JS Optimization

After profiling, I see that roughly 25-30% of my program's time is spent inside this function.

Unfortunately, this isn't my algorithm, and I don't think I can improve it. The paper it came from is here: http://www.dgp.toronto.edu/people/stam/reality/Research/pdf/GDC03.pdf

My only option seems to be "performance tricks", but I don't see any openings. The only thing I can say for sure that I haven't tried is ``loop unrolling'', but I don't have much experience doing that myself, and I don't see how I could do it here while staying in bounds. If it helps, height, width, and iterations are all set by the user.

So: any ideas on how to improve the performance of this code?

function lin_solve(b, x, x0, a, c) {
        if (a === 0 && c === 1) {
            for (var j = 1; j <= height; j++) {
                var currentRow = j * rowSize;
                ++currentRow;
        var i = width - 1; do {
                    x[currentRow] = x0[currentRow];
                    ++currentRow;
                } while (i--);
            }
            set_bnd(b, x);
        } else {
            var invC = 1 / c;
            for (var k = 0; k < iterations; k++) {
                for (var j = 1; j <= height; j++) {
                    var lastRow = (j - 1) * rowSize;
                    var currentRow = j * rowSize;
                    var nextRow = (j + 1) * rowSize;
                    var lastX = x[currentRow];
                    ++currentRow;
                   
            var i = width; do {
                        lastX = x[currentRow] = (x0[currentRow] + a*(lastX+x[++currentRow]+x[++lastRow]+x[++nextRow])) * invC;
            } while (i-- > 1)
                }
                set_bnd(b, x);
            }
        }
    }

>>16
This is what I would do, but frankly, I'm not that talented. This is "Babby's first fluid dynamics" for me.
You don't need to know what the algorithm is for or how it does what it does if you're making these sorts of "mechanical" optimizations; I know absolutely nothing about fluid dynamics nor the workings of this algorithm, but I can tell you to get rid of multiplies, flatten loops, and use fixed-point if you don't need the range of floats. I'm just analysing it from the perspective of "what operations are being performed and how can I make them go faster with equivalent results".

>>20
Get rid of those j * rowSize multiplies and replace them with addition.

0.5 *
Are x/x0 floating-point? Use integer/fixed-point if you don't need the extra range.