20,874 research outputs found
A parallel priority queue with fast updates for GPU architectures
The high computational throughput of modern graphics processing units (GPUs)
make them the de-facto architecture for high-performance computing
applications. However, to achieve peak performance, GPUs require highly
parallel workloads, as well as memory access patterns that exhibit good
locality of reference. As a result, many state-of-the-art algorithms and data
structures designed for GPUs sacrifice work-optimality to achieve the necessary
parallelism. Furthermore, some abstract data types are avoided completely due
to there being no corresponding data structure that performs well on the GPU.
One such abstract data type is the priority queue. Many well-known algorithms
rely on priority queue operations as a building block. While various priority
queue structures have been developed that are parallel, cache-aware, or
cache-oblivious, none has been shown to be efficient on GPUs. In this paper, we
present the parBucketHeap, a parallel, cache-efficient data structure designed
for modern GPU architectures that supports standard priority queue operations,
as well as bulk update. We analyze the structure in several well-known
computational models and show that it provides both optimal parallelism and is
cache-efficient. We implement the parBucketHeap and, using it, we solve the
single-source shortest path (SSSP) problem. Experimental results indicate that,
for sufficiently large, dense graphs with high diameter, we out-perform current
state-of-the-art SSSP algorithms on the GPU by up to a factor of 5. Unlike
existing GPU SSSP algorithms, our approach is work-optimal and places
significantly less load on the GPU, reducing power consumption
Parallel Combining: Benefits of Explicit Synchronization
A parallel batched data structure is designed to process synchronized batches of operations on the data structure using a parallel program. In this paper, we propose parallel combining, a technique that implements a concurrent data structure from a parallel batched one. The idea is that we explicitly synchronize concurrent operations into batches: one of the processes becomes a combiner which collects concurrent requests and initiates a parallel batched algorithm involving the owners (clients) of the collected requests. Intuitively, the cost of synchronizing the concurrent calls can be compensated by running the parallel batched algorithm.
We validate the intuition via two applications. First, we use parallel combining to design a concurrent data structure optimized for read-dominated workloads, taking a dynamic graph data structure as an example. Second, we use a novel parallel batched priority queue to build a concurrent one. In both cases, we obtain performance gains with respect to the state-of-the-art algorithms
The Adaptive Priority Queue with Elimination and Combining
Priority queues are fundamental abstract data structures, often used to
manage limited resources in parallel programming. Several proposed parallel
priority queue implementations are based on skiplists, harnessing the potential
for parallelism of the add() operations. In addition, methods such as Flat
Combining have been proposed to reduce contention by batching together multiple
operations to be executed by a single thread. While this technique can decrease
lock-switching overhead and the number of pointer changes required by the
removeMin() operations in the priority queue, it can also create a sequential
bottleneck and limit parallelism, especially for non-conflicting add()
operations.
In this paper, we describe a novel priority queue design, harnessing the
scalability of parallel insertions in conjunction with the efficiency of
batched removals. Moreover, we present a new elimination algorithm suitable for
a priority queue, which further increases concurrency on balanced workloads
with similar numbers of add() and removeMin() operations. We implement and
evaluate our design using a variety of techniques including locking, atomic
operations, hardware transactional memory, as well as employing adaptive
heuristics given the workload.Comment: Accepted at DISC'14 - this is the full version with appendices,
including more algorithm
Lock-free Concurrent Data Structures
Concurrent data structures are the data sharing side of parallel programming.
Data structures give the means to the program to store data, but also provide
operations to the program to access and manipulate these data. These operations
are implemented through algorithms that have to be efficient. In the sequential
setting, data structures are crucially important for the performance of the
respective computation. In the parallel programming setting, their importance
becomes more crucial because of the increased use of data and resource sharing
for utilizing parallelism.
The first and main goal of this chapter is to provide a sufficient background
and intuition to help the interested reader to navigate in the complex research
area of lock-free data structures. The second goal is to offer the programmer
familiarity to the subject that will allow her to use truly concurrent methods.Comment: To appear in "Programming Multi-core and Many-core Computing
Systems", eds. S. Pllana and F. Xhafa, Wiley Series on Parallel and
Distributed Computin
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
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