There are already a number of Python implementations using JIT techniques, often in sophisticated ways. PyPy has achieved impressive performance with its tracing JIT; Jython and IronPython are both built on top of mature VMs with extensive JIT support. So why do we think it’s worth starting a new implementation?
In short, it’s because we think the most promising techniques are incompatible with existing implementations. For instance, the JavaScript world has switched from tracing JITs to method-at-a-time JITs, due to the compelling performance benefits. Whether or not the same performance advantage holds for Python is an open question, but since the two approaches are fundamentally incompatible, the only way to start answering the question is to build a new method-at-a-time JIT.
Another point of differentiation is the planned use of a conservative garbage collector to support extension modules efficiently. Again, we won’t know until later whether this is a better approach or not, but it’s a decision that’s integral enough to a JIT that it is difficult to test in an existing implementation.
The downside of starting from scratch is, unsurprisingly, that creating a new language implementation is an enormous task. Luckily, tools are starting to come out that can help with this process; in particular, Pyston is built on top of LLVM, which lets us achieve top-tier code generation quality without having to deal with the details ourselves. Nonetheless, a new Python implementation is a huge undertaking, and Pyston will not be ready for use soon.
Why a new implementation
There are already a number of Python implementations using JIT techniques, often in sophisticated ways. PyPy has achieved impressive performance with its tracing JIT; Jython and IronPython are both built on top of mature VMs with extensive JIT support. So why do we think it’s worth starting a new implementation?
In short, it’s because we think the most promising techniques are incompatible with existing implementations. For instance, the JavaScript world has switched from tracing JITs to method-at-a-time JITs, due to the compelling performance benefits. Whether or not the same performance advantage holds for Python is an open question, but since the two approaches are fundamentally incompatible, the only way to start answering the question is to build a new method-at-a-time JIT.
Another point of differentiation is the planned use of a conservative garbage collector to support extension modules efficiently. Again, we won’t know until later whether this is a better approach or not, but it’s a decision that’s integral enough to a JIT that it is difficult to test in an existing implementation.
The downside of starting from scratch is, unsurprisingly, that creating a new language implementation is an enormous task. Luckily, tools are starting to come out that can help with this process; in particular, Pyston is built on top of LLVM, which lets us achieve top-tier code generation quality without having to deal with the details ourselves. Nonetheless, a new Python implementation is a huge undertaking, and Pyston will not be ready for use soon.