  Interprocessor communication often dominates the runtime of large matrix
computations. We present a parallel algorithm for computing QR decompositions
whose bandwidth cost (communication volume) can be decreased at the cost of
increasing its latency cost (number of messages). By varying a parameter to
navigate the bandwidth/latency tradeoff, we can tune this algorithm for
machines with different communication costs.
