Below are some frequently asked questions.

## TC language¶

### How are temporary variables handled in TC?¶

Since TC doesn’t perform any allocations internally, every variable has to be either an input or output in the TC language. For example:

Invalid TC:

The following TC is Invalid because the variable expSum is not marked as either input or output:

def softmax(float(N, D) I) -> (O, maxVal, expDistance) {
maxVal(n) max=! I(n, d)
expDistance(n, d) = exp(I(n, d) - maxVal(n))
expSum(n) +=! expDistance(n, d)
O(n, d) = expDistance(n, d) / expSum(n)
}


Valid TC

The correct TC would be:

def softmax(float(N, D) I) -> (O, maxVal, expDistance, expSum) {
maxVal(n) max=! I(n, d)
expDistance(n, d) = exp(I(n, d) - maxVal(n))
expSum(n) +=! expDistance(n, d)
O(n, d) = expDistance(n, d) / expSum(n)
}


### Can I re-use a temporary variable?¶

You can as long as the tensor dependencies form a DAG. For example:

Invalid

def softmax(float(N, D) I) -> (O, tmp) {
tmp(n) max=! I(n, d)
O(n, d) = exp(I(n, d) - tmp(n))
tmp(n) +=! O(n, d)
O(n, d) = O(n, d) / tmp(n)
}


This TC is invalid because tmp and O(n, d) have a cyclic dependency.

Valid

def softmax(float(N, D) I) -> (O, expsum, maxVal) {
maxVal(n) max=! I(n, d)
expsum(n) +=! exp(I(n, d) - maxVal(n))
O(n, d) = exp(I(n, d) - maxVal(n)) / expsum(n)
}


## Autotuner¶

### At the start of a new generation, I see higher kernel runtimes, Why?¶

This is expected behavior. When a new generation starts, the best runtime may bump to e.g. 600us when e.g. 168us was found as the best time from the previous generation. This is expected because the autotuner is multithreaded and we don’t enforce a strict order of evaluation: the best configurations from the previous generation may not be evaluated first in next generation. Furthermore, the other mutations in the current generation may perform worse than the last best known configuration. Therefore the initial jump in best runtime at generation (i+1) is likely to appear temporarily.

### I sometimes see fluctuations in the best kernel time, why?¶

The best time reported is the median of the best candidate runtime and the GPU runtime may be noisy. So a 10-20% variation is expected and normal.

### How do I stop autotuning early and save cache?¶

You can send a SIGINT signal (i.e. hit Ctrl+C) to stop the autotuning early. All compilations and evaluations in progress will be completed, but no new compilation or evaluation will be started. Therefore, stopping the autotuner may take some time.