Trace Optimizer

Traces of user programs are not directly translated into machine code. The optimizer module implements several different semantic preserving transformations that either allow operations to be swept from the trace or convert them to operations that need less time or space.

The optimizer is in rpython/jit/metainterp/optimizeopt/. When you try to make sense of this module, this page might get you started.

Before some optimizations are explained in more detail, it is essential to understand how traces look like. The optimizer comes with a test suit. It contains many trace examples and you might want to take a look at it (in rpython/jit/metainterp/optimizeopt/test/*.py). The allowed operations can be found in rpython/jit/metainterp/ Here is an example of a trace:

label(p0, i0, i1)
i2 = getarray_item_raw(p0, i0, descr=<Array Signed>)
i3 = int_add(i1,i2)
i4 = int_add(i0,1)
i5 = int_le(i4, 100) # lower-or-equal
jump(p0, i4, i3)

At the beginning it might be clumsy to read but it makes sense when you start to compare the Python code that constructed the trace:

from array import array
a = array('i',range(101))
sum = 0; i = 0
while i <= 100: # can be seen as label
    sum += a[i]
    i += 1
    # jumps back to the while header

There are better ways to compute the sum from [0..100], but it gives a better intuition on how traces are constructed than sum(range(101)). Note that the trace syntax is the one used in the test suite. It is also very similar to traces printed at runtime by PYPYLOG. The first line gives the input variables, the second line is a label operation, the last one is the backwards jump operation.

These instructions mentioned earlier are special:

  • the input defines the input parameter type and name to enter the trace.
  • label is the instruction a jump can target. Label instructions have a JitCellToken associated that uniquely identifies the label. Any jump has a target token of a label.

The token is saved in a so called descriptor of the instruction. It is not written explicitly because it is not done in the tests either. But the test suite creates a dummy token for each trace and adds it as descriptor to label and jump. Of course the optimizer does the same at runtime, but using real values. The sample trace includes a descriptor in getarrayitem_raw. Here it annotates the type of the array. It is a signed integer array.

High level overview

Before the JIT backend transforms any trace into machine code, it tries to transform the trace into an equivalent trace that executes faster. The method optimize_trace in rpython/jit/metainterp/optimizeopt/ is the main entry point.

Optimizations are applied in a sequence one after another and the base sequence is as follows:


Each of the colon-separated name has a class attached, inheriting from the Optimization class. The Optimizer class itself also derives from the Optimization class and implements the control logic for the optimization. Most of the optimizations only require a single forward pass. The trace is ‘propagated’ into each optimization using the method propagate_forward. Instruction by instruction, it flows from the first optimization to the last optimization. The method emit_operation is called for every operation that is passed to the next optimizer.

A frequently encountered pattern

To find potential optimization targets it is necessary to know the instruction type. Simple solution is to switch using the operation number (= type):

for op in operations:
    if op.getopnum() == rop.INT_ADD:
        # handle this instruction
    elif op.getopnum() == rop.INT_FLOOR_DIV:
    # and many more

Things get worse if you start to match the arguments (is argument one constant and two variable or vice versa?). The pattern to tackle this code bloat is to move it to a separate method using make_dispatcher_method. It associates methods with instruction types:

class OptX(Optimization):
    def prefix_INT_ADD(self, op):
        pass # emit, transform, ...

dispatch_opt = make_dispatcher_method(OptX, 'prefix_',
OptX.propagate_forward = dispatch_opt

optX = OptX()
for op in operations:

propagate_forward searches for the method that is able to handle the instruction type. As an example INT_ADD will invoke prefix_INT_ADD. If there is no function for the instruction, it is routed to the default implementation (emit_operation in this example).

Rewrite optimization

The second optimization is called ‘rewrite’ and is commonly also known as strength reduction. A simple example would be that an integer multiplied by 2 is equivalent to the bits shifted to the left once (e.g. x * 2 == x << 1). Not only strength reduction is done in this optimization but also boolean or arithmetic simplifications. Other examples would be: x & 0 == 0, x - 0 == x

Whenever such an operation is encountered (e.g. y = x & 0), no operation is emitted. Instead the variable y is made equal to 0 (= make_equal_to(op.result, 0)). The variables found in a trace are instances of Box classes that can be found in rpython/jit/metainterp/ OptValue wraps those variables again and maps the boxes to the optimization values in the optimizer. When a value is made equal, the two variable’s boxes are made to point to the same OptValue instance.

NOTE: this OptValue organization is currently being refactored in a branch.

Pure optimization

Is interwoven into the basic optimizer. It saves operations, results, arguments to be known to have pure semantics.

“Pure” here means the same as the jit.elidable decorator: free of “observable” side effects and referentially transparent (the operation can be replaced with its result without changing the program semantics). The operations marked as ALWAYS_PURE in are a subset of the NOSIDEEFFECT operations. Operations such as new, new array, getfield_(raw/gc) are marked as NOSIDEEFFECT but not as ALWAYS_PURE.

Pure operations are optimized in two different ways. If their arguments are constants, the operation is removed and the result is turned into a constant. If not, we can still use a memoization technique: if, later, we see the same operation on the same arguments again, we don’t need to recompute its result, but can simply reuse the previous operation’s result.

Unroll optimization

A detailed description can be found the document Loop-Aware Optimizations in PyPy’s Tracing JIT

This optimization does not fall into the traditional scheme of one forward pass only. In a nutshell it unrolls the trace _once_, connects the two traces (by inserting parameters into the jump and label of the peeled trace) and uses information to iron out allocations, propagate constants and do any other optimization currently present in the ‘optimizeopt’ module.

It is prepended to all optimizations and thus extends the Optimizer class and unrolls the loop once before it proceeds.


What is missing from this document

  • Guards are not explained
  • Several optimizations are not explained