Each iteration of minimum error rate training involves re-translating a development set.

Distributing this work across computational nodes can speed up translation time, 
but in practice some parts may take much longer to complete than others, 
leading to computational slack time. 

To address this problem, we develop three novel algorithms 
for distributing translation tasks in a parallel computing environment, 
drawing on research in parallel machine scheduling. 

We present results showing a substantial speedup in overall decoding time.