From 36d55756608a227f5698cb120814c793edacd863 Mon Sep 17 00:00:00 2001 From: Chris Lattner Date: Tue, 13 Nov 2007 07:06:30 +0000 Subject: Many typos, grammaro, and wording fixes. Patch by Kelly Wilson, thanks! llvm-svn: 44043 --- llvm/docs/tutorial/LangImpl4.html | 55 +++++++++++++++++++-------------------- 1 file changed, 27 insertions(+), 28 deletions(-) (limited to 'llvm/docs/tutorial/LangImpl4.html') diff --git a/llvm/docs/tutorial/LangImpl4.html b/llvm/docs/tutorial/LangImpl4.html index 04475e638e2..378b29b3163 100644 --- a/llvm/docs/tutorial/LangImpl4.html +++ b/llvm/docs/tutorial/LangImpl4.html @@ -42,8 +42,8 @@ Flow with LLVM" tutorial. Chapters 1-3 described the implementation of a simple language and added support for generating LLVM IR. This chapter describes two new techniques: adding optimizer support to your language, and adding JIT -compiler support. This shows how to get nice efficient code for your -language.

+compiler support. These additions will demonstrate how to get nice, efficient code +for the Kaleidoscope language.

@@ -72,14 +72,13 @@ entry: -

This code is a very very literal transcription of the AST built by parsing -our code, and as such, lacks optimizations like constant folding (we'd like to -get "add x, 3.0" in the example above) as well as other more important -optimizations. Constant folding in particular is a very common and very +

This code is a very, very literal transcription of the AST built by parsing +the input. As such, this transcription lacks optimizations like constant folding (we'd like to get "add x, 3.0" in the example above) as well as other more important +optimizations. Constant folding, in particular, is a very common and very important optimization: so much so that many language implementors implement constant folding support in their AST representation.

-

With LLVM, you don't need to. Since all calls to build LLVM IR go through +

With LLVM, you don't need this support in the AST. Since all calls to build LLVM IR go through the LLVM builder, it would be nice if the builder itself checked to see if there was a constant folding opportunity when you call it. If so, it could just do the constant fold and return the constant instead of creating an instruction. @@ -93,9 +92,9 @@ static LLVMFoldingBuilder Builder;

All we did was switch from LLVMBuilder to -LLVMFoldingBuilder. Though we change no other code, now all of our -instructions are implicitly constant folded without us having to do anything -about it. For example, our example above now compiles to:

+LLVMFoldingBuilder. Though we change no other code, we now have all of our +instructions implicitly constant folded without us having to do anything +about it. For example, the input above now compiles to:

@@ -153,7 +152,7 @@ range of optimizations that you can use, in the form of "passes".

 
 

 
-
LLVM provides many optimization passes which do many different sorts of
+
LLVM provides many optimization passes, which do many different sorts of
 things and have different tradeoffs.  Unlike other systems, LLVM doesn't hold
 to the mistaken notion that one set of optimizations is right for all languages
 and for all situations.  LLVM allows a compiler implementor to make complete
@@ -165,7 +164,7 @@ across as large of body of code as they can (often a whole file, but if run
 at link time, this can be a substantial portion of the whole program).  It also
 supports and includes "per-function" passes which just operate on a single
 function at a time, without looking at other functions.  For more information
-on passes and how the get run, see the How
+on passes and how they are run, see the How
 to Write a Pass document and the List of LLVM 
 Passes.

 
@@ -207,13 +206,13 @@ add a set of optimizations to run.  The code looks like this:
-

This code defines two objects, a ExistingModuleProvider and a +

This code defines two objects, an ExistingModuleProvider and a FunctionPassManager. The former is basically a wrapper around our Module that the PassManager requires. It provides certain flexibility -that we're not going to take advantage of here, so I won't dive into what it is -all about.

+that we're not going to take advantage of here, so I won't dive into any details +about it.

-

The meat of the matter is the definition of "OurFPM". It +

The meat of the matter here, is the definition of "OurFPM". It requires a pointer to the Module (through the ModuleProvider) to construct itself. Once it is set up, we use a series of "add" calls to add a bunch of LLVM passes. The first pass is basically boilerplate, it adds a pass @@ -223,7 +222,7 @@ which we will get to in the next section.

In this case, we choose to add 4 optimization passes. The passes we chose here are a pretty standard set of "cleanup" optimizations that are useful for -a wide variety of code. I won't delve into what they do, but believe me that +a wide variety of code. I won't delve into what they do but, believe me, they are a good starting place :).

Once the PassManager is set up, we need to make use of it. We do this by @@ -247,7 +246,7 @@ running it after our newly created function is constructed (in -

As you can see, this is pretty straight-forward. The +

As you can see, this is pretty straightforward. The FunctionPassManager optimizes and updates the LLVM Function* in place, improving (hopefully) its body. With this in place, we can try our test above again:

@@ -271,7 +270,7 @@ add instruction from every execution of this function.

LLVM provides a wide variety of optimizations that can be used in certain circumstances. Some documentation about the various passes is available, but it isn't very complete. Another good source of -ideas is to look at the passes that llvm-gcc or +ideas can come from looking at the passes that llvm-gcc or llvm-ld run to get started. The "opt" tool allows you to experiment with passes from the command line, so you can see if they do anything.

@@ -324,7 +323,7 @@ for you if one is available for your platform, otherwise it will fall back to the interpreter.

Once the ExecutionEngine is created, the JIT is ready to be used. -There are a variety of APIs that are useful, but the most simple one is the +There are a variety of APIs that are useful, but the simplest one is the "getPointerToFunction(F)" method. This method JIT compiles the specified LLVM Function and returns a function pointer to the generated machine code. In our case, this means that we can change the code that parses a @@ -353,7 +352,7 @@ static void HandleTopLevelExpression() { function that takes no arguments and returns the computed double. Because the LLVM JIT compiler matches the native platform ABI, this means that you can just cast the result pointer to a function pointer of that type and call it directly. -As such, there is no difference between JIT compiled code and native machine +This means, there is no difference between JIT compiled code and native machine code that is statically linked into your application.

With just these two changes, lets see how Kaleidoscope works now!

@@ -372,7 +371,7 @@ entry:

Well this looks like it is basically working. The dump of the function shows the "no argument function that always returns double" that we synthesize -for each top level expression that is typed it. This demonstrates very basic +for each top level expression that is typed in. This demonstrates very basic functionality, but can we do more?

@@ -397,19 +396,19 @@ entry:
-

This illustrates that we can now call user code, but it is a bit subtle what -is going on here. Note that we only invoke the JIT on the anonymous functions -that calls testfunc, but we never invoked it on testfunc -itself.

+

This illustrates that we can now call user code, but there is something a bit subtle +going on here. Note that we only invoke the JIT on the anonymous functions +that call testfunc, but we never invoked it on testfunc +itself.

-

What actually happened here is that the anonymous function is +

What actually happened here is that the anonymous function was JIT'd when requested. When the Kaleidoscope app calls through the function pointer that is returned, the anonymous function starts executing. It ends up making the call to the "testfunc" function, and ends up in a stub that invokes the JIT, lazily, on testfunc. Once the JIT finishes lazily compiling testfunc, it returns and the code re-executes the call.

-

In summary, the JIT will lazily JIT code on the fly as it is needed. The +

In summary, the JIT will lazily JIT code, on the fly, as it is needed. The JIT provides a number of other more advanced interfaces for things like freeing allocated machine code, rejit'ing functions to update them, etc. However, even with this simple code, we get some surprisingly powerful capabilities - check -- cgit v1.2.3