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coldbrew

coldbrew is a toy interpreter and tracing JIT compiler for the JVM it mainly serves as a demo project for how JIT compilers work in genenral.

Currently coldbrew is able to successfully interpret, record, compile and execute native code on x86-64 for some very simple demo programs e.g support/jit.

coldbrew is inspired primarly by TigerShrimp1 and some ideas from Higgs2 the TigerShrimp C++ implementation3 is very readable and was of huge help.

Other implementations I've found useful is LuaJIT 2.0 and Mike Pall's email about the LuaJIT internals which you can in the mailing list4.

While I tried to remain as close as the TigerShrimp implementation as possible, there are some changes such as (trace recording logic is different, we don't support trace stitching and we want to maybe add support for inlining calls).

It's possible support for ARM64 will be added in the future.

I was originally planning to use the C++ implementation as a baseline to test against but I didn't have much success building it.

How it works

coldbrew bundles a traditional bytecode interpreter with a runtime for the JVM as per the Java SE7 specification described in the link below5, during the execution the bytecode is profiled and execution traces are recorded.

The recorded traces are self-contained with backwards branches and inner branches only. Ideally we want to make this even more self-contained by recording just basic blocks.

The trace contains all the information needed to compile the bytecode to native such as the entry, exit codes and the bytecode of the core loop.

Once a trace is ready we pipeline it to the JIT cache for compilation and when we reach that code path again (loop entry) execution leaves the interpreter and executes the compiled native trace.

When a compiled trace finishes executing we overwrite the interpreter current stack frame to record all mutations that happened in native code then execution is returned to the interpreter again.

Trace recording and execution

To identify hotpaths we use heuristics that target loop headers which we can identify when we encounter a backwards branch instruction. Once such branch is identified we record the program counter where we branch as the start of the loop.

But it's not sufficient to track backwards branches we need to calculate their execution frequency to identify if they are hot, the invocation frequency threshold (currently set to 1) triggewrs the start of recording.

An example of a trace would be a sequence of bytecode like the one below, the format is Inst(opcode, operands) @ PC:

Inst(iload, Some([Int(2)])) @ Instruction Index 6 @ Method Index: 11
Inst(bipush, Some([Int(10)])) @ Instruction Index 7 @ Method Index: 11
Inst(if_icmpgt, Some([Int(13)])) @ Instruction Index 9 @ Method Index: 11
Inst(iload, Some([Int(1)])) @ Instruction Index 12 @ Method Index: 11
Inst(iload, Some([Int(2)])) @ Instruction Index 13 @ Method Index: 11
Inst(iadd, None) @ Instruction Index 14 @ Method Index: 11
Inst(istore, Some([Int(1)])) @ Instruction Index 15 @ Method Index: 11
Inst(iinc, Some([Int(2), Int(1)])) @ Instruction Index 16 @ Method Index: 11
Inst(goto, Some([Int(-13)])) @ Instruction Index 19 @ Method Index: 11

Ideally in a tracing JIT you might want to replace the comparison instruction by speculatively executing under the assumption that the condition is true. This is done in many production tracing JITs were guard clauses are introduced to assert the condition.

In our case we don't do any of that we simply compile the code as is, the above bytecode results in the following assembly (comments added for clarification).

; epilogue
00000000  55                push rbp
00000001  4889E5            mov rbp,rsp
; this is not needed since we don't clobber rdi or rsi
00000004  48897DE8          mov [rbp-0x18],rdi
00000008  488975E0          mov [rbp-0x20],rsi
; loop entry
0000000C  488B842710000000  mov rax,[rdi+0x10]
00000014  4881F80A000000    cmp rax,0xa
; loop condition i <= 10
0000001B  0F8F29000000      jg near 0x4a
; loop code.
00000021  488B8C2708000000  mov rcx,[rdi+0x8]
00000029  4C8B842710000000  mov r8,[rdi+0x10]
00000031  4C01C1            add rcx,r8
00000034  48898C2708000000  mov [rdi+0x8],rcx
0000003C  4080842710000000  add byte [rdi+0x10],0x1
         -01
; go back to the loop entry (equivalent to goto)
00000045  E9C2FFFFFF        jmp 0xc
; 0xd is the program counter of the target instruction of if_icmpge above
; this is preloaded and known at compile time and we don't need to inject
; it.
0000004A  48C7C00D000000    mov rax,0xd
00000051  5D                pop rbp
00000052  C3                ret

The above is the relocation free version, emitted by dynasm-rs.

Note: Special thanks to dynasm-rs author for an exellent and pleasent to use dynamic assembler.

When it comes to executing the trace we assemble the native trace using dynasm and record it as a pointer to a function with the following signature.

Going Further

I might possibly keep working on this but if you would like a challenge here are some ideas :

  • Handle nested loops.
  • Inline invoked functions (currently we abort traces that do function calls but under certain heuristics we can pretty much compile simple functions)
  • Add an IR then compile and optimize the IR before compiling to assembly this offers you the opportunity for DCE, Algebraic Simplification, Constant Folding, Loop Unrolling (the list goes on really).
  • Rewrite the tracer to build tracelets instead (basic blocks) then do trace splatting with branch flipping to really speed up things.
  • Add support for trace stitching
  • Add ARM64 support

Acknowledgments

I would like to thank the authors of the TigerShrimp work and for providing their implementation. The thesis is an exellent introduction to Tracing JITs and is a must read to anyone who wishes to understand the overall architecture and details of tracing JIT interpreters.

Footnotes

  1. TigerShrimp: An Understandable Tracing JIT Compiler

  2. Higgs: A New Tracing JIT for JavaScript

  3. Github/TigerShrimp

  4. Archive: On LuaJIT 2.0

  5. Java SE7 Spec