations involve analyzing the code to identify opportunities for improvement and then applying transformations to the code to make it more efficient. Some optimizations, like constant propagation and loop unrolling, may involve relatively simple transformations, while others, like register allocation and instruction scheduling, can be more complex.
--- snippets from "Use highly technical details and examples to answer this question.":
Memory reuse involves optimizing the use of memory to reduce the number of accesses and minimize cache misses. This can involve techniques such as loop-invariant code motion, which moves code that does not depend on loop variables outside of the loop, or loop fusion, which combines multiple loops that iterate over the same data into a single loop. However, these optimizations may also depend on the specific architecture being targeted, as the optimal memory access patterns may differ depending on the cache size and associativity
Writing a high-quality compiler that can handle all of the edge cases and produce optimized code for a wide range of architectures requires a significant amount of expertise and resources. Additionally, using an external compiler can provide a degree of portability, as the same code can be compiled for multiple architectures without requiring changes to the code itself
--- snippet from "Why would constant propagation need a PhD level proficiency to implement? It’s basically running part of your code and no different from parsing.":
Constant propagation is indeed a relatively simple optimization technique in theory, and it does not require a PhD level proficiency to implement it. In fact, most compilers include some form of constant propagation as part of their optimization pipeline, and many of these implementations are relatively straightforward.
However, there are several factors that can make constant propagation more challenging to implement in practice. Some of these factors include:
Handling interactions with other optimizations: Constant propagation can interact with other optimizations in non-trivial ways, such as dead code elimination and loop unrolling. Ensuring that these optimizations work together correctly can require careful analysis and testing.
Dealing with complex data structures: In some cases, constant propagation may need to deal with complex data structures, such as arrays or pointers. Ensuring that constant propagation works correctly in these cases can require more advanced techniques, such as dataflow analysis or points-to analysis.
Supporting language features: Different programming languages may have different rules for when constant propagation is allowed or required. For example, some languages may allow constants to be defined using complex expressions or function calls, while others may require constants to be literal values. Supporting these language features can require additional analysis and code generation logic.
Doing a good job of constant prop means doing a thorough job of implementing the best algorithm in the best paper. And to do that, you need to build a form of SSA. And to do that you need to compute dominators. And to do that, you need to build an appropriate form of the CFG. Don't need a PhD to do all those things (not inventing anything new here, just using other peoples' beautiful results), but it's still plenty of work to build a fast & thorough implementation, with lots of testing required.
Can't possibly be economical; but of course, the experience will teach you a lot.
Other than the first response, which seems to be just the most basic eli5 way of answering the question (and probably not helpful to original question), this has some pretty solid answers on the difficulty that might not be obvious to someone who hasn't worked with LLVM or built a programming language before.
If anyone's wondering, its response to the GP comment is 100% correct as far as I can tell. Only problem is that it's missing technical details and has a hard time giving specific examples.
---
There are several reasons why programming languages might choose to rely on LLVM or other external compilers and optimization frameworks rather than implementing optimizations on their own:
Focus on language design: Many programming languages prioritize language design and functionality over compiler optimization. Implementing optimizations can be a complex and time-consuming process, and language designers may prefer to focus on other aspects of the language.
Difficulty of optimization: While some optimizations may seem simple in theory, they can be difficult to implement in practice, especially for complex programs. Additionally, different optimizations may interact with each other in unpredictable ways, making it challenging to optimize code effectively.
Targeting multiple platforms: Many programming languages are designed to be portable and run on multiple platforms. Relying on external compilers and optimization frameworks like LLVM can help ensure that code is optimized for each platform without requiring extensive platform-specific optimizations.
Access to expertise: Compiler optimization is a specialized field that requires a deep understanding of computer architecture, algorithms, and programming languages. By relying on external frameworks like LLVM, programming languages can leverage the expertise of experienced optimization engineers and researchers.
As for the implementation of the top 20 optimizations, the specific details will depend on the optimization and the programming language being used. However, many optimizations involve analyzing the code to identify opportunities for improvement and then applying transformations to the code to make it more efficient. Some optimizations, like constant propagation and loop unrolling, may involve relatively simple transformations, while others, like register allocation and instruction scheduling, can be more complex.
--- snippets from "Use highly technical details and examples to answer this question.":
Memory reuse involves optimizing the use of memory to reduce the number of accesses and minimize cache misses. This can involve techniques such as loop-invariant code motion, which moves code that does not depend on loop variables outside of the loop, or loop fusion, which combines multiple loops that iterate over the same data into a single loop. However, these optimizations may also depend on the specific architecture being targeted, as the optimal memory access patterns may differ depending on the cache size and associativity
Writing a high-quality compiler that can handle all of the edge cases and produce optimized code for a wide range of architectures requires a significant amount of expertise and resources. Additionally, using an external compiler can provide a degree of portability, as the same code can be compiled for multiple architectures without requiring changes to the code itself
--- snippet from "Why would constant propagation need a PhD level proficiency to implement? It’s basically running part of your code and no different from parsing.":
Constant propagation is indeed a relatively simple optimization technique in theory, and it does not require a PhD level proficiency to implement it. In fact, most compilers include some form of constant propagation as part of their optimization pipeline, and many of these implementations are relatively straightforward.
However, there are several factors that can make constant propagation more challenging to implement in practice. Some of these factors include:
Handling interactions with other optimizations: Constant propagation can interact with other optimizations in non-trivial ways, such as dead code elimination and loop unrolling. Ensuring that these optimizations work together correctly can require careful analysis and testing.
Dealing with complex data structures: In some cases, constant propagation may need to deal with complex data structures, such as arrays or pointers. Ensuring that constant propagation works correctly in these cases can require more advanced techniques, such as dataflow analysis or points-to analysis.
Supporting language features: Different programming languages may have different rules for when constant propagation is allowed or required. For example, some languages may allow constants to be defined using complex expressions or function calls, while others may require constants to be literal values. Supporting these language features can require additional analysis and code generation logic.