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# IR - Lightweight JIT Compilation Framework
The main task of this framework is transformation of the IR (Intermediate
Representation) into an optimized in-memory target machine code, that may
be directly executed.
This is not a stable finished product yet. It’
of the next generation JIT compiler for PHP-9. There are a lot of required
things are not implemented yet.
## IR - Intermediate Representation
The Framework uses single Medium level Intermediate Representation during all
phases of optimization, register allocation and code generation. It is inspired
by Sea-Of-Nodes introduced by Cliff Click [1]. Sea-Of-Nodes is used in Java
HotSpot Server Compiler, V8 TurboFan JavaScript Compiler, Java Graal
Compiler...
This representation unifies data and control dependencies into a single graph,
where each instruction represented as a Node and each dependency as an Edge
between Nodes. There are no classical CFG (Control Flow Graph) with Basic
Blocks. Instead, IR uses special Control Nodes that start and finish some
code Regions. The data part of the IR is very similar to SSA (Static Single
Assignment) form. Each variable may be assigned only once, but except to SSA,
in our IR, we don’
is represented by computation Nodes and Edges between them. Of course, we
have special PHI Node that represents the Phi() function.
Despite, our IR is graph based, internally, it’
two-way grow-able array of Nodes. Dependency Edge are represented as indexes
of the other Node. This physical representation is almost completely repeats
the LuaJIT IR designed by Mike Pall [3].
## IR Generation
TODO...
## IR Optimization
In comparison to classical optimizing compilers (like GCC and LLVM), IR
Framework uses very short optimization pipeline. Together with compact IR
representation, this makes it extremely fast and allows to generate quite
good machine code in reasonable time.
### Folding
Folding is done on the fly, during IR generation. It performs a set of local
transformations, but because of the graph nature of the IR where most data
operations (like ADD) are “floating” (not “pinned” to Basic Block), the scope
of transformations is not limited by Basic Block. It’
IR in a proper (Reverse Post) order, that would emit all Nodes before their
first usage. (In case of different order the scope of the folding should be
limited).
Folding Engine performs Constants Folding, Copy Propagation, Algebraic
Simplifications, Algebraic Re-Association and Common Sub-Expression
Elimination. The simple and fast declarative implementation is borrowed from
LuaJIT [3].
### Sparse Conditional Constant Propagation
This pass implements a classical algorithm originally designed by M. N. Wegman
and F. K. Zadeck [4] for SSA form. Unification of data and control dependencies
made its implementation even simple. Despite of constant propagation itself
this pass also performs global Copy Propagation and re-applies the folding rules.
At the end all the “dead” instructions (instructions that result are not used)
are replaced with NOPs.
### Global Code Motion
Now we have to “fix” places of “floating” instructions. This pass builds CFG
(Control Flow Graph) skeleton and then ”pin” each “floating” instruction to the
best Basic Block. The algorithm is developed by Cliff Click [2].
## Local Scheduling
As the final IR transformation pass, we reorder instructions inside each Basic
Block to satisfy the dependencies. Currently this is done by a simple
topological sorting.
## Target Instruction Selection
This is the first target dependent step of compilation. It aims to combine
instruction Nodes into tiles that allows better instruction fusion. For example
``10 + a + b * 4`` may be calculated by a single x86 instruction
``lea 10(%eax, %ebx, 4), %ecx``. The selection is done by a constrained tree
pattern matching. The current implementation uses simple Max-Munch approach.
(This may be replaced by a smarter BURS method).
## Register Allocation
CPU independent implementation of Linear Scan Register Allocation for SSA form
with second chance bin-packing. [5] [6]
## Machine Code Generations
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IR Framework implements X86_64, x86 and AArch64 back-ends. The current
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implementation uses DynAsm [?]. (In the future, this should be replaced with
a faster “binary” encoder). Code generator walks throw all instructions of each
basic blocks and emits some code according to “rules” selected during
instruction selection pass. It uses registers, selected by register allocator
and inserts the necessary spill load/store and SSA deconstruction code.
## Tooling
- Ability to load and save IR in a textual form
- Ability to visualize IR graph through graphviz dot.
- Target CPU disassembler for generated code (uses libcapstone [?])
- GDB/JIT interface to allow debugging of JIT-ed code
- Linux perf interface to analyze the code performance
## IR Example
Let's try to generate code for the following function:
```c
int32_t mandelbrot(double x, double y)
{
double cr = y - 0.5;
double ci = x;
double zi = 0.0;
double zr = 0.0;
int i = 0;
while(1) {
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i++;
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double temp = zr * zi;
double zr2 = zr * zr;
double zi2 = zi * zi;
zr = zr2 - zi2 + cr;
zi = temp + temp + ci;
if (zi2 + zr2 > 16)
return i;
if (i > 1000)
return 0;
}
}
```
This may be done through IR construction API by the following code:
```c
void gen_mandelbrot(ir_ctx *ctx)
{
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ir_START();
ir_ref x = ir_PARAM(IR_DOUBLE, "x", 1);
ir_ref y = ir_PARAM(IR_DOUBLE, "y", 2);
ir_ref cr = ir_SUB_D(y, ir_CONST_DOUBLE(0.5));
ir_ref ci = ir_COPY_D(x);
ir_ref zi = ir_COPY_D(ir_CONST_DOUBLE(0.0));
ir_ref zr = ir_COPY_D(ir_CONST_DOUBLE(0.0));
ir_ref i = ir_COPY_D(ir_CONST_I32(0));
ir_ref loop = ir_LOOP_BEGIN(ir_END());
ir_ref zi_1 = ir_PHI_2(zi, IR_UNUSED);
ir_ref zr_1 = ir_PHI_2(zr, IR_UNUSED);
ir_ref i_1 = ir_PHI_2(i, IR_UNUSED);
ir_ref i_2 = ir_ADD_I32(i_1, ir_CONST_I32(1));
ir_ref temp = ir_MUL_D(zr_1, zi_1);
ir_ref zr2 = ir_MUL_D(zr_1, zr_1);
ir_ref zi2 = ir_MUL_D(zi_1, zi_1);
ir_ref zr_2 = ir_ADD_D(ir_SUB_D(zr2, zi2), cr);
ir_ref zi_2 = ir_ADD_D(ir_ADD_D(temp, temp), ci);
ir_ref if_1 = ir_IF(ir_GT(ir_ADD_D(zi2, zr2), ir_CONST_DOUBLE(16.0)));
ir_IF_TRUE(if_1);
ir_RETURN(i_2);
ir_IF_FALSE(if_1);
ir_ref if_2 = ir_IF(ir_GT(i_2, ir_CONST_I32(1000)));
ir_IF_TRUE(if_2);
ir_RETURN(ir_CONST_I32(0));
ir_IF_FALSE(if_2);
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ir_ref loop_end = ir_LOOP_END();
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/* close loop */
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ir_MERGE_SET_OP(loop, 2, loop_end);
ir_PHI_SET_OP(zi_1, 2, zi_2);
ir_PHI_SET_OP(zr_1, 2, zr_2);
ir_PHI_SET_OP(i_1, 2, i_2);
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}
```
The textual representation of the IR after system independent optimizations:
```
{
uintptr_t c_1 = 0;
bool c_2 = 0;
bool c_3 = 1;
double c_4 = 0.5;
double c_5 = 0;
int32_t c_6 = 0;
int32_t c_7 = 1;
double c_8 = 16;
int32_t c_9 = 1000;
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l_1 = START(l_22);
double d_2 = PARAM(l_1, "x", 1);
double d_3 = PARAM(l_1, "y", 2);
double d_4 = SUB(d_3, c_4);
l_5 = END(l_1);
l_6 = LOOP_BEGIN(l_5, l_29);
double d_7 = PHI(l_6, c_5, d_28);
double d_8 = PHI(l_6, c_5, d_26);
int32_t d_9 = PHI(l_6, c_6, d_10);
int32_t d_10 = ADD(d_9, c_7);
double d_11 = MUL(d_8, d_8);
double d_12 = MUL(d_7, d_7);
double d_13 = ADD(d_12, d_11);
bool d_14 = GT(d_13, c_8);
l_15 = IF(l_6, d_14);
l_16 = IF_TRUE(l_15);
l_17 = RETURN(l_16, d_10);
l_18 = IF_FALSE(l_15);
bool d_19 = GT(d_10, c_9);
l_20 = IF(l_18, d_19);
l_21 = IF_TRUE(l_20);
l_22 = RETURN(l_21, c_6, l_17);
l_23 = IF_FALSE(l_20);
double d_24 = MUL(d_7, d_8);
double d_25 = SUB(d_11, d_12);
double d_26 = ADD(d_25, d_4);
double d_27 = ADD(d_24, d_24);
double d_28 = ADD(d_27, d_2);
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l_29 = LOOP_END(l_23);
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}
```
The visualized graph:
The final generated code:
```asm
test:
subsd .L4(%rip), %xmm1
xorpd %xmm3, %xmm3
xorpd %xmm2, %xmm2
xorl %eax, %eax
.L1:
leal 1(%rax), %eax
movapd %xmm2, %xmm4
mulsd %xmm2, %xmm4
movapd %xmm3, %xmm5
mulsd %xmm3, %xmm5
movapd %xmm5, %xmm6
addsd %xmm4, %xmm6
ucomisd .L5(%rip), %xmm6
ja .L2
cmpl $0x3e8, %eax
jg .L3
mulsd %xmm2, %xmm3
subsd %xmm5, %xmm4
movapd %xmm4, %xmm2
addsd %xmm1, %xmm2
addsd %xmm3, %xmm3
addsd %xmm0, %xmm3
jmp .L1
.L2:
retq
.L3:
xorl %eax, %eax
retq
.rodata
.L4:
.db 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0xe0, 0x3f
.L5:
.db 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x30, 0x40
```
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## PHP JIT based on IR
A new experimental JIT for PHP based on IR is developed at [php-ir ](https://github.com/dstogov/php-src/tree/php-ir/ext/opcache/jit ) branch.
See [README-IR.md ](https://github.com/dstogov/php-src/blob/php-ir/ext/opcache/jit/README-IR.md ).
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## References
1. C. Click, M. Paleczny. “A Simple Graph-Based Intermediate Representation” In ACM SIGPLAN Workshop on Intermediate Representations (IR '95), pages 35-49, Jan. 1995.
2. C. Click. “Global Code Motion Global Value Numbering” In ACM SIGPLAN Notices, Volume 30, Issue 6, pp 246–
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3. M. Pall. “LuaJIT 2.0 intellectual property disclosure and research opportunities” November 2009 http://lua-users.org/lists/lua-l/2009-11/msg00089.html
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4. M. N. Wegman and F. K. Zadeck. "Constant propagation with conditional branches" ACM Transactions on Programming Languages and Systems, 13(2):181-210, April 1991
5. C. Wimmer. “Optimized Interval Splitting in a Linear Scan Register Allocator” In VEE '05: Proceedings of the 1st ACM/USENIX international conference on Virtual execution environments, pages 132–
6. C. Wimmer and M. Franz. “Linear Scan Register Allocation on SSA Form” In CGO '10: Proceedings of the 8-th annual IEEE/ACM international symposium on Code generation and optimization, pages 170–
