Speaker 1: So you've probably done this right, type to line of code,
Speaker 1: hit compile dot, and then you know, just watch your
Speaker 1: program run. It feels kind of like magic, doesn't it
Speaker 1: one moment? It's if by five the next poof the
Speaker 1: computer's doing it. Today, we're really going to pull back
Speaker 1: the curtain on that magic. We're doing a deep dive
Speaker 1: into compiler construction, and we're using Bill Campbell's introduction to
Speaker 1: compiler construction in a Java world as our map.
Speaker 2: Our mission really is, right, it's a translation, but a
Speaker 2: very sophisticated one. Think of it like translating a complex novel,
Speaker 2: your high level language into a super precise, step by
Speaker 2: step instruction manual for a worker, which is the computer
Speaker 2: and the absolute key. The non negotiable rule is it
Speaker 2: has to be semantics preserves scientic's preserving right, meaning the
Speaker 2: translated program it must behave exactly like the original, every detail,
Speaker 2: every calculation perfectly mirrors.
Speaker 1: Okay, got it, But then why compile it all? Why
Speaker 1: not just interpret everything directly? Seems simpler.
Speaker 2: That's a really good question. There are two main reasons. Usually,
Speaker 2: first performance, native machine code just runs way way faster,
Speaker 2: and interpreter it has to parse and analyze every statement
Speaker 2: every single time it runs.
Speaker 1: Okay, so compiling does that work up front.
Speaker 2: Exactly once, then it just runs. The second reason, well,
Speaker 2: it's intellectual property or secrecy. Compiled machine code is much
Speaker 2: harder to figure out to reverse engineer back into readable
Speaker 2: source code. It offers protection.
Speaker 1: Right, makes sense.
Speaker 2: Now. Interpretation definitely still has its uses, you know, for
Speaker 2: simple stuff scripts you don't run often, like Unix shell
Speaker 2: scripts maybe, or some dynamic languages like Lisp where you
Speaker 2: need names available at runtime. Compilation overhead isn't always worth it.
Speaker 1: So when we talk about that native machine code, we're
Speaker 1: really getting down to the metal, right, Things like memps
Speaker 1: or Intel I three eighty six. Are they very different?
Speaker 2: Yeah, they represent different philosophies. You have CISC complex instruction
Speaker 2: set computers like Intel's I three eighty six and RISC
Speaker 2: reduced instruction set computers like MIMPRSS. He tries to do
Speaker 2: more in one instruction. RIS uses simplier steps. But the
Speaker 2: key thing for the compiler is understanding these instructions and
Speaker 2: how to use the CPU's super fast internal memory spots.
Speaker 1: The register registers. Okay, those are limited.
Speaker 2: Right, very limited? Maybe thirty two in a typical RSC machine,
Speaker 2: even fewer, like eight general purpose ones in older CISE,
Speaker 2: and they're way faster than main memory. So a huge
Speaker 2: job for the compiler is figuring out how to keep
Speaker 2: your variables and intermediate results right there in those fast
Speaker 2: registers as much as possible.
Speaker 1: So it's mapping names, generating the code sequence, and catching errors.
Speaker 2: Absolutely, mapping names to memory or registers, generating that linear
Speaker 2: machine code, and finding errors early on. Those are the
Speaker 2: core tasks.
Speaker 1: It sounds like it's not just one big program doing
Speaker 1: all this, more like an assembly line.
Speaker 2: That's a great analogy. Yes, compilers are usually broken down
Speaker 2: into phases. Broadly, you've got a front end and a
Speaker 2: back end. The front end deals with the analysis. It
Speaker 2: takes your source code, understands its meaning, and this part
Speaker 2: is source language dependent. It cares if it's Java or
Speaker 2: C or whatever. Its final output is something called an
Speaker 2: intermediate representation or IR. Think of it as a generic
Speaker 2: version of your program.
Speaker 1: Okay, And what happens inside that front end? What are
Speaker 1: the steps?
Speaker 2: Several subphases. First up is the scanner or lexical analyzer.
Speaker 2: It's like breaking a sentence into words. It reads the
Speaker 2: stream of characters and spits out a stream of tokens
Speaker 2: identifiers like my var, numbers like forty two, keywords like if, operators,
Speaker 2: then comments ah good point. Comments are scanned, recognized, and
Speaker 2: then just thrown away. They mean nothing to the subsequent phases.
Speaker 1: Right. So, after tokens.
Speaker 2: Comes the parser or syntax analyzer. It takes the token
Speaker 2: stream and builds an abstract syntax tree the AST. This
Speaker 2: tree represents the grammatical structure of your program. It figures
Speaker 2: out how the tokens relate to each other based on
Speaker 2: the language's grammar.
Speaker 1: Rules, like diagramming a sentence exactly like that.
Speaker 2: Then the final analysis step is the semantic analyzer. This
Speaker 2: is where type checking happens. It makes sure you're not
Speaker 2: trying to add a number to a customer object Bickley.
Speaker 2: It forces type rules. It also figures out where to
Speaker 2: store variables, maybe on the stack frame, and can even
Speaker 2: rewrite bits of the AST to make things clearer for
Speaker 2: later stages.
Speaker 1: Okay, so the front end understands the code's meaning and structure.
Speaker 1: What's next is it straight to machine code?
Speaker 2: Often there's a step in between, sometimes called the middle end.
Speaker 2: It's kind of optional but very common. Its whole job
Speaker 2: is optimization. It takes that intermediate representation, the IR, and
Speaker 2: tries to make it better, faster, smaller. How does it
Speaker 2: do that various ways.
Speaker 1: It might.
Speaker 2: Loops that don't change loop invariants and pull them out
Speaker 2: so they only run once, or replace an expensive operation
Speaker 2: like multiplication with a cheaper one like addition, if possible.
Speaker 2: It's called strength reduction. It polishes the.
Speaker 1: IR optimized IR, then comes the back end.
Speaker 2: Then comes the back end the synthesis phase. This takes
Speaker 2: the R and generates the action target machine code. And
Speaker 2: this part is target language dependent. It needs to know
Speaker 2: if it's generating code for Intel or AR or MIIPS.
Speaker 1: It doesn't have subphases.
Speaker 2: Too, it does. There's code generation itself, picking the actual
Speaker 2: machine instructions. Then often a peephole optimizer. This looks at
Speaker 2: a small window of instructions just generated and looks for
Speaker 2: obvious local inefficiencies, like maybe a jump that immediately jumps
Speaker 2: somewhere else, tiing up locally exactly. And finally the object phase,
Speaker 2: which handles linking different compiled pieces together into the final
Speaker 2: executable program.
Speaker 1: Wow, that's quite a journey breaking it down like that.
Speaker 1: What's the big win for compiler builders?
Speaker 2: The modularity is huge. The biggest win is probably reusability
Speaker 2: thanks to that intermediate representation. If you have a well
Speaker 2: designed IR, it acts like a universal adapter. You can
Speaker 2: write one front end for Java and plug it into
Speaker 2: different back ends for Intel Era, whatever, or write one
Speaker 2: back end for Intel and plug in front ends for
Speaker 2: Java C plus plus Python. The source material gives a
Speaker 2: great example. Once you have a front end for Java
Speaker 2: and a back end for the Intel Core duo, you
Speaker 2: only need to write a new C front end to
Speaker 2: get a C compiler at a Spark back end, and
Speaker 2: you get two more compilers, four for the price of two.
Speaker 2: It's incredibly efficient.
Speaker 1: That makes a lot of sense. Now, Java specifically, it
Speaker 1: has a bit of a unique approach, doesn't it. It's
Speaker 1: not quite that direct to native code path we just described.
Speaker 2: That's absolutely right. Java takes a fascinating detour. When you
Speaker 2: compile Java source code using Javac dot Burke, you don't
Speaker 2: get native machine code. You get a dot class file
Speaker 2: which contains bytecode. This bitcode isn't for your actual CPU,
Speaker 2: it's for the Java Virtual Machine, the JVM. The JVM
Speaker 2: itself starts out as an interpreter.
Speaker 1: For this bike code an interpreter. But I thought we
Speaker 1: said compilation was faster. Ah.
Speaker 2: But here's the clever bit. The JVM uses just in
Speaker 2: time JIT compilation and techniques like hotspot compilation. As your
Speaker 2: Java program runs, the JVM watches itself. It identifies the
Speaker 2: hotspots methods or loops that are executed frequently, and then
Speaker 2: on the fly during execution, it compiles these specific hotspots
Speaker 2: down to native machine code for the actual hardware you're
Speaker 2: running on.
Speaker 1: WHOA, So it compiles while the program.
Speaker 2: Is running exactly it caches this native code. The next
Speaker 2: time that hotspot is hit, it runs the super fast
Speaker 2: native version. It's dynamic, and often this can lead to
Speaker 2: performance that rivals or even beats statically compiled code in
Speaker 2: some benchmarks. Because the JIT compiler has runtime information, so
Speaker 2: JVM bytecode essentially acts as another layer of intermediate representation.
Speaker 1: That's incredibly dynamic. How do the big players, the celebrity
Speaker 1: compilers like Oracles, manage that complexity.
Speaker 2: Well, let's look at Oracle's Java hotspot compiler. It's kind
Speaker 2: of the standard. It actually has two main JT compilers,
Speaker 2: a client one for fast startup and a server one
Speaker 2: for heavy optimization. The server compiler does amazing things like
Speaker 2: using advanced IR sophisticated register allocation, But one of the
Speaker 2: coolest things is dynamic de optimization optimization. It undoes its
Speaker 2: own work precisely. Hotspot can be really aggressive with optimizations,
Speaker 2: like inlining methods. It makes assumptions, but what if later
Speaker 2: the program loads a new class that invalidates an assumption,
Speaker 2: maybe overrides a method it inlined. Hotspot can detect this,
Speaker 2: throw away the now incorrect native code for that section,
Speaker 2: and fall back to interpreting it. Then it might reoptimize
Speaker 2: later with the new information.
Speaker 1: That's wild. It lets it be optimistic because it has
Speaker 1: an escape patch exactly.
Speaker 2: It can be aggressively optimistic. Another trick is onstack replacement OSR.
Speaker 2: Imagine a method is stuck in a long loop running
Speaker 2: in interpreted mode. Hotspot can compile an optimized version of
Speaker 2: that loop on the side and then incredibly swap the
Speaker 2: currently executing interpreted cun and its state with the new
Speaker 2: compiled version right in the middle of the loop. Execution continues,
Speaker 2: but now it's fast.
Speaker 1: Mind boggling. What about other compilers like the one used
Speaker 1: in Eclipse.
Speaker 2: AH the Eclipse compiler for Java BCJ it's claim to
Speaker 2: fame as being an in compiler. After you build your
Speaker 2: project once, if you change just one file, ECJ is
Speaker 2: smart enough to only recompile that file and any others
Speaker 2: that directly depend on it.
Speaker 1: Well, that must be way faster for big projects.
Speaker 2: Usually faster developers love it. And here's a really surprising
Speaker 2: thing the source mentions about ECJ. It actually lets you
Speaker 2: run an even debug code that contains errors.
Speaker 1: Wait, what how can that work?
Speaker 2: If you have an error and a method, but that
Speaker 2: method is never actually called during your test run, the
Speaker 2: program runs fine. The error only matters if you actually
Speaker 2: try to execute that specific broken part. If you do,
Speaker 2: you get an exception. But otherwise it works great for
Speaker 2: rapid development.
Speaker 1: That's surprisingly pragmatic. So we have dynamic incremental. Any other
Speaker 1: major approaches.
Speaker 2: Well, there's the g and U Java compiler GCJ. Its
Speaker 2: focus was different, more on static compilation. It aim to
Speaker 2: compile Java directly to native machine code ahead of time,
Speaker 2: like a traditional plus compiler. This gives you fast startup
Speaker 2: and potentially lower memory use, which can be good for
Speaker 2: embedded systems, but you lose some of the dynamic advantages.
Speaker 1: And we should mention Microsoft's cshard compiler for dot Net too.
Speaker 1: It's similar to Java in concept to compile c chap
Speaker 1: into Common Intermediate Language CIL, which is like JVM bytecode.
Speaker 1: The CIL is then run by the common language runtime CLR,
Speaker 1: which uses JT compilation.
Speaker 2: So a similar virtual machine and JIT approach very similar.
Speaker 1: Dot Net has its own bytcode format CIL and its
Speaker 1: own virtual machine CLR doing the JT magic. This is
Speaker 1: all fascinatingly complex, but the source talks about a hands
Speaker 1: on way to learn this using J How does that
Speaker 1: make it more concrete?
Speaker 2: J is brilliant for teaching. It's a subset of Java,
Speaker 2: maybe half the syntax, but still a proper object oriented language.
Speaker 2: The compiler for it describing the book is over thirty
Speaker 2: thousand lines. So by working with JJ, adding features, fixing bugs,
Speaker 2: you're not just learning compiler theory, you're practicing real software engineering,
Speaker 2: and honestly, it makes you a better Java programmer too.
Speaker 1: Okay, let's make it practical. Suppose we wanted to add
Speaker 1: a simple division operator a t J. How would that
Speaker 1: touch the different phases we talked about?
Speaker 2: Great example. Okay, first, lexical analysis. The scanner needs to
Speaker 2: recognize as a new token let's call it div, so
Speaker 2: when it sees in the source code, it outputs DV
Speaker 2: instead of say an error.
Speaker 1: Step one, recognize the symbol.
Speaker 2: Step two syntax analysis. The parser needs a new rule.
Speaker 2: We'd define a new node for our abstracts and tax tree,
Speaker 2: maybe JADA write up. The parser's grammar rules would be
Speaker 2: updated to note that an expression can be expression div expression,
Speaker 2: and it would build that jadavide up node in the AST.
Speaker 1: So the tree now represents division structurally exactly.
Speaker 2: Step three semantic analysis. This is type checking. We'd add
Speaker 2: rules here. J probably only allows in division, so the
Speaker 2: analyzer checks are the left and right operands of jadavide
Speaker 2: up both integers if not throw a type error. It
Speaker 2: also handles story delocation, but for a simple operator typechecking
Speaker 2: is key makes sense.
Speaker 1: Can't divide an integer by a Hello world precisely.
Speaker 2: Finally, Step four code generation. Assuming it past semantic analysis,
Speaker 2: we generate the JVM bytecode. Would use the provided tools
Speaker 2: like slimmett and the books example to output the right
Speaker 2: JVM instructions, probably something like loading the two operands onto
Speaker 2: the JVM stack, then issuing the ID of instruction for
Speaker 2: integer division.
Speaker 1: Wow. Okay, so even adding one operator forces you to
Speaker 1: modify the scanner, the parser, the semantic analyzer, and.
Speaker 2: The code generator every single phase. It really illustrates how
Speaker 2: interconnected it all is. And the j approach emphasizes writing
Speaker 2: tests first. Using extreme programming, you'd write tests that should
Speaker 2: compile with division and tests that should fail with specific
Speaker 2: errors before you even implement the operator.
Speaker 1: Test driven compiler development makes sense.
Speaker 2: It ensures robustness.
Speaker 1: So after all that, we might have JVM bytcode, but
Speaker 1: sometimes you need ROSS by native code. The source even
Speaker 1: talks about a JVM to my pace translator. What's that stage.
Speaker 2: About, right, that's pushing it all the way to the hardware.
Speaker 2: In that scenario, the JVM bytcode we just generated that
Speaker 2: becomes a new sourceling which or rather a new IR.
Speaker 2: Thead JVM compiler acts as the front end, and this
Speaker 2: JVM to SPIM translator SPIM simulates a mi IPS is
Speaker 2: the new back end. Its compilation layered on compilation.
Speaker 1: Buying the BI code itself.
Speaker 2: Exactly, and this back end needs to understand the target
Speaker 2: machine mi IPS in this case its memory layout, where
Speaker 2: code goes, where static data goes, the heap, the stack,
Speaker 2: and its registers. Those thirty two on ips registers, knowing
Speaker 2: which ones are for arguments mount fewer dollars, return values
Speaker 2: V dollar V dollars, the return address, which ones the
Speaker 2: called function must save Salmi's dollars, and which ones the
Speaker 2: caller must save if needed. What's conventions to follow absolutely
Speaker 2: and this back end often uses its own intermediate representations
Speaker 2: to manage the complexity. It might first convert JVM bycode
Speaker 2: into a high level ir HIR. This often uses static
Speaker 2: single assignment SSA form. It's a form where every variable
Speaker 2: is assigned of value exactly once. If a variable could
Speaker 2: get its value from different places, like after an IFLSE,
Speaker 2: special five functions are introduced to represent that merge. It
Speaker 2: makes many optimizations easier to implement. Then this HIR is
Speaker 2: lowered to a low level ir LR, the five functions
Speaker 2: are gone, replaced by actual move instructions. And crucially, the
Speaker 2: LAR often uses an unlimited number of virtual registers. It's
Speaker 2: much closer to the final machine code, but without worrying
Speaker 2: about the limited physical registers.
Speaker 1: Just yet, unlimited virtual registers. But the chip only has say,
Speaker 1: thirty two physical ones, how does they get resolved?
Speaker 2: That's the magic of register allocation. It's one of the
Speaker 2: most critical and complex parts of a modern back end.
Speaker 2: The goal is to map those unlimited virtual registers onto
Speaker 2: the scarce physical registers. When you have more virtual registers
Speaker 2: live holding values still needed than physical registers available, you
Speaker 2: have to spill some virtual registers. That means storing their
Speaker 2: values out to memory, usually the stack, and reloading them
Speaker 2: later when needed.
Speaker 1: Ah, and spilling is slow.
Speaker 2: Spilling is slow, so a good register allocator tries really
Speaker 2: hard to minimize spills. Techniques like linear scan or graph
Speaker 2: coloring are used. It's a huge area of compiler research.
Speaker 1: Okay, and you mentioned optimizations happen in the middle end,
Speaker 1: but surely there are more optimizations happening here in the
Speaker 1: back end too.
Speaker 2: Oh. Definitely, optimizations happen at multiple stages, often on these
Speaker 2: intermediate representations like HIR things like inlining replacing a function
Speaker 2: call with the function's actual code to avoid call return overhead.
Speaker 2: Very common. Constant folding and propagation. If you have xplicus
Speaker 2: three y quills x plus four, the compiler can figure
Speaker 2: out why is always seven and just use seven.
Speaker 1: Simple but effective.
Speaker 2: Very common sub expression elimination CSE, finding identical calculations and
Speaker 2: doing them only once, storing the result. Think about calculating
Speaker 2: an array elements address inside nested loops. CSE can save
Speaker 2: a lot of redundant math lifting loop invariant code. We
Speaker 2: mentioned this before. If a calculation inside a loop doesn't
Speaker 2: depend on the loop iteration, pull it out, compute at
Speaker 2: once before the loop starts. Array bounds check elimination Java,
Speaker 2: for instance, checks if your array index is valid. But
Speaker 2: if the compiler can prove the index will always be
Speaker 2: in bounds, maybe for iokal to array dot linksh one,
Speaker 2: it can remove that check entirely, saving potentially millions of
Speaker 2: checks in a big loop.
Speaker 1: That sounds like it can make a massive difference.
Speaker 2: It really does. The source quote's an expert Kathleen Nubbs
Speaker 2: saying it's often best to just try all the optimizations,
Speaker 2: even imperfectly. The combined effect is usually huge.
Speaker 1: Wow. What a journey from typing code through tokens, trees bycode, irs, optimizations,
Speaker 1: register allocation, finally down to the bare metal instructions. It's incredible,
Speaker 1: it really is.
Speaker 2: This deep dive shows that behind every app you use,
Speaker 2: every website, there's this incredibly intricate dance of translation analysis
Speaker 2: and refinement going on a compiler isn't just translating. It's optimizing, checking, restructuring,
Speaker 2: constantly working to bridge the gap between how we think
Speaker 2: and how the machine works.
Speaker 1: And it definitely drives home that even if you don't
Speaker 1: build compile, just knowing this process exists, understanding the steps
Speaker 1: makes you much more informed programmer. Right, you appreciate what's
Speaker 1: happening under the hood.
Speaker 2: Absolutely, you understand why some code might be faster than
Speaker 2: other code, or why certain language features might have performance implications.
Speaker 2: It demystifies things, and maybe a final thought to leave with,
Speaker 2: think about how these compiler techniques keep evolving JIT hotspot,
Speaker 2: dynamic deoptimization, incremental builds. It reflects this constant push and
Speaker 2: pull in computing between rows, speed, the flexibility programmers need,
Speaker 2: and managing the sheer complexity of modern software and hardware.
Speaker 2: What's the next piece of compiler magic going to.
Speaker 1: Be a fascinating question? Indeed, well, thank you for joining
Speaker 1: us on this really deep dive into the compiler's world.
Speaker 1: We hope you listening start seeing these princibles everywhere in
Speaker 1: the technology you use.