Sunday, November 25, 2012

Generating interpreters and JIT compilers

Based on a description of each register in a (virtual) machine and each instruction, shouldn't it be possible to generate an interpreter for such machine? And if it's possible to generate a interpreter, shouldn't it be possible to generate a JIT compiler as well? Additionally, if a interpreter can be generated, shouldn't it be possible to generate a "inference engine" that infers various static properties of a program, e.g., that c = a + b is 1 if a = 1 and b = 0?

Of course it is possible -- the question, however, is how such description should look. How convenient is it to write such description? Let's start with the description of the machines registers.

Assuming a simple machine with non-overlapping registers (i.e., no al/ah/ax/eax/rax mess) the register description is fairly straight forward, for instance:
    rf: int64[64]
    zflag: bit
which say that there is a register file called rf consisting of 64 64bit registers and a flag, zflag, indicating whether or not the result of the last arithmetic operation was zero.

Although the above is a description of the machine's register it's a state-less description, in the sense that it purely declarative description of structure -- there is no mutation described here. Thus, this description is denoted the structural semantic.

On top of the structure of the machine are the instructions, which mutate the registers. These are described by the operational semantics. The following describes the load instruction, which writes an immediate value to the register file:
    def load(imm: int64, dst:int6):
        rf[dst] = imm
where imm and dst are the operands to the instruction -- an immediate and the (index of the) destination register, respectively. Furthermore, rf is the register file as described by the structural semantics above.

To  give some credibility to my first claim, that it is possible to generate an interpreter from this description, note that the semantic description formulated as above can trivially be used as a interpreter (simple text transform is enough) as the following python program indicates:
    rf = [0] * 64
    zflag = 0

    def load(imm, dst):
        def execute():
            rf[dst] = imm
        return execute

    program = [load(1000, 0), load(200, 1)]
    for instr in program:

    print(rf[0], rf[1])
Furthermore, translating the description into a more efficient C implementation is simply a matter of more trivialities and a tea spoon of relatively straight-forward type inference. But such details are not important for the argument I'm making.

Now, what about generating a JIT compiler for the load instruction? Let's start by looking at the end result, that is, how can such a JIT compiler be implemented. Here's a sample of an implementation of the load instruction implemented in C++:
    class load {
        int64_t imm; 
        unsigned dst;
            load(int64_t imm, unsigned dst) : imm(imm), dst(dst) { }
            void emit(Xbyak::CodeGenerator& cg) {
      , imm);
      , dst);
      [rdi + offsetof(State, rf) + gc.r9 * 8],
where we used the great Xbyak framework for translating x64 assembler into binary encoded instructions. This class can be used to emit the load instruction at runtime, that is, we have a working JIT compiler -- all generated from the four line long description above!

I'm guessing that you by now sayt show me the code. Sure, here is a proof-of-concept implementation. It is called jiggawatt and it can, as of the writing of this post, generate interpreters and jit compilers that support load instructions, add and  sub instructions, non-conditional jumps, and conditional jumps. So far it does not generate a optimizing jit compiler, however that is (one of) the end goal(s).

But so what? Why would you ever need a interpreter and jit compiler generator? Well, as a back-end for DSLs as well as full-fledge programming languages is one application that comes to mind. Emulators of physical processors or other kind of hardware is another. A third application is for prototyping instruction sets and virtual machines.

Sunday, November 4, 2012

Stringify and sequence checks in Protest

Protest is the C++ unit test framework that I've been working on for a month or two that I've written about here before. Protest improves on other frameworks by having a single über-powerful check/assert and handles fixtures really well. But since a yesterday it has become even better, as this post will describe. Let's start with the a simple yet important feature of any testing framework -- printing objects.


All C++ unit test frameworks I've used suffer from the same illness -- the sorry-I-can't-stringify-anything flu. The prime example of this is std::vector, which has operator== overloaded, but no operator<<. This implies that std::vector can't be used in as arguments to, for instance CHECK_EQUAL in UnitTest++, because that macro requires the arguments to have operator<< implemented.

Protest solves this with two important features: 1) it can stringify std::*, and 2) a object without operator<< is simply not stringified. One issue remains though: what if operator<< is implemented but it needs to be printed differently when printed from a test? Well, of course, Protest to the rescue!

Protest doesn't hijack operator<<, it does however use it by default to print objects. This means that a object can be printed differently from tests and in production code. This is not yet documented on the wiki, but soon it will be. For the time being this example has to suffice (note however, that this code has to come before #include <protest.hh>):
struct Foo { };
void stringify(std::ostream& os, Foo const&) {
  os << "Foo";
#include <protest.hh>

Sequence checks

A key feature of Protest is that is only has one check/assert macro, while other frameworks either have five, ten, or even twenty; or they follow the Hamcrest route and forces you to write assert such as
ASSERT_THAT(v, Each(AllOf(Gt(10), Lt(20))));
which honestly isn't particularly easy to read, nor write. Furthermore the many-checks approach and the hamcrest-approach both fail in more complicated situations. Of course, Protest tries to remedy this, and the solution is sequence checks.

Sequence checks are checks that uses one or more sequence variable, which is essentially equivalent to a for loop. The following Protest check is equivalent to the assert in the example above and I is a sequence variable:
I(0, 3); // check elements 0, 1, and 2.
check(v[I] > 5);
check(v[I] < 20);
which obviously is more lines of code, but roughly the same number of characters. The neat thing with sequence checks is that it handles everything from simple array comparison to checking relationships between functions, e.g.,
I(0, 20, 2);
check(0 == least_significant_bit(I)); // Even numbers
check(1 == least_significant_bit(I + 1)); // Odd numbers

Sequence checks improve the quality of the your tests by enabling you to express invariants of the code you're testing without increasing the amount of test-code needed.


Protest ability to stringify all objects (with or without operator<<) avoids a annoying mis-feature of many other test frameworks. This feature does not, however, change how you write tests. As as step towards writing tests that express the logic of the production code Protest provides sequence checks.