135,042 research outputs found
A Bulk-Parallel Priority Queue in External Memory with STXXL
We propose the design and an implementation of a bulk-parallel external
memory priority queue to take advantage of both shared-memory parallelism and
high external memory transfer speeds to parallel disks. To achieve higher
performance by decoupling item insertions and extractions, we offer two
parallelization interfaces: one using "bulk" sequences, the other by defining
"limit" items. In the design, we discuss how to parallelize insertions using
multiple heaps, and how to calculate a dynamic prediction sequence to prefetch
blocks and apply parallel multiway merge for extraction. Our experimental
results show that in the selected benchmarks the priority queue reaches 75% of
the full parallel I/O bandwidth of rotational disks and and 65% of SSDs, or the
speed of sorting in external memory when bounded by computation.Comment: extended version of SEA'15 conference pape
Collisionless reconnection: The sub-microscale mechanism of magnetic field line interaction
Magnetic field lines are quantum objects carrying one quantum
of magnetic flux and have finite radius . Here
we argue that they possess a very specific dynamical interaction. Parallel
field lines reject each other. When confined to a certain area they form
two-dimensional lattices of hexagonal structure. We estimate the filling factor
of such an area. Antiparallel field lines, on the other hand, attract each
other. We identify the physical mechanism as being due to the action of the
gauge potential field which we determine quantum mechanically for two parallel
and two antiparallel field lines. The distortion of the quantum electrodynamic
vacuum causes a cloud of virtual pairs. We calculate the virtual pair
production rate from quantum electrodynamics and estimate the virtual pair
cloud density, pair current and Lorentz force density acting on the field lines
via the pair cloud. These properties of field line dynamics become important in
collisionless reconnection, consistently explaining why and how reconnection
can spontaneously set on in the field-free centre of a current sheet below the
electron-inertial scale.Comment: 13 journal pages, 6 figures, submitted to Ann. Geophy
An Efficient Multiway Mergesort for GPU Architectures
Sorting is a primitive operation that is a building block for countless
algorithms. As such, it is important to design sorting algorithms that approach
peak performance on a range of hardware architectures. Graphics Processing
Units (GPUs) are particularly attractive architectures as they provides massive
parallelism and computing power. However, the intricacies of their compute and
memory hierarchies make designing GPU-efficient algorithms challenging. In this
work we present GPU Multiway Mergesort (MMS), a new GPU-efficient multiway
mergesort algorithm. MMS employs a new partitioning technique that exposes the
parallelism needed by modern GPU architectures. To the best of our knowledge,
MMS is the first sorting algorithm for the GPU that is asymptotically optimal
in terms of global memory accesses and that is completely free of shared memory
bank conflicts.
We realize an initial implementation of MMS, evaluate its performance on
three modern GPU architectures, and compare it to competitive implementations
available in state-of-the-art GPU libraries. Despite these implementations
being highly optimized, MMS compares favorably, achieving performance
improvements for most random inputs. Furthermore, unlike MMS, state-of-the-art
algorithms are susceptible to bank conflicts. We find that for certain inputs
that cause these algorithms to incur large numbers of bank conflicts, MMS can
achieve up to a 37.6% speedup over its fastest competitor. Overall, even though
its current implementation is not fully optimized, due to its efficient use of
the memory hierarchy, MMS outperforms the fastest comparison-based sorting
implementations available to date
GPU LSM: A Dynamic Dictionary Data Structure for the GPU
We develop a dynamic dictionary data structure for the GPU, supporting fast
insertions and deletions, based on the Log Structured Merge tree (LSM). Our
implementation on an NVIDIA K40c GPU has an average update (insertion or
deletion) rate of 225 M elements/s, 13.5x faster than merging items into a
sorted array. The GPU LSM supports the retrieval operations of lookup, count,
and range query operations with an average rate of 75 M, 32 M and 23 M
queries/s respectively. The trade-off for the dynamic updates is that the
sorted array is almost twice as fast on retrievals. We believe that our GPU LSM
is the first dynamic general-purpose dictionary data structure for the GPU.Comment: 11 pages, accepted to appear on the Proceedings of IEEE International
Parallel and Distributed Processing Symposium (IPDPS'18
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