55,750 research outputs found
Gunrock: A High-Performance Graph Processing Library on the GPU
For large-scale graph analytics on the GPU, the irregularity of data access
and control flow, and the complexity of programming GPUs have been two
significant challenges for developing a programmable high-performance graph
library. "Gunrock", our graph-processing system designed specifically for the
GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on
operations on a vertex or edge frontier. Gunrock achieves a balance between
performance and expressiveness by coupling high performance GPU computing
primitives and optimization strategies with a high-level programming model that
allows programmers to quickly develop new graph primitives with small code size
and minimal GPU programming knowledge. We evaluate Gunrock on five key graph
primitives and show that Gunrock has on average at least an order of magnitude
speedup over Boost and PowerGraph, comparable performance to the fastest GPU
hardwired primitives, and better performance than any other GPU high-level
graph library.Comment: 14 pages, accepted by PPoPP'16 (removed the text repetition in the
previous version v5
Gunrock: GPU Graph Analytics
For large-scale graph analytics on the GPU, the irregularity of data access
and control flow, and the complexity of programming GPUs, have presented two
significant challenges to developing a programmable high-performance graph
library. "Gunrock", our graph-processing system designed specifically for the
GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on
operations on a vertex or edge frontier. Gunrock achieves a balance between
performance and expressiveness by coupling high performance GPU computing
primitives and optimization strategies with a high-level programming model that
allows programmers to quickly develop new graph primitives with small code size
and minimal GPU programming knowledge. We characterize the performance of
various optimization strategies and evaluate Gunrock's overall performance on
different GPU architectures on a wide range of graph primitives that span from
traversal-based algorithms and ranking algorithms, to triangle counting and
bipartite-graph-based algorithms. The results show that on a single GPU,
Gunrock has on average at least an order of magnitude speedup over Boost and
PowerGraph, comparable performance to the fastest GPU hardwired primitives and
CPU shared-memory graph libraries such as Ligra and Galois, and better
performance than any other GPU high-level graph library.Comment: 52 pages, invited paper to ACM Transactions on Parallel Computing
(TOPC), an extended version of PPoPP'16 paper "Gunrock: A High-Performance
Graph Processing Library on the GPU
Architectural support for task dependence management with flexible software scheduling
The growing complexity of multi-core architectures has motivated a wide range of software mechanisms to improve the orchestration of parallel executions. Task parallelism has become a very attractive approach thanks to its programmability, portability and potential for optimizations. However, with the expected increase in core counts, finer-grained tasking will be required to exploit the available parallelism, which will increase the overheads introduced by the runtime system. This work presents Task Dependence Manager (TDM), a hardware/software co-designed mechanism to mitigate runtime system overheads. TDM introduces a hardware unit, denoted Dependence Management Unit (DMU), and minimal ISA extensions that allow the runtime system to offload costly dependence tracking operations to the DMU and to still perform task scheduling in software. With lower hardware cost, TDM outperforms hardware-based solutions and enhances the flexibility, adaptability and composability of the system. Results show that TDM improves performance by 12.3% and reduces EDP by 20.4% on average with respect to a software runtime system. Compared to a runtime system fully implemented in hardware, TDM achieves an average speedup of 4.2% with 7.3x less area requirements and significant EDP reductions. In addition, five different software schedulers are evaluated with TDM, illustrating its flexibility and performance gains.This work has been supported by the RoMoL ERC Advanced Grant (GA 321253), by the European HiPEAC Network of Excellence, by the Spanish Ministry of Science and
Innovation (contracts TIN2015-65316-P, TIN2016-76635-C2-2-R and TIN2016-81840-REDT), by the Generalitat de Catalunya (contracts 2014-SGR-1051 and 2014-SGR-1272), and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 671697 and No. 671610. M. Moretó has been partially supported by the Ministry of Economy and Competitiveness under Juan de la Cierva postdoctoral fellowship number JCI-2012-15047.Peer ReviewedPostprint (author's final draft
Empirical Evaluation of the Parallel Distribution Sweeping Framework on Multicore Architectures
In this paper, we perform an empirical evaluation of the Parallel External
Memory (PEM) model in the context of geometric problems. In particular, we
implement the parallel distribution sweeping framework of Ajwani, Sitchinava
and Zeh to solve batched 1-dimensional stabbing max problem. While modern
processors consist of sophisticated memory systems (multiple levels of caches,
set associativity, TLB, prefetching), we empirically show that algorithms
designed in simple models, that focus on minimizing the I/O transfers between
shared memory and single level cache, can lead to efficient software on current
multicore architectures. Our implementation exhibits significantly fewer
accesses to slow DRAM and, therefore, outperforms traditional approaches based
on plane sweep and two-way divide and conquer.Comment: Longer version of ESA'13 pape
ParMooN - a modernized program package based on mapped finite elements
{\sc ParMooN} is a program package for the numerical solution of elliptic and
parabolic partial differential equations. It inherits the distinct features of
its predecessor {\sc MooNMD} \cite{JM04}: strict decoupling of geometry and
finite element spaces, implementation of mapped finite elements as their
definition can be found in textbooks, and a geometric multigrid preconditioner
with the option to use different finite element spaces on different levels of
the multigrid hierarchy. After having presented some thoughts about in-house
research codes, this paper focuses on aspects of the parallelization for a
distributed memory environment, which is the main novelty of {\sc ParMooN}.
Numerical studies, performed on compute servers, assess the efficiency of the
parallelized geometric multigrid preconditioner in comparison with some
parallel solvers that are available in the library {\sc PETSc}. The results of
these studies give a first indication whether the cumbersome implementation of
the parallelized geometric multigrid method was worthwhile or not.Comment: partly supported by European Union (EU), Horizon 2020, Marie
Sk{\l}odowska-Curie Innovative Training Networks (ITN-EID), MIMESIS, grant
number 67571
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