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Abstract:
In this paper, we present a randomized, online, space-efficient
algorithm for the general class of programs with synchronization
variables (such programs are produced by parallel programming
languages, like, e.g., Cool, ID, Sisal, Mul-T, OLDEN and Jade).
The algorithm achieves good locality and low scheduling overheads
for this general class of computations, by combining work-stealing
and depth-first scheduling.
More specifically, given a computation with work $T_1$,
depth $T_\infty$ and $\sigma$ synchronizations that its
execution requires space $S_1$ on a single-processor
computer, our algorithm achieves expected space
complexity at most $S_1 + O(PT_\infty \log (PT_\infty))$
and runs in an expected number of
$O(T_1/P + \sigma \log (PT_\infty)/P + T_\infty \log (PT_\infty))$
timesteps on a shared-memory, parallel machine with $P$ processors.
Moreover, for any $\varepsilon > 0$, the space complexity of our
algorithm is at most $S_1 + O(P(T_\infty + \ln (1/\varepsilon))
\log (P(T_\infty + \ln(P(T_\infty + \ln (1/\varepsilon))/\varepsilon))))$
with probability at least $1-\varepsilon$. Thus, even for values
of $\varepsilon$ as small as $e^{-T_\infty}$, the space complexity
of our algorithm is at most $S_1 + O(PT_\infty \log(PT_\infty))$,
with probability at least $1-e^{-T_\infty}$. The algorithm achieves
good locality and low scheduling overheads by automatically
increasing the granularity of the work scheduled on each
processor.
Our results combine and extend previous algorithms and
analysis techniques (published by Blelloch et. al [6]
and by Narlikar [26]). Our algorithm not only exhibits the
same good space complexity for the general class of programs
with synchronization variables as its deterministic analog
presented in [6], but it also achieves good locality and
low scheduling overhead as the algorithm presented in [26],
which however performs well only for the more restricted class
of nested parallel computations.
