The CS 6120 Course Blog

C Implementation of Bril

by Bhargava S. Manja


While Bril is a very useful testbed for exploring existing language technologies and experimenting with new ideas, I was frustrated with the tooling around the language. It took me three or four hours of fiddling around with Node, npm, and Python to get brili working, and I did not get either bril2json or bril2txt to work on my machine (I reimplemented them myself in Python). I've rarely had such issues with my trusted systems programming language, C. I decided to implement a simple, fast, and correct interpreter for Bril. I call it cril.

All code can be found in the project repository.


The goal here is simplicity and speed (in comparison to other student interpreters). The only external library used was for parsing JSON. The interpreter's evaluation loop, in src/interp.c, first loops through the parsed Bril program and notes the indices of labels. This was the simplest way to implement jumps and branches, which become a simple setting of the instruction pointer to the label's index (or an error if the label is not found in the label->index map). Actual instructions are implemented with a set of functions, one for each op code. Once the op is known, the interpreter calls one of these functions, which fetches arguments, does the required manipulation, and stores its result in the right place (or in the case of effect operations, has the correct effect).

Op Code Implementation

All instructions but jmp, br, print, and const have a trivial implementation. A function called get_or_quit fetches arguments from the source program and stores their values in a global array, sized to the max of the argument count of all non-print instructions (2). If the variable is not found in storage, an error is reported (containing the specific issue, incorrect variable, and instruction pointer value) and the program quits. Otherwise, the op implementing function calls put with the argument that implements the specific op code, which stores the result in the destination variable. For example, the add op code is implemented as follows:

static void op_add() {

Above, mem_args is the name of the global argument array. Thus, most op codes have 2 line implementations. Even br, the most complex op code, requires only 16 lines to implement.

Memory Implentation (and More, for Free)

I needed a hash table implementation to implement variable storage, so I built it myself. It can be found in src/table.{c,h}. The dictionary uses open addressing and linear chaining. The hash function copies the hash used by java.lang.String’s hashCode() method. The _hash function computes a polynomial whose coeffecients are the integer values of the string's characters, evaluated at x=31. The polynomial is evaluated with Horner's method. The table maps string keys to int64_t values. I represent bril integers and bril booleans with int64_t to avoid storing the type of bril variables. Since bril's typesystem only allows for those two types, this design is sufficient for now. Incorporating more types will be simple: I can extend the table_elem struct with a type bitfield/enum value. For now, this simple table suffices.

The table code is also used for the label->index map and to store the mapping between string op codes and the index for that op code's implementing function in an array of function pointers.



I ran cril against the programs in bril/test and made sure the outputs matched <program>.out. My interpreter gave correct results on the Fibonacci program in benchmark/fibonacci.json, while the brili reference interpreter gave wrong results on the last 3 outputted Fibonacci numbers due to rounding issues with Javascript's BigInt type.



I collected relatively compute heavy Bril programs under benchmark in the cril repository. I took these programs from Wen-Ding Li's Bril benchmark repository. I used his Fibonacci and factorial implementation verbatim, and used his matrix multiplication and polynomial multiplication programs to generate Bril programs with options n=1 to n=5 (the size of the respective matrices and polynomials).

Measurement and Comparison

I wanted to have a way to get reliable performance numbers to square off against any other students' implementation of bril. First, I had the main evaluation loop return a uint64_t number of nanoseconds of elapsed time. I used the POSIX provided timespec structure to record the time tracked by CLOCK_PROCESS_CPUTIME_ID, the nanosecond resolution process time clock. This tracks CPU ticks spent on the program process itself, irrespective of other scheduled processes. This data is procured with the C standard library's clock_gettime. Some notes:

Then, in src/main.c, I have a constant named NUM_RUNS, and if cril is called with --benchmark, it will run each program under benchmark NUM_RUNS times, calculate a mean and standard deviation per program, and output the results. I modified the reference brili source code to perform the same measurements on the same benchmark programs. Times reported are in milliseconds, displayed as means plus or minus standard deviations.

Fibonacci.099 ± .03.44 ± .23
Factorial.019 ± .005.042 ± .073
MatMul 1.005 ± .002.004 ± .001
MatMul 2.014 ± .004.022 ± .025
MatMul 3.037 ± .008.041 ± .027
MatMul 4.08 ± .012.075 ± .05
MatMul 5.017 ± .023.136 ± .07
PolyMul 1.005 ± .002.006 ± .002
PolyMul 2.022 ± .014.011 ± .004
PolyMul 3.018 ± .003.019 ± .005
PolyMul 4.028 ± .006.027 ± .015
PolyMul 5.04 ± .008.04 ± .027