After reading the title above, did you think I would be talking about your co-workers?
Dependency analysis is a big part of the Prism parallel migration flow. Data dependencies are characterized as true data (read-after-write), anti (write-after-read) and output (write-after-write) dependencies.
Dependency relationships between threads limit the amount of parallelism that can be achieved. They must either be removed or properly synchronized. Improperly handled dependencies show up as data races during execution.
Anti-Dependencies
Anti-dependencies are a common artifact of optimized sequential code when data storage is reused to save space. Loops are a common source of these anti-dependencies. The good news is that, once identified, these dependencies can be removed by duplicating or localizing variables, often making the loops embarrassingly parallel. Consider this made up sequential code block:
int i;
x_t x;
y_t y[N];
for (i = 0; i < N; ++i) {
x = f(i);
y[i] = g(i, x);
}
The x variable is shared by all iterations. In sequential code, this works fine because the iterations occur one after another and do not overlap. Unfortunately, the shared x variable causes problems when trying to run iterations in parallel. There is an anti-dependency between iterations where one iteration cannot write a new x value until after another iteration has read the existing x value.
This is easy to fix. If the x declaration is moved inside each iteration, each iteration maintains its own local x variable:
int i;
y_t y[N];
for (i = 0; i < N; ++i) {
x_t x;
x = f(i);
y[i] = g(i, x);
}
In this case, there are no more dependencies between iterations, and the iterations can be run in parallel with each other. This code is now easy to parallelize using any typical parallel API, such as Pthreads or OpenMP.
True Data Dependencies
True data dependencies are usually fundamental to the algorithm being implemented. A value cannot be used (read) until after it has been computed (written). These dependencies are usually impossible to remove without changing algorithms.
Despite this, their impact can be minimized. Consider this made up sequential code block:
int i;
x_t x[N];
y_t y;
y = y_init();
for (i = 0; i < N; ++i) {
x[i] = f(i);
y = g(y, x[i]);
}
To run properly, the y values must computed in order. (Finicky readers will note the implicit assumption that the g(a, b) function is not associative.) The next iteration’s initial y value cannot be read until the previous iteration has written it. This is a true data dependency.
Though it cannot be removed, the dependency can be isolated. Consider this code refactoring:
int i;
x_t x[N];
y_t y;
for (i = 0; i < N; ++i) {
x[i] = f(i);
}
y = y_init();
for (i = 0; i < N; ++i) {
y = g(y, x[i]);
}
Here the x and y computations have been separated. Though the y values must still be computed serially, the x iterations can all be run in parallel using common parallel APIs.
If the f(i) function is expensive, this separation pays dividends. For example, assume the upper loop takes 98% of the run time and the second iteration only 2%. Using Amdahl’s law, the speed up for 7 cores is 100/(98/7 +2) = 6.25x.
So, when migrating optimized sequential code to parallel, I usually start with the low hanging fruit first- refactor the code to remove anti-dependencies. If lucky, this will make critical loops easy to parallelize. For remaining loops with true data dependencies, consider splitting the loops to isolate the code which must remain serial.
And do try to be respectful to your co-workers; you never know…
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