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\lhead{HW Chap. 4\\\ \\\ }
\rhead{Josh Holtrop\\2008-12-03\\CS 677}
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\noindent
\begin{enumerate}
\item[1.]{
    In store-and-forward communication, the communication cost is given by
    $$T_{\mathrm{comm}} = t_s + (t_h + mt_w)\ell = t_s + \ell t_h + \ell mt_w$$
    In cut-through routing, the communication cost is given by
    $$T_{\mathrm{comm}} = t_s + \ell t_h + mt_w$$
    Since the ``header'' is the only part of the communication that is
    encountering overhead for the $\ell$ links in the communication network,
    cut-through routing can save communication time on the order of
    $(\ell - 1) mt_w$.
    Obviously, this makes cut-through routing only advantageous on
    architectures with $\ell > 1$, meaning non-fully-connected networks.
}

\vskip 1em
\item[2.]{
    We are to transpose an $n \times n$ matrix that is initially
    rowwise block-decomposed among $p$ processes.
    Originally I interpreted the problem description to mean that after
    the matrix was transposed, then process $i$ would be responsible
    for the same column numbers that it originally had rows for.
    But, I realized this would not make much sense since then no communication
    would be needed at all - the process could simply use the same data
    that it had in its rows as its column data and transpose its own data.
    So, instead I interpreted the problem to mean that a given process $i$
    owns certain rows of the matrix before the transposition.
    After the transposition, it still owns the same row indices but
    these rows should contain the transposed matrix data.

    I assume that $p \leq n$ and if any process $i$ owns $k > 1$ rows of
    the matrix, then the $k$ rows that it owns are contiguous.
    I also assume that a gather operation can be done in $\log p$
    communication steps.

    Then, I give a procedure to transpose the matrix is as follows:
    For each row $r \in \{0, 1, \ldots, n-1\}$,
        a gather operation is performed.
    Let $i$ be the process that owns row $r$.
    In this gather operation, each process $j$ sends to $i$ the $k$
        values it owns from column $r$ (where $k$ is the number of rows
        assigned to process $j$).
    Thus, at the end of each gather operation process $i$ has received
        the entire contents of column $r$ of the matrix
        (and has stored them now in row $r$ of a receive matrix in
         order to preform the transposition).
    This algorithm takes $n \log p$ communication steps to complete.
    
    This algorithm could be tweaked to have each process accumulate
    the $k$ values of column $r$ that it owns \textbf{for each}
    of the $\ell$ columns that the receiving process will require
    from it and transmit these all to the receiving process in a single
    gather.
    This would mean there was one gather per process instead of per
    row of the matrix, which would reduce the number of communication
    steps to $p \log p$.
    I have implemented in MPI my first algorithm which does a gather
    for each row in the transposed matrix.
}


\end{enumerate}

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