17,456 research outputs found
GLB: Lifeline-based Global Load Balancing library in X10
We present GLB, a programming model and an associated implementation that can
handle a wide range of irregular paral- lel programming problems running over
large-scale distributed systems. GLB is applicable both to problems that are
easily load-balanced via static scheduling and to problems that are hard to
statically load balance. GLB hides the intricate syn- chronizations (e.g.,
inter-node communication, initialization and startup, load balancing,
termination and result collection) from the users. GLB internally uses a
version of the lifeline graph based work-stealing algorithm proposed by
Saraswat et al. Users of GLB are simply required to write several pieces of
sequential code that comply with the GLB interface. GLB then schedules and
orchestrates the parallel execution of the code correctly and efficiently at
scale. We have applied GLB to two representative benchmarks: Betweenness
Centrality (BC) and Unbalanced Tree Search (UTS). Among them, BC can be
statically load-balanced whereas UTS cannot. In either case, GLB scales well--
achieving nearly linear speedup on different computer architectures (Power,
Blue Gene/Q, and K) -- up to 16K cores
Improving the scalability of parallel N-body applications with an event driven constraint based execution model
The scalability and efficiency of graph applications are significantly
constrained by conventional systems and their supporting programming models.
Technology trends like multicore, manycore, and heterogeneous system
architectures are introducing further challenges and possibilities for emerging
application domains such as graph applications. This paper explores the space
of effective parallel execution of ephemeral graphs that are dynamically
generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The
workloads are expressed using the semantics of an Exascale computing execution
model called ParalleX. For comparison, results using conventional execution
model semantics are also presented. We find improved load balancing during
runtime and automatic parallelism discovery improving efficiency using the
advanced semantics for Exascale computing.Comment: 11 figure
PT-Scotch: A tool for efficient parallel graph ordering
The parallel ordering of large graphs is a difficult problem, because on the
one hand minimum degree algorithms do not parallelize well, and on the other
hand the obtainment of high quality orderings with the nested dissection
algorithm requires efficient graph bipartitioning heuristics, the best
sequential implementations of which are also hard to parallelize. This paper
presents a set of algorithms, implemented in the PT-Scotch software package,
which allows one to order large graphs in parallel, yielding orderings the
quality of which is only slightly worse than the one of state-of-the-art
sequential algorithms. Our implementation uses the classical nested dissection
approach but relies on several novel features to solve the parallel graph
bipartitioning problem. Thanks to these improvements, PT-Scotch produces
consistently better orderings than ParMeTiS on large numbers of processors
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